iOS 13.4 Beta4 release: predicting the official version or coming at the end of the month

I believe that Apple fans both love and hate Apple’s iOS 13 system. What they love is that they can experience some new features, and they hate that iOS 13’s overturning continues to compete with Apple’s previous iOS 11 system.

At present, the latest official version of iOS 13.3.1 is also constantly complained by the majority of fruit fans. Everyone seems to be waiting for the arrival of the next official version, that is, what can the official version of iOS 13.4 come? Don’t worry, Mingmei Infinite is here to bring the latest news about Apple, iOS and iPhone to many fruit fans!

iOS 13.4 Beta4 release: predicting the official version or coming at the end of the month

It’s not early this morning, Apple has launched the beta versions of four systems: iOS 13.4, iPadOS 14.4, macOS Catalina 10.15.4, and tvOS 13.4. After a week, but because this version is a test version, so we need to download the description file to update it. What does Apple’s iOS 13.4 beta4 update this time? Next, Mingmei Infinite will lead you to understand it together!

After the iOS 13.4 Beta4 update, the version number becomes: 17E5249a. From the developer log and the size of the update package, this update is an ordinary routine update, so we should not expect any major changes.

This update is mainly to fix some bugs in the previous version. For example, the problem that some options in the previous settings would flash back has been fixed.

Specifically, included in this beta is a return to iCloud folder sharing, the option for developers to sell macOS and iOS apps as a one-time purchase, new Memoji emojis, an updated Mail toolbar, keyboard shortcuts for Photos, TV adjustments In-app Family Sharing, new CarPlay controls, and refreshed location access authorization prompts. The addition of the CarKey API allows the iPhone or Apple Watch to be used as an NFC key to lock, unlock or start the car.

One of the most significant feature changes is that developers can now sell macOS and iOS versions of the app in one go. Apple says the feature will be available in March 2020, which is when we expect the official release of iOS 13.4.

Overall, this iOS 13.4 Beta4 version is still a small update. The recent high-frequency update speed may also be related to the upcoming release of the iPhone 9, because it has been pointed out that the iPhone 9 will be equipped with iOS 13.4. Released with the official version.

Update suggestion:

As a senior and loyal old fruit fan, Mingmei Infinite must have upgraded and experienced it for the first time: this time iOS 13.4 Beta 4 seems to be more stable, and the battery life and performance have been slightly improved. If it has been upgraded to iOS 13 .4 beta version students can upgrade, if iOS 13.3, it is recommended to wait for the official version. Of course, the overall test is artificial and there are certain errors. Mingmei’s own personal test results are for reference only.

So, if you have anything else to say about the iOS 13.4 Beta4 beta version that Apple just released today, you may leave a message in the comment area and let Mingmei participate in the discussion together!

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Summary of Shenzhen’s “Seven Strategic Emerging” Industrial Support Policies

With its youth and creativity, Shenzhen has become China’s most transformative technology cradle city. Since the implementation of industrial revitalization in accordance with the “Thirteenth Five-Year Plan” outline, an industrial structure has been formed with the framework of “Four Pillars, Seven Great Wars, and Six Great Futures”, and a series of policies have been issued for this purpose. It is these preferential policies. The landing has laid a solid foundation for Shenzhen’s industrial rise.

The so-called “four pillars, seven strategic emerging, six future” industries refer to: finance, logistics, culture and related, high-tech and other four pillar industries; information technology, Internet, new materials, biology, new energy, energy saving Seven strategic emerging industries such as environmental protection and cultural creativity; six future industries including life and health, wearable devices, robots, intelligent equipment manufacturing, marine economy, and aerospace.

In the previous article, OFweek Electronic Engineering Network has sorted out the support policies adopted by Shenzhen in the four pillar industries for everyone. This article will continue to take stock of the preferential policies implemented by Shenzhen in the seven strategic emerging industries.

Review of the previous article: Summary of Shenzhen’s “Four Pillars” Industrial Support Policies

1. Improve the ability of independent innovation

Guide the whole society to increase support for basic research, and provide up to 3 million yuan of support for basic research discipline layout projects that promote Shenzhen’s original innovation breakthroughs in cutting-edge fields, as well as applied basic research projects that support industrial technology needs.

Encourage enterprises to strengthen cooperation with universities and scientific research institutions, focus on the bottlenecks and weak links in the development of strategic emerging industries in Shenzhen, organize and implement key technology research projects in key areas, and support up to 10 million yuan.

Support the undertaking of national, provincial (ministerial) science and technology plan (special) projects, participate in or initiate international major scientific plans, and provide up to 1:1 supporting support, and the total amount of superior funding and municipal supporting funds shall not exceed the total project investment.

Support the construction of state-level innovation carriers such as state key laboratories and national engineering research centers, with a maximum support of 30 million yuan. Support the construction of a national enterprise technology center with a maximum support of 15 million yuan.

Support the construction of provincial and ministerial state key laboratories, national and local joint engineering research centers, and the Shenzhen branch of national innovation carriers that meet national policies and the needs of our city and have begun substantive construction, with a maximum support of 20 million yuan.

Support the construction of municipal key laboratories, engineering research centers, public technology service platforms, enterprise technology centers and other municipal innovation carriers, with a maximum support of 10 million yuan. Among them, the construction of municipal innovation carriers is divided into two stages: formation and promotion, and each stage is supported by a maximum of 5 million yuan.

Support all kinds of innovative entities to carry out necessary scientific experiments, core technologies and key equipment pre-research around the construction needs of major scientific and technological infrastructure, industrial application basic platforms, etc., and rely on the facilities or platforms to carry out interdisciplinary and large-scale collaborative innovation research with a major leading role. , special funds to provide financial support.

2. Guide the development of high-end industries

Encourage enterprises to speed up the efficient transformation of processes, technologies and products, build pilot-scale bases and pilot-scale production lines, and support up to 10 million yuan.

Enterprises are encouraged to be market-oriented, promote technological innovation, product innovation, and business model innovation, and carry out demonstration and promotion of new products, new technologies, new formats, and new model applications, with a maximum support of 10 million yuan.

Support enterprises to implement industrialization projects of independent innovation achievements, adopt the method of “pre-project establishment and ex-post funding”, with a maximum support of 15 million yuan.

Support enterprises, universities, scientific research institutions, etc. to carry out industry-university-research cooperation, focusing on basic research, technology development and industrial application demonstration, and jointly organize the implementation of “innovation chain + industrial chain” integration projects, and support up to 15 million yuan per year. The period is generally not more than 3 years.

Support the undertaking of national and provincial (ministerial) industrial development plan (special) projects, with a maximum of 1:1 supporting support, with a maximum of no more than 15 million yuan, and the sum of the funding and the national and provincial (ministerial) funding does not exceed the total project investment. 40%.

For the key introduction of international high-end R&D enterprises, as well as major industrial projects that can fill the gap in the city’s industrial chain and have particularly outstanding economic and social benefits, after the approval of the municipal government, special funds will provide comprehensive support for R&D expenses, equipment purchase subsidies, etc.

3. Expanding Financial Support Means

Explore the establishment of industrialization pilot test funds, and increase precise support for pilot test links in key areas. The special funds provide key support to enterprises and projects invested by overseas M&A funds and pilot-scale funds.

Strengthen cooperation with equity investment institutions, and provide up to 30 million yuan of equity capital support for industrialization projects implemented by enterprises. Among them, the funding is divided into two parts: equity investment and direct funding, and each part of the funding is not more than 15 million yuan.

Support enterprises to implement major industrialization projects by means of bank loans and bond financing, and provide discount support according to 70% of the total loan or bond interest, and the maximum discount amount does not exceed 15 million yuan.

Guide banks to provide industrialized credit loan support for small and medium-sized enterprises, with a maximum loan amount of no more than 15 million yuan, risk compensation based on 50% of the loss of the loan principal, and discount support based on 50% of the loan interest.

Guide guarantee institutions to provide industrialized guaranteed loan support for small and medium-sized enterprises, with a maximum loan amount of no more than 15 million yuan, risk compensation based on 50% of the loss of the loan principal, discount support based on 50% of the loan interest, and 50% of the guarantee fee. Guaranteed subsidies.

Guide financial leasing institutions to provide industrialized equipment financial leasing support for enterprises, with a maximum amount of no more than 50 million yuan, risk compensation according to 40% of the principal loss, and discount support according to 50% of the financial leasing interest.

4. Improve industrial supporting services

Relying on national and provincial “mass entrepreneurship and innovation” demonstration bases and municipal innovation and entrepreneurship bases, support the construction of public service platforms for innovation and entrepreneurship such as resource sharing, inspection and testing, and support up to 10 million yuan.

Enterprises are encouraged to obtain up to 5 million yuan of ex post support in order to develop domestic and foreign markets and meet the listing requirements of different countries and regions for their technologies, products and services to obtain various market access registrations, certifications and licenses.

Guide industry associations, industry alliances, new think tanks and other public service agencies to provide services such as industry-university-research cooperation, technology and industrial innovation platform planning and construction, standard formulation, decision-making consultation, and industry exchanges, with a maximum support of 3 million yuan.

Support universities, enterprises, industry associations and other institutions to host or undertake well-known domestic and foreign exhibitions, high-end forums and other activities in the field of strategic emerging industries, and provide up to 3 million yuan in post-event support.

Support all districts (new districts) to implement major engineering package projects such as application demonstrations, regional agglomeration pilots, manufacturing innovation centers, industrial innovation centers, and industrial infrastructure clusters in key industrial fields, with special funds to support them.

