According to a report by Taiwan’s Commercial Times, JPMorgan’s latest analysis reveals that AI demand will remain robust in the second half of the year. Encouragingly, TSMC’s CoWoS capacity expansion progress is set to exceed expectations, with production capacity projected to reach 28,000 to 30,000 wafers per month by the end of next year.

The trajectory of CoWoS capacity expansion is anticipated to accelerate notably in the latter half of 2024. This trend isn’t limited to TSMC alone; other players outside the TSMC are also actively expanding their CoWoS-like production capabilities to meet the soaring demands of AI applications.

Gokul Hariharan, Head of Research for JPMorgan Taiwan, highlighted that industry surveys indicate strong and unabated AI demand in the latter half of the year. Shortages amounting to 20% to 30% are observed with CoWoS capacity being a key bottleneck and high-bandwidth memory (HBM) also facing supply shortages.

JPMorgan’s estimates indicate that Nvidia will account for 60% of the overall CoWoS demand in 2023. TSMC is expected to produce around 1.8 to 1.9 million sets of H100 chips, followed by significant demand from Broadcom, AWS’ Inferentia chips, and Xilinx. Looking ahead to 2024, TSMC’s continuous capacity expansion is projected to supply Nvidia with approximately 4.1 to 4.2 million sets of H100 chips.

Apart from TSMC’s proactive expansion of CoWoS capacity, Hariharan predicts that other assembly and test facilities are also accelerating their expansion of CoWoS-like capacities.

For instance, UMC is preparing to have a monthly capacity of 5,000 to 6,000 wafers for the interposer layer by the latter half of 2024. Amkor is expected to provide a certain capacity for chip-on-wafer stacking technology, and ASE Group will offer chip-on-substrate bonding capacity. However, these additional capacities might face challenges in ramping up production for the latest products like H100, potentially focusing more on older-generation products like A100 and A800.

(Photo credit: TSMC)

According to a report by Taiwan’s Economic Daily, Foxconn Group has achieved another triumph in its AI endeavors. The company has secured orders for over 50% of NVIDIA’s HGX GPU base boards, marking the first instance of such an achievement. Adding to this success, Foxconn had previously acquired an order for another NVIDIA DGX GPU base board, solidifying its pivotal role in NVIDIA’s two most crucial AI chip base board orders.

The report highlights that in terms of supply chain source, Foxconn Group stands as the exclusive provider of NVIDIA’s AI chip modules (GPU Modules). As for NVIDIA’s AI motherboards, the suppliers encompass Foxconn, Quanta, Inventec, and Super Micro.

Industry experts analyze that DGX and HGX are currently NVIDIA’s two most essential AI servers, and Foxconn Group has undertaken the monumental task of fulfilling the large order for NVIDIA’s AI chipboards through its subsidiary, Foxconn Industrial Internet (FII). Having previously secured orders for NVIDIA’s DGX base boards, Foxconn Group has now garnered additional orders from FII for the HGX base boards. This expanded supply constitutes more than half of the total, solidifying Foxconn Group’s role as a primary supplier for NVIDIA’s two critical AI chip base board orders.

Furthermore, Foxconn’s involvement doesn’t end with AI chip modules, base boards, and motherboards. The company’s engagement extends downstream to servers and server cabinets, creating a vertically integrated approach that covers the entire AI ecosystem.

(Photo credit: Nvidia)

According to the news from Commercial Times, in a recent press conference, the four major American cloud service providers (CSPs) collectively expressed their intention to expand their investment in AI application services. Simultaneously, they are continuing to enhance their cloud infrastructure. Apple has also initiated its foray into AI development, and both Intel and AMD have emphasized the robust demand for AI servers. These developments are expected to provide a significant boost to the post-market prospects of Taiwan’s AI server supply chain.

Industry insiders have highlighted the ongoing growth of the AI spillover effect, benefiting various sectors ranging from GPU modules, substrates, cooling systems, power supplies, chassis, and rails, to PCB manufacturers.

The American CSP players, including Microsoft, Google, Meta, and Amazon, which recently released their financial reports, have demonstrated growth in their cloud computing and AI-related service segments in their latest quarterly performance reports. Microsoft, Google, and Amazon are particularly competitive in the cloud services arena, and all have expressed optimistic outlooks for future operations.

The direct beneficiaries among Taiwan’s cloud data center suppliers are those in Tier 1, who are poised to reap positive effects on their average selling prices (ASP) and gross margins, driven by the strong demand for AI servers from these CSP giants in the latter half of the year.

Among them, the ODM manufacturers with over six years of collaboration with NVIDIA in multi-GPU architecture AI high-performance computing/cloud computing, including Quanta, