According to a report by TechNews citing an article from the international column Project Syndicate, Burn Lin, former R&D Vice President of TSMC, Chintay Shih, former President of the Industrial Technology Research Institute, and Chang-Tai Hsieh, an Academia Sinica member and economics professor at the University of Chicago Booth School of Business, collaborated on an article titled “How America’s CHIPS Act Hurts Taiwan.”

In the article, they collectively elucidated how US semiconductor subsidies weaken TSMC’s strength, rendering the entire semiconductor industry more vulnerable. Additionally, they expressed concern that if China were to blockade or invade Taiwan, the supply chain would become compromised.

The US CHIPS and Science Act, aiming to address this issue with a USD 52 billion subsidy, seeks to encourage semiconductor manufacturers to relocate to the United States. However, according to the report addressing on the design of the bill, its objectives may not be achievable and could even weaken Taiwan’s most crucial industry, posing a threat to Taiwan’s security.

Concerns Arise Over Chip Act Threatening Taiwan’s Security

Currently, the semiconductor industry is dominated by specialized companies distributed globally. TSMC specializes in contract manufacturing, focusing primarily on high-end chips. Other important companies include AMD, NVIDIA, Qualcomm, ASML, Tokyo Electron, and Arm.

Specialization in the industry offers two major benefits.

Firstly, each part of the global supply chain can concentrate on its core expertise and advance further, benefiting other supply chains. Secondly, the production capacity of each link in the global supply chain increases, enhancing resilience against demand shocks.

The cost of specialization is that the industry becomes vulnerable to supply shocks. This issue is not unique to Taiwan; all segments of the supply chain face potential bottlenecks.

However, unlike other segments, Taiwan is reportedly confronted with territorial claims from China. Therefore, the United States and Japan have offered substantial subsidies for TSMC’s relocation. TSMC is constructing new factories in Kumamoto, Japan, and Phoenix, Arizona, in the United States.

Currently, Fab 1 in Kumamoto has been completed according to plan, and many of TSMC’s suppliers have also set up shop there. However, the Arizona plant is substantially behind schedule, and fewer TSMC suppliers have followed suit to establish operations in the United States.

Moreover, TSMC’s experience at its Portland plant in Washington state over the past 25 years has raised doubts about the prospects of the Arizona plant. TSMC struggled to find competitive workers there; even with identical training and equipment, production costs in the U.S. were still 50% higher than in Taiwan. Therefore, TSMC chose not to expand its Portland plant further.

Still, the fundamental issue lies in the fact that while American workers are skilled in chip design technology, they lack the skills required for chip manufacturing, which is crucial in this field.

The article further mentions that TSMC’s Phoenix plant will continue to struggle because there is a shortage of American workers with the skills necessary for semiconductor manufacturing.

As warned by TSMC’s founder, Morris Chang, in 2022, seeking economic security by relocating semiconductor manufacturing to the United States is an expensive exercise in futility. Furthermore, while the USD 52 billion subsidy from the United States may seem substantial, it is insufficient to establish a self-sufficient semiconductor ecosystem in Phoenix.

Additionally, the article points out that Taiwan’s industrial planners have deliberately chosen a niche market built upon existing manufacturing advantages, without attempting to replicate the model of the leading Intel at that time, due to the scarcity of Taiwanese workers with the necessary design skills. Similarly, Japan’s subsidies for TSMC are likely to succeed because Japan already possesses an ample supply of skilled manufacturing workers.

The article also highlights three major risks brought about by the US chip act at the end:

Firstly, if TSMC shifts its focus and loses its investment in innovation, the biggest losses will be incurred by its customers and suppliers, most of which are American companies.

Moreover, it may hinder AI development, as this field largely relies on TSMC-manufactured advanced chips. Consequently, TSMC may reduce its investment in production capacity in Taiwan, reducing the entire semiconductor industry’s ability to withstand demand shocks.

Lastly, TSMC may lose its way and risk being replaced by other companies, losing its leadership position in the field of advanced semiconductor manufacturing.

Well-Intentioned US Chip Act with Poor Design May Ultimately Harm Taiwan’s Economy

The commentary suggests that despite the well-intentioned nature of the US chip act, its design is flawed. Instead of establishing a sustainable semiconductor manufacturing cluster in the United States, it may result in long-term damage to TSMC and ultimately harm Taiwan’s economy.