V. Innovate the management system and mechanism

Actively explore the project bidding reward system, adopt the project manager management system and the “milestone” assessment mechanism, and face the global bidding reward task undertaking team, focusing on solving bottleneck problems such as cutting-edge technology engineering and key component development.

Carry out the pilot project of a third-party professional institution for project management, and the competent industry department can entrust a third-party professional institution to carry out project management work such as project review and approval, process management, acceptance evaluation, etc., to provide business guidance and supervision to the third-party professional institution, and to provide relevant conditions and guarantees.

Implement the system of joint punishment for dishonesty. If the project unit and its legal representatives, project leaders, and directly responsible persons, etc., have dishonest behavior and are listed as the object of joint punishment for violations and dishonesty by relevant departments, they will be restricted from applying for special fund support in accordance with the law. Fully implement contract management, and establish a credit score management system for project units and their responsible subjects.

Note: For projects funded by the municipal special funds for strategic emerging industries, the amount of the district-level special funds shall not exceed 50% of the municipal-level special funds in principle. The total shall not exceed 70% of the actual interest paid by the project.

In the follow-up, OFweek electronic Engineering Network will also summarize the support policies of Shenzhen’s “six future” industries for readers, and welcome everyone to pay attention.

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China Telecom will work with industry partners to jointly promote the development of AI networks

In the 5G era, more and more problems cannot be solved by traditional manual methods. Scenarios such as intelligent edge computing, intelligent network slicing, intelligent network configuration, and green energy saving rely more on artificial intelligence (AI) to exert their value, which drives the industry to widely adopt AI technology. In order to realize network intelligent operation, Chen Yunqing, president of China Telecom Beijing Research Institute, said today.

He pointed out at the Huawei Autonomous Driving Network Press Conference that with the application of new technologies such as 5G and NFV/SDN in the network, while creating new business formats for operators, it also further increases the complexity of the network and increases the management of the network. and maintenance challenges. Through network automation, digital twins, AI and other means, simplify network and O&M, help operators improve O&M efficiency, resource utilization, customer experience, and energy efficiency, and promote network evolution towards full autonomy. become the focus of the industry.

“Operators have a good IT environment and a large amount of network data, which has become a natural advantage for the development of network AI. AI technology brings prediction and reasoning capabilities to the network. It plays a role in solving and reducing repetitive manual tasks, helping the network to reduce costs and increase efficiency, value mining and experience improvement in all aspects of the network,” he said.

According to reports, China Telecom released a white paper on artificial intelligence development in June this year. In the white paper, it clarified China Telecom’s future artificial intelligence development strategy. China Telecom will position itself as an “AI network builder, AI industry driver, AI technology application.” AI service provider and AI service provider”, with the direction of strategic transformation 3.0 and CTNet2025 as the foundation, through the comprehensive introduction and development of artificial intelligence technology, it provides AI general capability platforms, applications and solutions both internally and externally, accelerates the upgrade of intelligent networks, and forms Intelligent industrial ecology, improve the level of intelligent operation, build a comprehensive and intelligent “Wish Network”, and provide user-centric services. China Telecom has covered these contents in the CTNet2025 network architecture reconstruction strategy. The entire strategy will focus on the construction of AI empowerment platform, network infrastructure, testing and evaluation system, and compound talent team.

At the same time, China Telecom has carried out a series of explorations in AI applications, especially in the field of 5G+AI. It officially established the 5G Industry Innovation Alliance in September this year. The 5G+AI working group explores business innovation and service innovation based on 5G network and AI technology, promotes the standardization and Display of innovation achievements, and promotes the development of intelligent industry applications. It has jointly developed network operation and maintenance solutions based on artificial intelligence technology with Huawei and other partners. Relying on the research and development results, it has led the promotion of network AI standardization in international and domestic standard organizations such as ITU, ETSIENI, 3GPP, and CCSA, and guided network AI technology and standards. and industrial development.

“The development of network artificial intelligence is a long-term evolution task that is gradually accumulated from point to surface under the guidance of a unified framework. China Telecom is willing to join hands with partners from upstream and downstream in the industry chain to jointly promote the formulation of artificial intelligence standards for telecom networks and promote the artificial intelligence industry. To accelerate the upgrading of intelligent networks, improve the level of intelligent operation, expand comprehensive intelligent information services, improve people’s livelihood, help the country become a strong network, and inject a strong source of development for the comprehensive digital transformation of the economy and society.” Chen Yunqing said at the end of his speech. said.
Responsible editor; zl

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2020 Cybersecurity Investment and Financing Report: Perspective on Investment Hotspots and Trends

foreword

There is no doubt that the epidemic is one of the biggest keywords in 2020. Affected by the epidemic, the global economic situation is not optimistic, and the network security industry has also been affected to a certain extent, and the growth rate has slowed down. Gartner data shows that the global network security industry market size in 2019 reached 124.401 billion US dollars, a year-on-year increase of 9.11%, and the market size in 2020 is expected to be around 127.827 billion US dollars, the growth rate is much smaller than that in 2019, almost the same.

Against such a background, the capital operation activities of the cybersecurity industry have not been affected too much. The emergence of new scenarios such as telecommuting, data compliance, privacy protection, and the Internet of Everything has brought freshness to cybersecurity entrepreneurship and the capital market. vitality. Judging from the performance of the investment and financing market, many investment institutions, including Insight Partners, Sequoia Capital, Accel, Bessemer Venture Partners, Sequoia, Bain Capital, and domestic Qi’an Venture Capital, all have large-scale capital operations.

In the secondary market, according to public statistics, as of December 31, 2019, the total market value of 106 listed companies (hereinafter referred to as “overall market value”) reached 3,875.587 billion yuan, a year-on-year increase of 29.12%. As of December 31, 2020, the overall market value reached 4,710.161 billion yuan, a year-on-year increase of 21.53%.

  

Figure 1 Changes in the market value of listed cybersecurity companies

01 The overall situation of investment and financing in the global network security field in 2020

In fact, since 2016, there have been more than 15 investment institutions that have invested in more than 10 cybersecurity companies globally. In 2020 alone, there have been at least 232 incidents with relatively large amounts of financing and M&A in the cybersecurity investment and financing market (there are unavoidable omissions in the process of this article, please forgive me), including 77 domestic cases, accounting for 33%, and 153 foreign cases, accounting for 33% of the total. than 67%.

In terms of capital scale, the total amount of investment, financing and mergers and acquisitions in the global cybersecurity industry is about 24.4861 billion US dollars (some financing and mergers and acquisitions do not disclose financial details, so only the public financial information is counted, the same below), of which the domestic amount is about 2.2912 billion US dollars, and about 22.1949 billion US dollars abroad (1 US dollar ≈ 6.5 yuan at the exchange rate of USD and RMB), accounting for 9% and 91% respectively. The proportional distribution diagram is as follows:

  

Figure 2 Distribution of the number of domestic and foreign investment and financing events, collation of public information

Figure 3 Distribution of domestic and foreign investment and financing quotas, collation of public information

At the same time, according to the type of investment and financing, the events included in this article are divided, there are 3 listing events, 31 M&A events, and 208 financing events (as shown in the figure below). Among them, it is worth noting that “the first brother of cybersecurity” Qi Anxin will land on China’s Shanghai Stock Exchange Science and Technology Innovation Board in 2020, and its market value once exceeded the 90 billion yuan mark; and the internationally renowned anti-virus manufacturer McAfee is also listed in the United States. The stock exchange listed again, raising $750 million; Veeam, a data storage and disaster recovery company, was acquired by equity and private equity firm Insight Partners for $5 billion, setting the highest amount of acquisitions in the cybersecurity field in 2020.

Figure 4 Distribution of investment and financing types, collation of public information

Further from the perspective of subdivisions, the more popular ones mainly include cloud security, application security, identity security, detection and response, data security, security services, industrial security and business security, etc. The number and quota distribution of events are shown in the following figure:

Figure 5 Distribution of investment and financing events in sub-sectors, collation of public information

Figure 6 Distribution of investment and financing quotas in sub-sectors, collation of public information

  02 The overall characteristics of investment and financing in the global cybersecurity field in 2020

First, the new scenarios spawned by the epidemic have led to a new wave of cybersecurity venture capital investment. The surge in demand for telecommuting is one of the biggest changes brought by the epidemic to enterprises. Therefore, security protection companies targeting normal telecommuting or remote access scenarios have attracted the attention of the capital circle.

As we all know, telecommuting will inevitably involve a large number of self-owned devices. How to confirm whether the devices, people and corresponding operations connected to the enterprise network are safe has become the most concerned topic. This is mainly reflected in the field of identity management and access control. According to the number of financing events in the field of identity security included in this article, 2020 has increased by nearly 100% compared to the previous year. Among the enterprises that have obtained financing, the enterprises that do identity authentication and management occupy the vast majority. Among them, multi-factor authentication and identity authentication based on user entity behavior analysis have become mainstream.

It is worth noting that the “zero trust” architecture system has penetrated into all aspects of network security management, and has begun to enter the stage of standardized and implemented development. Following the introduction of the “Zero Trust Architecture” standard by NIST, the national zero trust standard led by Qi Anxin was officially launched, and relatively subdivided and implementable solutions have appeared in 2020. This is a big improvement compared to 2019.

Second, the pace of mergers and acquisitions of leading IT giants has gradually slowed down. Looking back on the past two years, IT giants have been very active in their capital activities, especially in the two fields of cloud security and terminal security. Microsoft, VMware, Broadcom, and domestic Alibaba and other companies have all launched large-scale mergers and acquisitions, and PaloAlto, Fortinet and other established security vendors are not far behind, and Symantec’s enterprise security business has sold for a sky-high price of 10.7 billion US dollars.

However, in 2020, the industry giants seem to have become a lot “quieter”, perhaps due to the impact of the economic downturn, they have to clench their fists and focus more on their main business. So, what is going on with the multi-billion-dollar M&A events in the world in 2020? In fact, this is basically from the handwriting of equity and private equity companies, and does not involve too many products or business integration.

Looking at China, Alibaba, Tencent and other leading Internet companies have not done much. However, 360 has made quite a few capital moves, including the wholly-owned acquisition of Hansi, the increase in holdings of Hillstone Networks, etc. In the dilemma of the shrinking 2C business, 360 tried to penetrate deeper into the customer site through mergers and acquisitions and integrate its core security. Brain ability and safe operation are connected.

Third, security compliance makes data security and personal privacy protection a favorite. This stems from the fact that the epidemic has also raised public concerns about the protection of personal privacy. On the one hand, during the epidemic, there were cybercriminal gangs who wanted to “fish in troubled waters” to make illegal profits; on the other hand, new technologies were used to fight the epidemic, and a large amount of personal information was collected and used, resulting in a sudden increase in personal privacy risks, and personal privacy leaks continued. The incident was exposed.

At the same time, with the official implementation of the CCPA and the introduction of domestic draft regulations such as the Data Security Law and the Personal Information Protection Law, security compliance will greatly increase the needs of government and enterprise organizations for data security and personal privacy protection. Private equity firm Insight Partners spent $5 billion to acquire Veeam, a data storage and backup company. Obviously, this field has attracted great attention from major capitals.

Fourth, the upsurge in the field of detection and response will continue. Whether it is 2019 or 2020, it can be seen from the number of capital transaction incidents that detection and response are regarded as the key strategies of enterprises. This is mainly due to the frequent occurrence of security incidents in the past few years, and the management of investment institutions, enterprises and various organizations. All layers recognize that security has no silver bullet thinking. They want to look for some endpoint-based, network-based or user-based approach to continuous advanced threat detection, investigation and rapid response capabilities. From this point of view, for some time to come, detection and response will still be a hot spot for investment institutions.

Fifth, Chinese cybersecurity companies may enter the era of 100 billion market value. In 2020, another major event occurred in the network security industry. Qi Anxin officially landed on the Science and Technology Innovation Board. In the past, the market value of leading cybersecurity companies was around 20 to 30 billion yuan. Although Sangfor’s market value exceeded 100 billion yuan, its cybersecurity revenue only accounted for about 60% of its total revenue. On the second trading day after Qi Anxin was listed on the Science and Technology Innovation Board, the market value once exceeded 90 billion yuan, and recently broke the 90 billion mark again. It is easy to foresee that with the rapid increase in the size of the network security market and the capital market’s optimism about the industry situation, it is a high probability event that Qi Anxin’s market value exceeds 100 billion yuan.

 03 Interpretation of global cybersecurity investment and financing segments in 2020

Cloud security: Cloud native sparks a boom in native security investment

Let’s look at a set of data first. In 2020, this article includes a total of 20 investment and financing events in the cloud security field, including 18 foreign and 2 domestic events. The financing amount was US$1,053.45 million. The details are shown in the following table:

Table 1: Some financing and M&A companies in the cloud security field

The above data actually shows that after a long period of development, the security market on the cloud has become quite “crowded”. On the one hand, in the past ten years, a large number of cloud security startups have been emerging. On the other hand, traditional security companies are also transforming in the direction of the cloud. Alibaba Cloud and Tencent Cloud are also relying on the advantages of cloud infrastructure to expand their own The map in the field of cloud security. From this perspective, it is very difficult for start-up security companies to take root in the field of cloud security and gain recognition from the capital market, especially in terms of infrastructure security protection on the relatively mature cloud.

But in 2020, cloud-native applications are completely on fire, which gives enterprises engaged in cloud computing a lot of opportunities. Correspondingly, the security protection on the cloud also shifts towards cloud-native applications. From a technical point of view, enterprises that implement cloud virtual hosts, container hosts and other related workloads and cloud native application security protection with technologies such as visualization, micro-isolation, and CWPP have won the favor of more capital, which is also in line with cloud computing. , The development trend of cloud security technology. my country’s host security star company Qingteng Cloud Security also said after completing the financing that it will fully support the research and development of container security and other related fields.

At the same time, with the rise of cloud-native applications, government and enterprise organizations have significantly increased the demand for cloud-native flexibility, high elasticity and high scalability, which makes security vendors must be able to provide a cross-application, platform, multi-cloud, hybrid cloud safety management capabilities. JupiterOne, for example, claims to centralize security controls by integrating with dozens of services and tools, including Amazon Web Services, Cloudflare, and GitLab.

Endpoint Security: The Market Is Stabilizing

In 2020, there were 9 investment and financing incidents in the terminal security field, including 1 domestic incident and 8 foreign incidents, with a total financing amount of 1.81 billion US dollars.

Table 2: Financing and M&A in the field of terminal security

Looking back at the concept hype and the crazy blessing of capital in the past few years, endpoint security has gone through a pretty crazy period, and endpoint-based detection technology (EDR) has shined brightly, giving birth to several such as CrowdStrike, Carbon Black (later acquired by VMware), etc. The star terminal security enterprise has formed three technical modules of terminal management and control (including patch distribution, terminal access, asset verification, etc.), anti-virus and EDR.

Unlike previous years, there will be no multi-billion dollar acquisitions or financing events in 2020. What attracts attention is only the secondary listing of the old antivirus software company McAfee on Nasdaq. On the one hand, mainly due to several years of mergers and acquisitions, the layout of major industry giants in the field of terminal security has been basically formed; on the other hand, the technological development of terminal security (concept speculation) has entered a “bottleneck period”. It can only detect terminal behavior, and cannot restore the entire attack, and has a certain false positive rate.

Therefore, through technological innovation, and at the same time linking NDR and threat intelligence capabilities to reduce false positives in EDR, it can still attract the attention of capital. For example, Deep Instinct and Dtex, two companies will improve detection capabilities and reduce false positives as their own. core competitiveness.

It is worth noting that SentinelOn, which has received financing twice a year, with a financing amount of more than 400 million US dollars, and Tanium, which has received financing of 150 million US dollars, have quite strong unified terminal security management and control capabilities, which can be used in a load IT environment. , including physical terminals (including IoT devices) and cloud workloads (virtual hosts, container hosts) into the unified protection category. This shows that the unified management and control of terminals still has obvious market space.

Detection and response: Deep integration of detection and response, further security operations

In the field of detection and response, this paper includes a total of 35 investment and financing events, 10 domestic and 25 foreign, with a financing and M&A amount of US$1.2055 billion.

Table 3: Financing mergers and acquisitions in the field of cybersecurity detection and response

From 2015 to the present, detection and response has been one of the most active segments in cybersecurity, favored by the capital market, and the market continues to expand. According to the survey data of the third-party consulting agency Sushi Consulting, the domestic threat detection and response (TDR) scale in 2019 is about 1.9 billion yuan, and it is expected to reach about 2.6 billion yuan in 2020, and it is expected to grow to 3.9 billion yuan in 2021. about.

With the continuous evolution of attack technology, the corresponding detection and response technology will also keep pace with the times. It is foreseeable that in the next few years, this subdivision will still be the main battlefield of technological innovation. Several characteristics can be seen from the companies that received financing in 2020.

One is that there is no breakthrough innovation in detection technology itself, but the expansion of collected data sources makes the product more grounded. For example, the domestic start-up company Fule Technology has realized the detection and analysis of all traffic; the foreign company Abnormal uniquely analyzes various data sets about people, interpersonal relationships and business environment through the API interface to block social engineering attacks .

The second is that cloud-based detection models are becoming mainstream. The huge computing power advantage brought by cloud computing provides more space for massive data analysis, which is why more and more manufacturers choose to provide customers with products in the SaaS model. This is particularly evident in the SIEM product. Manufacturers such as Devo and Panther Labs can provide customers with cloud-based SIEM products for processing massive logs and security events.

The third is the deep integration of detection and response technologies. Detection and response should be integrated, but in recent years, people have attached great importance to detection technology, and the development of response is relatively slow. However, a good phenomenon has emerged in 2020. Detection and response technologies are being deeply integrated, which will drive security operations to a new level. The main manifestation is that the threat intelligence providers represented by Weibu Online are integrating their superior capabilities and moving towards a comprehensive detection and response service provider; SOAR technology is highly valued by capital, and it can be linked with SOC and TDR equipment to promote Incident response is developing from manual transmission to automatic transmission; third, some manufacturers are integrating threat detection teams with security service teams, such as FireEye’s acquisition of XDR provider Respond, deep integration with its incident response team Mandiant, and domestic security services. The company Qi Anxin has also integrated its own threat detection team Tianyan and Anfu team.

Identity Security: The demand for telecommuting is soaring, and identity security is more contextualized

In the field of identity security, this article includes a total of 38 investment and financing events, including 12 domestic and 26 foreign, with a financing amount of US$1.6955 billion.

Table 4: Financing and M&A in the field of identity security

In 2020, zero trust is no longer a new word, but people’s attention to it has not declined, but has continued to rise due to the surge in the demand for remote work. It is worth noting that there have been many VPN-based supply chain APT attacks in China, which has increased people’s voice for zero-trust networks. Gartner believes that by 2023, 60% of VPNs will be replaced by zero trust. Because of this, a large number of startups that implement identity authentication and access control with zero trust ideas have emerged this year, and have received early investment from venture capital institutions (mainly in the B round and before).