A better approach for the United States, per the report, would be to protect its own economic security while strengthening Taiwan’s, committing to defend Taiwan, and building production capacity in countries like Japan. This strategy may be more prudent in the long run.

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(Photo credit: TSMC)

NVIDIA, the global leader in artificial intelligence (AI) chips, is set to commence pre-orders for its AI chip H20 specially designed for the Chinese market by the end of March this year in response to US export bans, according to sources cited by a report from STAR Market Daily.

However, due to consecutive reductions in specifications, the cost-performance ratio has diminished, prompting cautious attitudes among Chinese distributors.

The report further mentions that by the end of 2022, the US Department of Commerce restricted the export of NVIDIA AI chips to China due to concerns about their potential military use. In response, NVIDIA has repeatedly reduced product performance to comply with US regulations. The H20 chip, derived from the H800, is specifically designed as a ‘special edition’ for the Chinese market.

Citing industry sources, STAR Market Daily‘s report also states that H20 will be available for pre-order following NVIDIA’s GTC 2024 conference (March 18th to 21st), with deliveries possible within a month. The sources note that H20’s performance is approximately one-fourth that of H100, resulting in a less favorable cost-performance ratio. Additionally, production capacity is unable to meet demand, with mass production expected to commence in the second half of this year.

A distributor in Beijing pointed out that currently, there is not significant demand for the H20 chip, primarily due to its relatively low cost-performance ratio. Chinese-made AI chips serve as viable alternatives.

The same distributor noted that most of the foundational technology for computing power providers is still supported by NVIDIA. The advantages of adopting the H20 lie in its compliance and low migration costs. However, the trend toward self-developed AI chip in China is a long-term certainty, presenting a choice between the two options.

The distributor further emphasized that NVIDIA’s introduction of the H20 is primarily aimed at stabilizing its presence in the Chinese market. As long as the product specifications slightly surpass those of domestically produced chips, it should suffice. However, whether there is demand for this chip still requires market validation.

Another distributor from the Shenzhen cited in the report also stated that it is uncertain whether they will stock the H20 chip, as their decision depends on subsequent market demand.

Regarding the need for H20, TrendForce believes Chinese companies will continue to buy existing AI chips in the short term. NVIDIA’s GPU AI accelerator chips remain a top priority—including H20, L20, and L2—designed specifically for the Chinese market following the ban.

At the same time, major Chinese AI firms like Huawei, will continue to develop general-purpose AI chips to provide AI solutions for local businesses. Beyond developing AI chips, these companies aim to establish a domestic AI server ecosystem in China.

TrendForce recognizes that a key factor in achieving success will come from the support of the Chinese government through localized projects, such as those involving Chinese telecom operators, which encourage the adoption of domestic AI chips.

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(Photo credit: NVIDIA)

Under the formidable impetus of AI, global enterprises are vigorously strategizing for AI chip development, and China is no exception. Who are the prominent AI chip manufacturers in China presently? How do they compare with industry giants like NVIDIA, and what are their unique advantages? A report from TechNews has compiled an overview of eight Chinese AI chip manufacturers in self-development.

• An Overview of AI Chips

In broad terms, AI chips refer to semiconductor chips capable of running AI algorithms. However, in the industry’s typical usage, AI chips specifically denote chips designed with specialized acceleration for AI algorithms, capable of handling large-scale computational tasks in AI applications. Under this concept, AI chips are also referred to as accelerator cards.

Technically, AI chips are mainly classified into three categories: GPU, FPGA, and ASIC. In terms of functionality, AI chips encompass two main types: training and inference. Regarding application scenarios, AI chips can be categorized into server-side and mobile-side, or cloud, edge, and terminal.

The global AI chip market is currently dominated by Western giants, with NVIDIA leading the pack. Industry sources cited by TechNews have revealed data that NVIDIA nearly monopolizes the AI chip market with an 80% market share.

China’s AI industry started relatively late, but in recent years, amid the US-China rivalry and strong support from Chinese policies, Chinese AI chip design companies have gradually gained prominence. They have demonstrated relatively outstanding performance in terminal and large model inference.