From a technical point of view, companies that harvest financing pay more attention to the problem-solving capabilities of their products and solutions, and are therefore more contextualized. For example, Edgewise pays more attention to the identity security of software and API, while Preempt mainly conducts repeated analysis and verification of traffic.

At the same time, the integration of zero-trust thinking and Secure Access Service Edge (SASE) devices has also attracted great attention from investment institutions. For example, Perimeter 81, a SASE provider whose user-centric secure network-as-a-service enables enterprises to use cloud services to protect on-premises network resources simplifies cybersecurity for remote and mobile workers; Palo Alto’s $420 million acquisition of CloudGenix, Used to enhance capabilities in SD-WAN. This is in line with Gartner’s view that security and risk management leaders should pilot Zero Trust programs as part of a SASE strategy, or rapidly expand remote access.

In addition to startups, some leading security companies have also launched zero-trust solutions that can be implemented on the ground. For example, Qi Anxin has launched a zero-trust remote access solution. It is believed that with the implementation of the foreign NIST “Zero Trust Architecture” standard and the “Information Security Technology Zero Trust Reference Architecture” led by domestic Qi Anxin, the development of zero trust will enter a new situation.

Data security: Privacy compliance ushered in a big explosion

In the field of data security, this article includes a total of 35 investment and financing events, including 15 domestic and 20 foreign, with a total financing amount of 6.9685 billion US dollars.

Table 5: Financing mergers and acquisitions in the field of data security

In 2019, technological breakthroughs are no longer the main theme of data security protection, replaced by risk compliance. The influence of GDPR, CCPA and other laws continues to expand, and the compliance drive has ushered in a big explosion in 2020. Almost all companies that have received financing are claiming that they can help organizations meet the requirements of various regulations. In China, the excessive collection of personal privacy by apps has also become a prominent problem. The Ministry of Industry and Information Technology has also removed a large number of apps and ordered rectification during the Internet clean-up campaign over the years. However, in the field of app privacy compliance, there are no star start-ups in China, mainly leading security companies relying on advantageous resources to provide app privacy testing services for government and enterprise organizations, such as the app privacy compliance testing launched by Qi Anxin during the epidemic Serve.

One thing to note is that some data compliance solution providers targeting vertical industries have begun to emerge in 2020. For example, the VGS invested by Visa focuses on the compliance of financial data, while the domestic Yifangjianshu is more focused on the compliance of medical data. These all indicate that the data security and compliance market is maturing, because the industry attributes of sensitive data are very distinct, and companies that are more vertical and better understand business are needed to do more professional things.

There is another area worthy of attention-disaster recovery backup. In this segment, the largest single acquisition in 2020 was also born: the $5 billion acquisition of Veeam by equity and private equity firm Insight Partners. The importance of disaster recovery backup to ensure business continuity is self-evident. It can help enterprises prevent two types of risks: the first is internal malicious deletion of data events, such as service downtime caused by the deletion of the Weimob database; the second is external Attacks result in data loss or encryption, mainly ransomware attacks. Ransomware has been active for many years, and everyone hates it. According to data, starting in the second quarter of 2019, the number of ransomware attacks targeting businesses exceeded that of consumers for the first time. Obviously, the black production group is targeting enterprise-level terminals and servers with more attack value.

Therefore, in order to deal with rampant ransomware attacks, government and enterprise institutions must, in addition to deploying corresponding intrusion detection and anti-virus means, must also provide strict disaster recovery and backup services (such as three centers in two places), so as to ensure that in the event of an attack Maintain business and data continuity while avoiding extortion. For professional disaster recovery companies, if they want to gain recognition from customers and the capital market, and compete with leading technology giants such as Alibaba Cloud, Tencent Cloud and Huawei, they must have obvious advantages in disaster recovery and recovery technology.

Security services: Start-up companies are blooming, and the domestic market is poised to take off

In the field of security services, this article includes a total of 27 investment and financing events, including 10 domestic and 17 foreign, with a financing and M&A amount of US$1.787 billion.

Table 6: Financing and M&A in the Security Services Field

Affected by the epidemic, a large number of security service engineers cannot be on-site to provide support for customers, so the global security service market is not booming. Especially in China, IDC data shows that in the first half of 2020, the overall revenue of China’s IT security service market vendors was about US$572 million (about 4.023 billion yuan), and the revenue of vendors decreased by 27.7% compared with the same period in 2019.

In fact, due to the lack of capital budget and sufficient network security professional support, most customers hope that network security vendors can provide solutions to all security problems for themselves without too much intervention by themselves. Perhaps it is because of the impact of the epidemic that the market is more aware of the importance of security services, and the capital market has also noticed this. Therefore, the investment and financing enthusiasm in the field of security services has not declined because of this, but has shown a trend of blooming flowers. The companies that have obtained financing cover various fields such as penetration testing, vulnerability management, MDR, consulting, education and training, and insurance. Among them, cloud-based full-lifecycle managed detection and response has attracted more attention because it gets rid of the dependence on on-site security engineers.

Unlike the global market, the domestic network security market is still dominated by the sales of software and hardware. IDC data shows that the domestic security service market accounts for only 20.5%. But what can be seen is that domestic start-up companies are also eager to try. Douxiang Technology, which takes vulnerability public testing as its core business, has received financing for two consecutive years, and Yuanbao Technology, which provides security testing services for the insurance industry, has completed two rounds of financing within one year. .

There are two other businesses worthy of attention. One is the cybersecurity range. Due to the great promotion of actual offensive and defensive exercises for the construction of network security in recent years, the network shooting range has attracted more and more attention from government and enterprise customers. The domestic start-up companies Yongxin Zhicheng and Saining Network Security have made great achievements in this field, enabling the network to Safety competitions and offensive and defensive exercises are developing towards normalization. In addition, the use of shooting ranges for safety awareness and skills education and training has also been recognized by the capital and the market.

Another is the security consulting planning business. Although there are already very mature network security consulting service providers abroad, such as Deloitte, KPMG, etc., the domestic market is still slightly immature. However, it can be seen that in the actual offensive and defensive exercises over the years, due to the lack of systematic network security construction, the defenders are often “riddled with holes”, which further reflects the importance of consulting planning. Therefore, Northern Labs, a provider of complete network security consulting services for enterprises, has been recognized by the capital market.

It is worth noting that, according to reports, based on the concept of endogenous security, Qi Anxin officially launched the endogenous security framework in 2020. The framework starts from a top-level perspective and uses the methodology of systems engineering to deconstruct the “Ten Major Projects and Five Tasks” for network security planning in the digital age, which can guide different industries to output network security architectures that meet their characteristics and build a dynamic and comprehensive network security defense system.

Industrial Safety: Capital Markets Need New Vitality

In the field of industrial security, this article includes a total of 9 investment and financing events, including 7 domestic and 2 foreign, with a total amount of financing and mergers and acquisitions of 214 million US dollars.

Table 7: Financing mergers and acquisitions in the field of industrial security

In 2020, affected by the acceleration of new infrastructure, the Industrial Internet will undoubtedly be one of the hottest topics. Industrial enterprises are all making layouts and equipping the development of industry and manufacturing with new engines to create “new industries”. However, because the industrial network has been in a closed state for a long time and lacks the ability to resist virus intrusion and corresponding security products, industrial security problems have become more prominent in recent years.

In the face of huge risks, industrial safety is naturally the focus of capital. However, subject to the complexity of industrial security itself, it is difficult for startups to break out into the world. Judging from the companies that received financing in 2020, it is still the companies that received financing in 2019. (either in quantity or amount)

From the product level, our products are not very differentiated at present, mainly focusing on industrial-grade firewalls, gatekeepers, anti-virus and other gateway products. Only a few leading companies such as Qi Anxin and Venus have industrial advanced network monitoring and situational awareness products. At present, industrial safety is still at a fairly early stage. If you want to gain access to a large amount of capital, you must be able to tell a good “story” and inject new vitality into the capital market.

Business security: Risk control and anti-fraud are always on the way, and business security is heading for deep water

In the field of business security, this article includes a total of 9 investment and financing incidents, including 5 domestic and 4 foreign. The amount of financing mergers and acquisitions was US$372.3 million.

Table 8: Financing mergers and acquisitions in the field of business security

With the vigorous development of the mobile Internet and e-commerce, online consumption, transactions, and value-added services have gradually become popular, and online frauds such as cheating, account counterfeiting, billing, and malicious crawling have also emerged wildly. Fraud gangs exploit platform or business loopholes and cause different losses to platforms and users. Data shows that more than $50 billion is lost globally to online fraud every year. Moreover, this situation is more common in the country.

From the perspective of financing companies, most companies claim to use artificial intelligence and machine learning methods to establish risk control and anti-fraud models, identify user identities, and reduce business security risks. Interestingly, in 2020, some companies have begun to focus on vertical industries to provide risk control solutions. For example, Zhanlue Data focuses on the field of health insurance, and Forter focuses on payment card fraud detection.

If only in terms of financing, the pace of domestic anti-fraud companies is slightly ahead of the world. This is mainly due to the rapid development of Internet and mobile Internet services such as Internet finance, e-commerce, and short video in China. Risk control and anti-fraud have become the rigid needs of a large number of online companies. And with the popularity of 5G in China, it is believed that more and newer online services will be born quickly, and risk control and anti-fraud will always be on the way.