However, compared to global giants, they still have significant ground to cover, especially in the higher-threshold GPU and large model training segments.

GPUs are general-purpose chips, currently dominating the usage in the AI chip market. General-purpose GPU computing power is widely employed in artificial intelligence model training and inference fields. Presently, NVIDIA and AMD dominate the GPU market, while Chinese representative companies include Hygon Information Technology, Jingjia Micro, and Enflame Technology.

FPGAs are semi-customized chips known for low latency and short development cycles. Compared to GPUs, they are suitable for multi-instruction, single-data flow analysis, but not for complex algorithm computations. They are mainly used in the inference stage of deep learning algorithms. Frontrunners in this field include Xilinx and Intel in the US, with Chinese representatives including Baidu Kunlunxin and DeePhi.

ASICs are fully customized AI chips with advantages in power consumption, reliability, and integration. Mainstream products include TPU, NPU, VPU, and BPU. Global leading companies include Google and Intel, while China’s representatives include Huawei, Alibaba, Cambricon Technologies, and Horizon Robotics.

In recent years, China has actively invested in the field of self-developed AI chips. Major companies such as Baidu, Alibaba, Tencent, and Huawei have accelerated the development of their own AI chips, and numerous AI chip companies continue to emerge.

Below is an overview of the progress of 8 Chinese AI chip manufacturers:

1. Baidu Kunlunxin

Baidu’s foray into AI chips can be traced back to as early as 2011. After seven years of development, Baidu officially unveiled its self-developed AI chip, Kunlun 1, in 2018. Built on a 14nm process and utilizing the self-developed XPU architecture, Kunlun 1 entered mass production in 2020. It is primarily employed in Baidu’s search engine and Xiaodu businesses.

In August of the same year, Baidu announced the mass production of its second-generation self-developed AI chip, Kunlun 2. It adopts a 7nm process and integrates the self-developed second-generation XPU architecture, delivering a performance improvement of 2-3 times compared to the first generation. It also exhibits significant enhancements in versatility and ease of use.

The first two generations of Baidu Kunlunxin products have already been deployed in tens of thousands of units. The third-generation product is expected to be unveiled at the Baidu Create AI Developer Conference scheduled for April 2024.

2. T-Head (Alibaba)

Established in September 2018, T-Head is the semiconductor chip business entity fully owned by Alibaba. It provides a series of products, covering data center chips, IoT chips, processor IP licensing, and more, achieving complete coverage across the chip design chain.

In terms of AI chip deployment, T-Head introduced its first high-performance artificial intelligence inference chip, the HanGuang 800, in September 2019. It is based on a 12nm process and features a proprietary architecture.

In August 2023, Alibaba’s T-Head unveiled its first self-developed RISC-V AI platform, supporting over 170 mainstream AI models, thereby propelling RISC-V into the era of high-performance AI applications.

Simultaneously, T-Head announced the new upgrade of its XuanTie processor C920, which can accelerate GEMM (General Matrix Multiplication) calculations 15 times faster than the Vector scheme.

In November 2023, T-Head introduced three new processors on the XuanTie RISC-V platform (C920, C907, R910). These processors significantly enhance acceleration computing capabilities, security, and real-time performance, poised to accelerate the widespread commercial deployment of RISC-V in scenarios and domains such as autonomous driving, artificial intelligence, enterprise-grade SSD, and network communication.

3. Tencent 

In November 2021, Tencent announced substantial progress in three chip designs: Zixiao for AI computing, Canghai for image processing, and Xuanling for high-performance networking.

Zixiao has successfully undergone trial production and has been activated. Reportedly, Zixiao employs in-house storage-computing architecture and proprietary acceleration modules, delivering up to 3 times the computing acceleration performance and over 45% cost savings overall.

Zixiao chips are intended for internal use by Tencent and are not available for external sales. Tencent profits by renting out computing power through its cloud services.

Recently, according to sources cited by TechNews, Tencent is considering using Zixiao V1 as an alternative to the NVIDIA A10 chip for AI image and voice recognition applications. Additionally, Tencent is planning to launch the Zixiao V2 Pro chip optimized for AI training to replace the NVIDIA L40S chip in the future.