Application Security: Cloud Native Drives DevSecOps to Absolute Mainstream

In the field of application security, this article includes 19 investment and financing events, including 2 domestic and 17 foreign. The amount of financing mergers and acquisitions was US$2.3672 billion.

Table 9: Financing mergers and acquisitions in the field of application security

Affected by the popularity of cloud native, a large number of cloud native applications have been continuously developed. At the same time, native security is similar in essence to the endogenous security concept proposed by Qi Anxin, which requires the integration of network security and informatization, and focuses on security design from the development stage. DevSecOps is in line with such a concept. It emphasizes that when the DevOps plan is just launched, it is necessary to invite the security team to ensure the security of information, and develop an automatic security protection plan to achieve continuous IT protection, so it has been warmly welcomed by investment institutions.

From the perspective of technological development, whether it is for binary code vulnerability detection for developers, or for API and application security protection in operation and maintenance, the application of automation, artificial intelligence and other technologies will undoubtedly bring more value to customers. For example, Signal Sciences provides a platform that allows customers to automate testing of dynamic application security protections; DeepCode uses AI to provide customers with automated binary vulnerability detection and remediation capabilities.

IoT security: The commercialization of 5G promotes the accelerated implementation of IoT security

In the field of application security, this article includes 13 investment and financing events, including 6 domestic and 7 foreign. The amount of financing mergers and acquisitions was US$2.7413 billion.

Table 10: Financing and mergers and acquisitions in the field of IoT security

Compared with 2019, the investment and financing events in the Internet of Things field in 2020 included in this article have increased significantly in both the total amount and the total number of financing. It should be noted that with the popularization of 5G and IPv6, IoT devices will grow exponentially. At the same time, whether it is an attack on the IoT device itself or a DDoS attack using a botnet composed of IoT devices, it will cause immeasurable losses to production and life.

Obviously, IoT security has broad market prospects, which is one of the most important reasons for investment institutions to be optimistic about this segment. From a technical point of view, most enterprises position their core capabilities in the management and control of IoT devices, including comprehensive management of vulnerabilities, status, and configuration.

For example, Sepio provides a service to detect and mitigate any malicious device that has been connected to an enterprise infrastructure; Armis is able to monitor millions of devices around the world, providing visibility into enterprise devices, identifying risks and threats.

other

Compared with the above popular tracks, mobile security, Electronic forensics and other sub-sectors are relatively cold to avoid risks, and most of them are early-stage financing of start-ups, and giants’ “checking and filling vacancies”.

In addition, there is another type of enterprise that does not belong to a single subdivision. Their product lines are relatively comprehensive, so they are classified as comprehensive security in this article. Among them, Qi Anxin, the leading network security enterprise, has been listed, which has attracted great attention in China.

The Links:   2MBI600VE-120 LQ070Y3DG05

China-ASEAN Digital Innovation Competition (Artificial Intelligence Track) Finals Concluded

On November 5, the finals of Bailian Chenggang·China-ASEAN Digital Innovation Competition (Artificial Intelligence Track) were successfully held in Nanning, Guangxi. The competition is jointly sponsored by Huawei Technologies Co., Ltd., Digital Guangxi Group Co., Ltd., and Guangxi Artificial Intelligence Society under the guidance of the Big Data Development Bureau of Guangxi Zhuang Autonomous Region, organized by China-ASEAN (Huawei) Artificial Intelligence Innovation Center, Guangxi Liuzhou Iron and Steel Group Co., Ltd., A national event co-organized by the Guangxi Zhuang Autonomous Region Information Center.

As one of the series of activities of the 1st Guangxi “Digital Silk Road” Summit and the 3rd China-ASEAN Artificial Intelligence Summit, the China-ASEAN Digital Innovation Competition (Artificial Intelligence Track) takes the opportunity to promote the development of Guangxi’s artificial intelligence industry. Starting from the typical scene of Guangxi iron and steel industry, the issuer uses artificial intelligence technology to help improve the efficiency of steel product research and development or finished product quality inspection. Further integrate scientific research and teaching achievements with the business innovation needs of the steel industry, and inject new momentum into the deep integration of artificial intelligence and the new economy.

Since the opening of the online registration platform for the competition on August 23, 2021, it has attracted 1,024 developers from 48 cities across the country, including Beijing, Xi’an, Hangzhou, Guangzhou, and Changsha to sign up for the competition. During the competition, the organizer has opened a training camp for the competition, etc. On-site and offline activities empower developers. 245 teams participated in the competition and received a total of 154 outstanding works. In the end, 12 teams advanced to the finals and joined forces in Nanning.

At the site of the finals, the 12 qualifying teams conducted on-site defenses one by one, and the expert judges conducted on-site questions and comments. Finally, the final winning team was judged based on the scores of the online works and the comprehensive scores of the offline defense scores.

In May this year, the Big Data Development Bureau of Guangxi Zhuang Autonomous Region issued the “Notice on Organizing the Declaration of the First Batch of Special Subsidies for China-ASEAN (Huawei) Artificial Intelligence Innovation Center in 2021”, planning to promote the integration of artificial intelligence technology and the autonomous region’s industries from various aspects Development – Support industrial intelligent transformation projects, guide traditional industries to pay attention to the value of data elements; incubate innovative demonstration applications, and encourage market entities to increase investment in research and development; assist in the cultivation of practical talents, and strengthen the professional teaching capabilities of colleges and universities.

In recent years, steel enterprises represented by Liugang Group have begun to focus on production process, quality control, process improvement, etc., taking digitization, informatization, and intelligence as the implementation path, to build smart steel plants with modern industrial control systems. In order to accelerate the digital transformation of manufacturing enterprises in Guangxi and realize intelligent production, the China-ASEAN Digital Innovation Competition (Artificial Intelligence Track), with the title of “Intelligent Analysis of Iron and Steel Metallography”, provides developers with HUAWEI CLOUD AI development platform, through artificial intelligence Technology solves the pain points of the steel industry and promotes the transformation and upgrading of the steel industry in Guangxi.

The Links:   FZ1600R17KF6C_B2 LM240WU5-SLA4

A control system of human-like arm-shaped prosthesis based on single chip microcomputer and CPLD

A prosthesis is a replacement for a missing limb in the human body to compensate for the shape and function of the missing limb. Aiming at the situation of losing the whole arm, this paper designs a control system of a human-like arm-shaped prosthesis. Users can compensate for some of the missing functions, achieve self-care and even engage in basic labor, reduce physical and mental pain, and correspondingly liberate nursing work. In the design, C8051F020 single-chip microcomputer is used as the main controller, and through CPLD (Complex Programmable Logic Device), the detection of each joint control quantity is completed and the Lingyang 61 single-chip microcomputer realizes the voice control function.

1. Robot prosthetic structure
The multi-degree-of-freedom robotic prosthesis (upper limb) has 6 degrees of freedom, and its structural schematic diagram is shown in Figure 1.

2. Hardware design of prosthetic control system

2.1 The working principle of the controller

The principle block diagram of the control system is shown in Figure 2, which is mainly composed of four parts: control, drive, controlled object and detection feedback. The control part is mainly completed by C8051f020; the driving part is composed of 6 motor drivers; the controlled object is the six-free prosthesis; the detection feedback part is mainly used to detect the control amount of each joint at the target position by CPLD. The system mainly adopts two control methods: button control and voice control.

2.1.1 Button Control

The system uses 12 independent buttons to control the forward and reverse rotation of 6 motors, and the rotation of the motor drives each joint to move. The corresponding control joint motion of each key is shown in Table 1.

2.1.2 Voice Control

The speech recognition module is completed by SPCE061A microcontroller. First, train each command and save the training results. During system operation, when SPCE061A MCU recognizes a certain command, it will send the command to C8051F020 through serial port, and when C8510F020 receives the corresponding command, it will control the corresponding joint to do the corresponding movement. For example: when SPCE061A recognizes the “finger open” command, it will send the hexadecimal number such as “0x10” through the serial port, when C8051F020 receives “0x10” through the serial port, it will control the finger to open the movement . The same goes for other joint controls. The control form of its voice command is shown in Figure 3.

3. System software design

The system software mainly includes: initialization program, frequency output subroutine, PWM signal output subroutine, keyboard control subroutine, communication subroutine, speech recognition system, CPLD detection encoder output subroutine.

3.1 The main process of the system

Figure 4 shows the flow chart of the entire control system. First initialize the system hardware; then design two working modes: button mode and voice mode; then select three positions for teaching, and save the pulse control amount of each joint in the teaching position through CPLD, and the final design is manual motion To the taught target or automatically move to the taught target position, at this point, the grasping function of the object at the target position can be completed.

3.2 CPLD detection encoder design

It can be seen from the system flow chart that the detection and storage of each joint control amount of each teaching position is completed by CPLD. Since the sensor of the rotary encoder is used in this design, it usually outputs two pulses A and B with a duty ratio of 50%. The number of pulses of A and B reflects the angle that the spindle has turned, and the phase relationship of A and B reflects the rotation of the spindle. turn around. In order to detect the above-mentioned two variables, namely the rotation angle of the main shaft and the rotation direction of the main shaft, the system adopts a direction discrimination subdivision circuit.

The A and B input signals are respectively composed of four states (A, B): (0, 0), (0, 1), (1, 0) and (1, 1). When the spindle rotates in the forward direction, the state transition process is: (0, 0), (1, 0), (1, 1), (0, 1), (0, 0), and it is just the opposite when it rotates in the reverse direction. Therefore, it is only necessary to judge the order of state (1, 0) and state (1, 1) to determine the direction of spindle rotation, and the number of transitions between states (1, 0) and (1, 1) can be determined. The angle that the rotating shaft rotates can be determined, that is, a state transition occurs when the main shaft rotates by 1°. The block diagram of the detection system of the CPLD designed by the characteristics of the rotary encoder is shown in Figure 5.