4. Huawei

Huawei unveiled its Huawei AI strategy and all-scenario AI solutions at the 2018 Huawei Connect Conference. Additionally, it introduced two new AI chips: the Ascend 910 and the Ascend 310. Both chips are based on Huawei’s self-developed Da Vinci architecture.

The Ascend 910, designed for training, utilizes a 7nm process and boasts computational density that is said to surpass the NVIDIA Tesla V100 and Google TPU v3.

On the other hand, the Ascend 310 belongs to the Ascend-mini series and is Huawei’s first commercial AI SoC, catering to low-power consumption areas such as edge computing.

Based on the Ascend 910 and Ascend 310 AI chips, Huawei has introduced the Atlas AI computing solution. As per the Huawei Ascend community, the Atlas 300T product line includes three models corresponding to the Ascend 910A, 910B, and 910 Pro B.

Among them, the 910 Pro B has already secured orders for at least 5,000 units from major clients in 2023, with delivery expected in 2024. Sources cited by the TechNews report indicate that the capabilities of the Huawei Ascend 910B chip are now comparable to those of the NVIDIA A100.

Due to the soaring demand for China-produced AI chips like the Huawei Ascend 910B in China, Reuters recently reported that Huawei plans to prioritize the production of the Ascend 910B. This move could potentially impact the production capacity of the Kirin 9000s chips, which are expected to be used in the Mate 60 series.

5. Cambricon Technologies

Founded in 2016, Cambricon Technologies focuses on the research and technological innovation of artificial intelligence chip products.

Since its establishment, Cambricon has launched multiple chip products covering terminal, cloud, and edge computing fields. Among them, the MLU 290 intelligent chip is Cambricon’s first training chip, utilizing TSMC’s 7nm advanced process and integrating 46 billion transistors. It supports the MLUv02 expansion architecture, offering comprehensive support for AI training, inference, or hybrid artificial intelligence computing acceleration tasks.

The Cambricon MLU 370 is the company’s flagship product, utilizing a 7nm manufacturing process and supporting both inference and training tasks. Additionally, the MLU 370 is Cambricon’s first AI chip to adopt chiplet technology, integrating 39 billion transistors, with a maximum computing power of up to 256TOPS (INT8).

6. Biren Technology 

Established in 2019, Biren Technology initially focuses on general smart computing in the cloud.

It aims to surpass existing solutions gradually in various fields such as artificial intelligence training, inference, and graphic rendering, thereby achieving a breakthrough in China’s produced high-end general smart computing chips.

In 2021, Biren Technology’s first general GPU, the BR100 series, entered trial production. The BR100 was officially released in August 2022.

Reportedly, the BR100 series is developed based on Biren Technology’s independently chip architecture and utilizes mature 7nm manufacturing processes.

7. Horizon Robotics 

Founded in July 2015, Horizon Robotics is a provider of smart driving computing solutions in China. It has launched various AI chips, notably the Sunrise and Journey series. The Sunrise series focuses on the AIoT market, while the Journey series is designed for smart driving applications.

Currently, the Sunrise series has advanced to its third generation, comprising the Sunrise 3M and Sunrise 3E models, catering to the high-end and low-end markets, respectively.

In terms of performance, the Sunrise 3 achieves an equivalent standard computing power of 5 TOPS while consuming only 2.5W of power, representing a significant upgrade from the previous generation.

The Journey series has now iterated to its fifth generation. The Journey 5 chip was released in 2021, with global mass production starting in September 2022. Each chip in the series boasts a maximum AI computing power of up to 128 TOPS.

In November 2023, Horizon Robotics announced that the Journey 6 series will be officially unveiled in April 2024, with the first batch of mass-produced vehicle deliveries scheduled for the fourth quarter of 2024.

Several automotive companies, including BYD, GAC Group, Volkswagen Group’s software company CARIAD, Bosch, among others, have reportedly entered into cooperative agreements with Horizon Robotics.

8. Enflame Technology

Enflame Technology, established in March 2018, specializes in cloud and edge computing in the field of artificial intelligence.

Over the past five years, it has developed two product lines focusing on cloud training and cloud inference. In September 2023, Enflame Technology announced the completion of Series D funding round of CNY 2 billion.

In addition, according to reports cited by TechNews, Enflame Technology’s third-generation AI chip products are set to hit the market in early 2024.