3.3 Design of Speech Recognition System

Voice recognition is to enable the prosthesis to accurately hear the operator’s voice content, and to accurately complete the operator’s commands. This system is only trained for a specific person, and can only recognize the voice of a specific person. It is mainly completed by Lingyang SPCE061A microcontroller. Its system block diagram is shown in Figure 6.

4. Analysis of experimental results

After completing the above software and hardware design, the actual control and debugging of the whole system is carried out. During the debugging process, each joint can move freely, and the corresponding control of each button is completely correct. During the pressing of the button, there will be jitter. Therefore, the jitter is eliminated through the delay program during the design of the program. In the voice control module, because it adopts two-level command control, that is, the control form shown in Figure 3. The speech system has been trained for many times, and the speech recognition rate for a specific person has reached more than 96%, and the recognition rate is very high. But only on specific people. If there are errors in recognizing other people’s voice signals, individual misoperations will occur. But this error can be controlled. As long as a specific person is trained, and the trained person controls the device, the control error caused by the difference between the controller and the voice input person can be avoided.

The biggest feature of the control system is that it can manually control the prosthesis to do the corresponding activities freely, or use the wearer’s voice to realize the voice intelligent control of the prosthesis to do the corresponding movement. The design method and control are simple, and the purpose of safe and stable operation is realized. In addition, a remote control can be designed for the wearer (provided that the user has only lost one arm), and if both arms are lost, a voice control device can be installed in the appropriate part to control it using voice.

The Links:   PM400DSA060 PM45502C

Delong Laser completed 150 million yuan in financing, and Zhongwei Company participated in the investment

On November 30, Delong Laser, a laser fine micromachining equipment company, announced the completion of a new round of financing of several hundred million yuan. This round of financing was jointly invested by Woyan Capital Alliance and China Micro semiconductor, CLP Fund, Sunny V Fund and other institutions and enterprises.

The company’s early investment institution, Beijing Woyan Investment Center (Limited Partnership), holds 16.45% of the shares and is the second largest shareholder. Woyan Capital said that after this financing, it will help Delong Laser to lay a solid foundation for its deep cultivation in semiconductor, Display, consumer electronics and other fields.

As a manufacturer of etching equipment, China Micro is also an investor in this round of financing. China Microelectronics has capacitive plasma etching equipment, inductive plasma etching equipment and deep silicon etching equipment.

Suzhou Delong Laser Co., Ltd. (abbreviation: Delong Laser) was established in 2005, located in Suzhou Industrial Park, with investment from China and Australia. Specializing in the research and development, production and sales of precision laser processing equipment and lasers, the products are widely used in precision processing fields such as semiconductors, displays, precision electronics, scientific research and new energy.

The third-generation semiconductors such as silicon carbide and gallium nitride are hard and brittle materials. When using traditional mechanical cutting WaferSaw (wafer dicing), wafers made of these materials are prone to edge chipping and other defects, affecting The final yield and reliability of the product need to be replaced by a more advantageous processing method. The official website shows that the current Delong Laser’s silicon carbide wafer laser cutting equipment uses ultra-short pulse laser to ensure the high quality of silicon carbide wafers.

The Links:   FS300R12KE3 LQ9D011K SHARP-LCD

vivo starts its own chip plan, or it will be launched in the X70 series

Recently, it is reported that vivo will release its first chip product, named Yueying, which may be launched in vivo X70 series mobile phones. However, this chip is not a processor chip, but a chip specially used to improve imaging capabilities, which can effectively improve the speed of the image processor and imaging quality.

As early as May last year, vivo applied for two chip trademarks, vivo SOC and vivo chip, covering a series of processor-related products such as central processing units, modems, computer chips, printed circuits, and computer storage devices. It is worth noting that the application for these two trademarks was in September 2019, which means that vivo has already planned for the chip in 2019.

At this year’s Xiaomi conference, the self-developed chip Pengpai C1 focusing on imaging functions was released. It can assist Xiaomi mobile phones to achieve more refined and advanced 3A processing. The dual filter configuration can achieve high and low frequency signals. Parallel processing, the digital signal processing efficiency is improved by 100%, and the CPU and memory usage is very low, which is very similar to the Vivo’s Yueying chip.

For domestic mobile phone manufacturers, self-developed processor chips are not a good choice. First of all, it is too difficult, and there is no corresponding technical support in China. Second, Qualcomm’s chips are the best choice at present. It is possible to use self-developed chips. It will reduce the competitiveness of their own mobile phones, so self-developed chips focusing on imaging are a good choice for many mobile phone manufacturers, and they are also the current choice of manufacturers such as Xiaomi and vivo.

The Links:   LM64C12P 2MBI200VB-120

Make digital predistortion troubleshooting and fine-tuning easy

[Introduction]This article introduces the digital predistortion (DPD) function of the ADI ADRV9002. Some of the debugging techniques used can also be applied to general DPD systems. First, an overview of background information about DPD and some typical problems users may encounter when experimenting with their systems. Finally, the article presents tuning strategies that can be applied to DPD algorithms to analyze performance with the help of DPD software tools.

Introduction

Digital predistortion (commonly referred to as DPD) is an algorithm widely used in wireless communication systems. DPD is designed to suppress spectral regeneration on broadband signals passing through an RF power amplifier (PA), thereby increasing the overall efficiency of the PA. In general, PAs suffer from nonlinear effects and inefficiencies when dealing with high power input signals. Due to spectral regeneration, non-linear effects and spectral interference occur in adjacent frequency bands. Figure 1 shows spectral regeneration before and after DPD correction using the TETRA1 standard on the ADRV9002 platform.

Figure 1. TETRA1 DPD using ADRV9002

The ADRV9002 provides a power-optimized, internally programmable DPD algorithm that can be customized to correct for PA nonlinear effects to improve the overall adjacent channel power ratio (ACPR). Although DPD can bring the expected benefits to communication systems, it is often difficult for inexperienced personnel to get started with DPD, let alone set it up correctly. This is mainly because digital predistortion involves multiple factors that can lead to errors that degrade DPD performance. In fact, even after setting up the hardware correctly, it can be challenging to determine the correct parameters to fine-tune the DPD and obtain an optimal solution. This article is intended to assist engineers using the DPD option in the ADRV9002 and to provide some general strategies for fine-tuning the DPD mode using the available parameters for optimal DPD performance. In addition, MATLAB® tools are used to help users analyze the DPD and eliminate common errors, while providing some insights into the internal DPD operation.

When the DPD option is enabled, the ADRV9002 can provide up to 20MHz of signal bandwidth. This is because the receive bandwidth is limited to 100MHz. A DPD will typically operate with a receive bandwidth five times the transmit bandwidth, so third- and fifth-order intermodulation signals can be seen and corrected. The highest PA peak power signal supported by the ADRV9002 is approximately 1dB (commonly referred to as P1dB) compression region. This metric represents the degree of PA compression. If the PA compresses beyond the P1dB point, there is no guarantee that the DPD will work properly. However, this requirement is not strict; in many cases, DPD will still work beyond the P1dB point and still provide very good ACPR. But this requires a specific analysis of specific issues. In general, DPD may suffer from instability and crashes if compressed too heavily. Later, the compression area will be discussed in detail, including how to use MATLAB tools to observe the current PA compression status.

See the “Digital Predistortion” chapter of UG-1828 for more details on DPD.

Architecture

There are two basic ways to perform DPD functions. The first method is called indirect DPD, which captures the signal before and after the PA. In contrast to the direct DPD method, the signal is captured before the DPD module and after the PA. The advantages and disadvantages of each approach are beyond the scope of this article. Indirect DPD understands its nonlinear characteristics by analyzing the signal before and after PA, and performs inversion on the DPD module. Direct DPD analyzes the pre-DPD and post-PA signals and removes the error between the two by applying predistortion on the DPD module. Users should be aware that the ADRV9002 uses an indirect approach and the effects associated with it. Also, it is important to understand that when using MATLAB tools, capturing data is also an indirect method.

Figure 2 shows a simplified block diagram of the DPD operation of the ADRV9002. The input signal u(n) enters the DPD module. DPD will predistort the signal and generate x(n). Here, it’s called transmit capture, but this is actually a predistorted version of the transmit signal. Then, the signal passes through the PA to become y(n), and the signal is finally sent to the air. Here, y(n) is called the receive capture, but this is actually the transmitted signal after PA. Then, y(n) is fed back to the receiver port for use as an observation receiver. Essentially, the DPD engine will use the captured x(n) and y(n) and then generate coefficients that will be applied in the next iteration of DPD.

Figure 2. Simplified block diagram of indirect DPD

Operating mode

ADRV9002 supports TDD and FDD operation on DPD. In TDD mode, the DPD is updated every transmit frame. This means that during the transmit frame, the receiver will act as the observation path. In FDD, since the transmitter and receiver operate simultaneously, a dedicated receiver channel is required. The 2T2R in the ADRV9002 is capable of supporting DPD in 2T2R/1T1R TDD and 1T1R FDD modes.

DPD mode

structure

The following equations show the DPD mode implemented in the transmit path.

in:

u(n) is the input signal of DPD, x(n) is the output signal of DPD

T is the total number of branches in DPD mode

ψtis the polynomial function used to implement the branch t look-up table (LUT), ltis the amplitude delay

ktis the data delay

at,lti is the coefficient calculated by the DPD engine

bt,lt,i is the switch to enable or disable the item

i is the exponent and power of the polynomial term

The user can configure the number of terms of the polynomial for each branch. ADRV9002 provides 3 memory item branches and 1 cross-item branch, and the order of each branch is from 0 to 7.

Mode selection

The user can select the default mode option provided by the ADRV9002 (shown in Figure 3), which should be suitable for most common applications. Alternatively, users can choose their own mode by enabling and disabling items. The first 3 branches (0 to 2) represent memory items, where branch 1 is the central branch. Branch 3 is the cross term branch.

Note that branch 3 (or the cross-term branch) should not enable zero-order terms in order to distinguish it from the mnemonic branch.

Figure 3. Terms of the DPD mode polynomial

LUT size: User can set the LUT size. The ADRV9002 offers two options, 256 and 512. By choosing a size of 512, the user will get better quantization noise levels and thus better ACPR, since in general larger sizes will give better signal resolution. For narrowband applications, ADI recommends 512 as the default option. 256 can be used for wideband because the noise level is less critical and it improves computation and power.

Pre-LUT scaling: The user can set the pre-LUT scaling module to scale the input data to make it more suitable for the compander. The compander selects the signal from the transmitter and compresses it to fit into the 8-bit LUT address. Depending on the input signal level, the user can adjust this value to optimize LUT utilization. Its value can be set in the range (0,4) with a step size of 0.25. More information on companders is provided in the last section of this article.

configure

Figure 4. Basic configuration to enable DPD

In order to perform DPD, the user will have to enable the external loopback path on the PA and then set the feedback power to ensure it is not out of range. Note that this is peak power, not average power. Too much power or too little power will affect DPD performance. Users also need to set the external path delay, which can be obtained using External_Delay_Measurement.py. Users can find the script in the ADRV9002 evaluation software installation path under the IronPython folder.

Note that only external delays need to be set for high sample rate curves (for example, LTE 10MHz). For low sample rate curves (TETRA1 25kHz) the user can set this to 0. Later in this article, the software tool will be used to observe the capture data to understand the effects of external delays.

Other configuration

Figure 5. Additional configuration on DPD

User configurable number of samples. By default, the user can set 4096 samples. The default value is recommended. In most cases, the default 4096 samples will provide the optimal solution for DPD.

Other power scalings are more advanced parameters. In most cases, the default value of 4 is recommended for the ADRV9002. This parameter is related to the internal correlation matrix. Based on experiments, the default value provides the best performance for existing waveforms and PAs tested by ADI. In rare cases, if the input signal amplitude is extremely small or extremely large, the user can try to adjust the value to smaller and larger values ​​to maintain the appropriate condition number for the correlation matrix, resulting in a more stable solution.

Rx/Tx Normalization: The user should set the receiver/transmitter normalization to the region where the data is linear. In Figure 6, linear regions are shown in red. In this region, the power of the data does not reach the compressed region and is high enough to be used to calculate the gain. After selecting this region, DPD can estimate the transmitter and receiver gains and then proceed to further processing the algorithm. In most cases, -25dBFS to -15dBFS should fit most standard PAs. However, the user should still take care, as a particular PA may have a very different AM/AM curve shape, in which case appropriate modifications will be required. This is explained in detail later in this article.

Figure 6. Typical AM/AM curve.Linear regions are shown in red

set up

hardware setup

Figure 7. Typical DPD Hardware Block Diagram

A typical setup is shown in Figure 7. Before the signal enters the PA, a low-pass filter is required to prevent LO signal harmonics from appearing. In some cases, an external LO may be required if the internal LO phase noise performance does not meet the application requirements. In this case, the external LO source needs to be synchronized to DEV_CLK. Narrowband DPDs with tighter near-band noise requirements typically require an external LO. It is generally recommended to provide a variable attenuator in front of the PA to prevent damage to the PA. The feedback signal should have appropriate attenuation to set the peak power as discussed in the previous section.

software settings

IronPython

Download the IronPython library for executing IronPython code on the GUI.

Figure 8. IronPython GUI window

Here, the user can run dpd_capture.py in the IronPython window of the GUI, as shown in Figure 8, which is provided with the MATLAB tool to obtain capture data for the transmitter and receiver. The DPD sample rate is also included in the captured file.

Note that the script should be run in the startup or calibration state.

MATLAB tools

MATLAB tool to analyze data captured from dpd_capture.py. This tool will help check signal integrity, signal alignment, PA compression levels, and finally, fine-tuning of the DPD.

MATLAB Tools requires MATLAB Runtime. The first installation takes some time to download. Once installed, the user can load the data captured by the IronPython script and observe the graph, as shown in Figure 9.

Figure 9. MATLAB DPD Analyzer

Additionally, users can set high/low thresholds for data normalization and press “Reload” to see changes.

First, plot the normalized transmitter and receiver data in the time domain. Users can zoom in on the graph to observe the alignment of the transmitter and receiver. Only the real part of the data is shown here, but the user can easily plot the imaginary part as well. The real and imaginary parts should usually be aligned or not.

Then there’s the transmitter and receiver spectrum – blue is the transmitter, red is the receiver. Note that this is indirect DPD – the transmitter data will be predistorted data, not the transmitter data path on the SSI port.

Next, there are two AM/AM curves, both in linear and dB coordinates. These are important metrics about DPD performance and PA compression status.

AM/PM curves and receiver/transmitter phase differences are also provided.

Also, there are high and low threshold numbers. These numbers should match the settings in the ADRV9002 TES evaluation software.

Note that since APIs are provided to capture data, users can develop their own graphical and analytical models if desired. This tool provides some common checks for analyzing DPD. API includes:

adi_ADRV9002_dpd_CaptureData_Read, this is to read DPD capture data, must be run in calibration or boot state.

adi_ADRV9002_DpdCfg_t → dpdSamplingRate_Hz, this is the DPD sampling rate, a read-only parameter.

Typical problems

DPD can be affected by many different factors. Therefore, it is important to ensure that all potential issues listed are considered and checked by the user. The user should make sure the hardware is connected properly before considering all the issues.

Sending data overload

Figure 10. Simplified hardware block diagram of DPD

Figure 10 shows a simplified schematic block diagram of the ADRV9002 implementing DPD. Transmitter data from the interface can overload the DAC. If the DAC is overloaded, the RF signal from the transmitter is distorted before the PA gets involved. Therefore, it is important to ensure that the transmitter data does not overload the DAC.

The user can observe whether the transmitter DAC is overloaded through the GUI. Figure 11 shows the TETRA1 25kHz waveform. The peak is still far from the digital full scale. For the ADRV9002, it is recommended to stay at least a few dB from full scale to avoid overloading the DAC. It is difficult to quantify how much the user should back off – this is because the DPD will attempt to perform predistortion, and the predistorted signal will be “peak-extended”, potentially overloading the DAC. It depends on how the DPD handles a particular PA – in general, the harder the PA is compressed, the more headroom it needs to peak.

Figure 11. Part of the TETRA1 standard waveform in the time domain

Receiver data overload

Another common mistake is overloading the feedback DAC with receiver data. The reason for this error is that there is not enough attenuation returned to the receiver port. This can be observed from the debug tool, and the effect is that the receiver data is clipped so that the transmitter and receiver are not aligned effectively, resulting in a miscalculation of the DPD. DPD usually behaves very badly, increasing the noise throughout the spectrum.

Figure 12. Receiver Data Overdrive

Receiver data underrun

Compared to receiver overload, this problem is often overlooked. The problem is that the feedback attenuation is not set correctly. The user may provide too much attenuation to the feedback path, which results in too small receiver data. By default, -18dBm peak is recommended for the ADRV9002 due to its ability to convert data from analog to digital to a known good DPD power level. But users can adjust this number according to their needs. Users should be aware that DPD feedback receivers use different attenuators than conventional receivers, with higher step sizes. The attenuation level is adjusted by the peak power level set by the user. -23dBm is the lowest power level (0 attenuation) – if you go outside this range you will get a low power level which will affect DPD performance. As a rule of thumb, the user should ensure that the feedback power is always measured and set correctly. Many times users tend to try different power levels but forget to set the feedback power correctly, causing this problem.

TDD and FDD

DPD in TDD mode must run in an automatic state machine. When evaluating with TES, in manual TDD mode, the user can still enable DPD, but the performance will be poor. This is because DPD can only work on a frame basis. In manual TDD mode, the length of the frame will be determined by the transmit/receive enable signal toggle. In other words, each play and stop is a frame. However, during the artificial switching time, the PA has transitioned to a different temperature state. Therefore, the DPD state cannot be maintained without the use of an automatic TDD mode that can frequently toggle the transmit enable signal. However, in FDD mode, DPD should work normally.

For example, a user might want to use TETRA1, which follows a TDD-like frame scheme (actually TDM-FDD). Therefore, one should not directly select TDD mode and check DPD manually, and DPD tends to perform badly. Instead, the user can use a “custom FDD” profile, choosing the same sampling rate and bandwidth as TETRA1, or the user can set the TETRA1 TDD frame timing and use the automatic TDD mode. Both methods can provide better performance than manual TDD.

Transmitter/receiver misaligned

The ADRV9002 will attempt to time align the transmitter and receiver data. When the user captures data, the user expects the data to be aligned. Latency measurements are done during initial calibration. However, for high sample rate curves, more precise subsample alignment needs to be done separately.