Conclusion

Looking ahead, the industry remains bullish on the commercial development of AI, anticipating a substantial increase in the demand for computing power, thereby creating a significant market opportunity for AI chips.

Per data cited by TechNews, it has indicated that the global AI chip market reached USD 580 billion in 2022 and is projected to exceed a trillion dollars by 2030.

Leading AI chip manufacturers like NVIDIA are naturally poised to continue benefiting from this trend. At the same time, Chinese AI chip companies also have the opportunity to narrow the gap and accelerate growth within the vast AI market landscape.

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(Photo credit: iStock)

DRAM prices have risen for three consecutive months, a trend attributed to Chinese clients accepting the price hike requests from memory manufacturers, as reported by Nikkei on February 16th.

As per data cited by Nikkei, the wholesale price (transaction price) of the benchmark product DDR4 8Gb was around USD 1.85 each in January 2024, marking a 9% increase from the previous month (December 2023). The price of the smaller 4Gb product was around USD 1.40 each, representing an 8% increase from the previous month. The aforementioned prices have been rising for the third consecutive month.

Reportedly, the price negotiation occurred before the Chinese Lunar New Year holiday, with Chinese clients increasing their purchasing volume before the break.

On the other hand, per TrendForce, since the fourth quarter of last year through the first quarter of this year, contract prices for DRAM products have seen continuous increases. For the mainstream product DDR4 8Gb, the contract price in January was USD 1.80.

The estimated increase for the first quarter is between 10% to 15%, and it is anticipated that there will be an additional increase of at least close to 10% by the end of the first quarter.

The report from Nikkei further addresses that 2024 is expected to enter the early stages of the PC replacement cycle, leading to increased demand for DRAM. According to sources cited in the report, besides Chinese clients, major PC manufacturers are also accepting price hikes.

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(Photo credit: Samsung)

According to TrendForce’s compilation and analysis of various industry data and the recent financial reports of major representative companies, it appears that China’s local equipment industry can cover the various stages required in semiconductor manufacturing processes (excluding lithography machines).

Overall, locally produced equipment in China shows relatively high localization rates in processes such as photoresist stripping, cleaning, and etching. In recent years, there has been significant progress in processes like CMP, thermal processing, and deposition. However, in equipment related to measurement, coating and developing, lithography, and ion implantation, the Chinese equipment manufacturers still face challenges.

As per SEMI data, the semiconductor equipment market, including wafer processing, fab facilities, and mask/reticle equipment, is projected to decline by 3.7% to USD 90.6 billion in 2023. Looking ahead, semiconductor manufacturing equipment is expected to rebound in 2024, driven by both front-end and back-end market demands. Sales are forecasted to reach a new high of USD 124 billion in 2025.

The growth in the equipment market is closely tied to the extensive expansion of foundries. It is reported that approximately 70%-80% of the capital expenditure for fab expansion is allocated to the purchase of semiconductor equipment.

According to statistics from TrendForce, China currently operates 44 fabs (25 of which are 12-inch fabs, 4 are 6-inch fabs, and 15 are 8-inch fabs/lines).

Additionally, there are 22 fabs under construction (15 of which are 12-inch fabs, and 8 are 8-inch fabs). Furthermore, companies including SMIC, Nexchip, and Silan Micro are planning to construct 10 additional fabs (9 of which are 12-inch fabs, and 1 is an 8-inch fab). Overall, China is expected to establish 32 large-scale fabs focused entirely on mature processes by the end of 2024.

Per TrendForce’s data, from 2023 to 2027, the global mature process (28nm and above) and advanced process (16nm and below) capacities are expected to maintain a ratio of approximately 7:3.

Due to policies promoting localization and subsidies, China has shown the most proactive expansion progress. It is estimated that the proportion of mature process capacity in China will increase from 29% in this year to 33% by 2027, with SMIC, Hua Hong Group, and Nexchip being the most active in expanding production.

Despite rapid development in China’s equipment industry in recent years, Chinese semiconductor manufacturers still have room to catch up compared to international giants like Applied Materials, Tokyo Electron, Lam Research, ASML, and KLA Corporation, which boast billion-dollar scales and diverse high-end product lines.

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