Figure 13. Unaligned DPD capture

Figure 14. Zoom in on the transmitter and receiver real data for LTE10 (not aligned)

DPD is an adaptive algorithm that requires computing the error of two entities, namely the transmitter and the receiver. Before calculating the error of the transmitter and receiver, the two signals need to be properly aligned – especially when using high sample rate curves (e.g. LTE10). Alignment is critical because the spacing between samples is very small. Therefore, the user needs to run the script External_Delay_Measurement.py to extract the external path delay. This number can be entered under Board Configuration → Path Delay.

Figure 15. IronPython External Latency Measurement

The effect of misaligning the transmitter and receiver data is that the user will observe a noisier AM/AM curve.

Figure 16. Aligned DPD capture

After setting the path delay numbers, it can be observed that the AM/AM and AM/PM curves are cleaner and less noisy. The phase difference is also significantly reduced.

Figure 17. Enlarged LTE10 transmitter and receiver real data (aligned)

PA overload

Each PA has its own specification for how much compression it can handle. While P-1dB figures are often given in data sheets, in practice it is still recommended to make an accurate measurement of DPD to ensure the compression point is at P-1dB. With DPD software, users can view AM/AM curves based on captured data to see how close the compression point is to P-1dB.

Figure 18. PA overload data

Figure 19. AM/AM curve in dB (zoomed in)

However, if the signal exceeds P-1dB, this can cause the DPD to become unstable, or even to break, with the spectrum jumping to very high levels and never coming down again. In Figure 19, the compression at the peak is well beyond the 1dB region, and the shape of the curve starts to become flatter. This means that the PA is overdriven, and in order to increase the output power, more input will be provided to support the output power level. At this point, if the user decides to continue increasing the input power, the DPD performance will degrade.

General strategy mode selection and adjustment

Indirect DPD simply captures data before and after the PA, and the DPD engine will try to simulate the opposite effects of the PA. The LUT is used to apply the effect using coefficients, the mode is based on polynomials. This means that DPD is more of a curve fitting problem and the user will try to “curve fit” nonlinear effects using terms. The difference is that a curve fitting problem fits a single curve, whereas DPD also has to account for memory effects. ADRV9002 has 3 memory branches, and 1 cross branch for modeling DPD LUTs.

Figure 20. Memory entry and cross entry mapping

Figure 20 shows the 3 memory branches and 1 cross branch provided by the ADRV9002. The general strategy is similar to the curve fitting problem. Users can start with a baseline and then add and remove items. In general, the central branch must exist (branch 1). Users can add and remove items one by one to test the effects of DPD. The user can then proceed to add two memory branches (branch 0 and 2) to add the effect of memory effect correction. Note that since the ADRV9002 has two side branches, these branches should be identical – that is, should be symmetrical. Also, adding and removing items must be done individually. Finally, users can experiment with cross terms. The cross term completes the curve fitting problem mathematically, thus providing better DPD performance.

Note that the user must not skip items by leaving them blank, as this will lead to undesirable behavior of DPD. Also note that the user must not set item 0 on the cross-item branch, as this is also invalid from a mathematical point of view.

Figure 21. Invalid mode item settings

Advanced adjustment

Compander and pre-LUT scaling module

In the previous section, companders were mentioned. This concept can be confusing when first reading the user guide, not knowing what it means or what to choose (256 or 512). The purpose of a compander is to compress the input data and put it into a LUT.

Figure 22. Compander – Estimating the shape of the square root

The general shape of a compander is the square root, where the I/Q data comes in. Before putting this data into the LUT, the equation √(i(n)2+q(n)2) will be used to obtain the signal amplitude from the previous equation. However, due to the high speed requirements of the square root operation and the need to map it to a LUT (8-bit or 9-bit), a compander is used. Figure 22 is an ideal square root curve. The actual implementation will not be shown here, but in short this will be an estimate of the square root curve.

Once you understand how the data fits into the LUT, you can start tweaking the data more wisely. The ADRV9002 can select either 8-bit (256) or 9-bit (512) as the LUT size. A larger LUT means double the address location of the data. This means that the resolution of the data is higher and, in general, the level of quantization noise is better. For narrowband applications, since noise is very important, 512 is always recommended. For broadband applications, since the noise level is less critical, either option can be used. However, if you choose 512, the power consumption will be slightly higher and the calculation speed will be slower.

Histogram and CFR

In the DPD configuration section, pre-scaling was briefly mentioned. This parameter is used to provide a large amount of input data to the LUT. The reason for the large amount of input data is that, in some cases, the data is not used correctly by DPD. For such PA compression problems, it is the high-amplitude samples that are really compressed and cause the problem. Therefore, all samples cannot be treated equally; instead, focus on high-amplitude samples.

Take a look at the TETRA1 standard waveform histogram (see Figure 23 and Figure 24). As you can see, most of the values ​​appear in the mid to high amplitude region. This is because the TETRA1 standard uses a D-QPSK modulation scheme and as a result the signal will acquire a constant envelope. There is not much difference between peak power and average power.

This is exactly what DPD needs. As mentioned earlier, DPD will capture higher amplitude samples and therefore will better characterize the PA’s behavior.

Figure 23. TETRA1 amplitude histogram

Figure 24. TETRA1 power histogram

Now, look at the LTE10 standard in a similar fashion. LTE uses an OFDM modulation scheme that groups together hundreds or thousands of sub-carriers. Here again the amplitude and power of LTE10 can be seen. The difference from TETRA1 can be easily observed, i.e. the peaks are very far from the main mean.

Figure 25. LTE10 amplitude histogram without CFR

Figure 26. LTE power histogram without CFR

In the power histogram (see Figure 26), if you zoom in on the far end, you can see that there are still very high peaks, but with a very low probability. For DPD, this is very detrimental. There are two reasons.

First, low probability counts of high peaks (high amplitude signals) will make the PA extremely inefficient. For example, LTE PAPR is about 11 dB. This is a big difference. To avoid damaging the PA, the input levels will need to be backed off significantly. Therefore, the PA is not using most of its gain capability to boost power.

Secondly, the high peak is also wasting the utilization of the LUT. Because of these high peaks, LUTs will allocate a lot of resources to them and only a small fraction of the LUTs for most of the data. This reduces DPD performance.

Figure 27. Zooming in on high-amplitude samples

Peak Clipping (CFR) technology moves signal peaks down to a more acceptable level. This is typically used for OFDM type signals. The ADRV9002 does not contain an on-chip CFR, so this function needs to be implemented externally. For this purpose, a CFR version of the LTE waveform is also included in the ADRV9002 TES evaluation software. CFR_sample_rate_15p36M_bw_10M.csv is shown in Figure 28. It can be seen that at high power, the peak of the signal is limited to a certain level (slope at the end) due to CFR. This effectively pushes the PAPR to 6.7dB, a difference of about 5dB. The operation of the CFR will cause “damage” to the data as the EVM will be degraded. However, compared to the entire waveform, the probability of high-level amplitude peaks is very small, which brings huge advantages.

Figure 28. LTE10 amplitude histogram with CFR

Figure 29. LTE10 power histogram with CFR

in conclusion

DPD is a complex algorithm that many people find difficult to use. For optimal results, it takes a lot of effort and care to set up hardware and software. ADI’s ADRV9002 provides an integrated on-chip DPD that will significantly reduce complexity. ADRV9002 is also equipped with DPD software tools that can help users analyze their DPD performance.

About Analog Devices

ADI is the world’s leading high-performance analog technology company dedicated to solving the toughest engineering design challenges. With outstanding detection, measurement, power, connection and interpretation technology, build intelligent bridges between the real and digital worlds, thereby helping customers to re-understand the world around them. For details, please visit ADI’s official website www.analog.com/cn.

About the Author

Wangning Ge is a Product Applications Engineer based in Somerset, NJ. He joined Analog Devices in 2019. Before that, he worked as a software engineer at Nokia (formerly Alcatel-Lucent). Wangning has extensive experience in DPD algorithm design and base station RF applications. He is responsible for the ADRV9001 family of transceiver products.

By: Wangning Ge, Product Application Engineer, Analog Devices

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In order to further supplement the demand for talents, Guowei Sierxin assists the EDA competition

EDA is a “small but refined” link in the industrial chain in the integrated circuit industry. With the continuous development of large-scale integrated circuit, computer and Electronic system design technology, the role of EDA technology is increasing at an alarming rate.

Because of the late start of domestic EDA, there is a big gap between foreign EDA giants. It should be noted that the EDA industry is an intelligence-intensive industry and is highly dependent on talents. If my country wants to develop EDA, talents are an indispensable part, but there is still a huge EDA talent gap in my country. Realizing the importance of talents, in the past two years, domestic EDA manufacturers have also made a lot of efforts in terms of talents.

  Help EDA Competition

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  The strength of Guowei Sierxin

Guowei Sierxin, which spares no effort in solving talents, also has quite strong strength itself. As a key EDA enterprise in Shanghai, SIR Core’s business mainly covers prototype verification, hardware simulation and heterogeneous verification cloud systems, and is a leading verification solution provider in the industry.

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  Summarize

Talent and technology are necessary elements for the development of EDA. While continuously cultivating and excavating talents, Guowei Sierxin is also continuously increasing its investment in research and development, and speaks with real strength through independent research and development.

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At present, the breakthrough of domestic manufacturers still depends on the domestic market. Guowei Sierxin is accumulating existing technologies, close to customer needs, and working closely with customers to develop innovation and rapidly iterate. While providing customers with higher value, we are also working hard to promote the development of China’s integrated circuits.

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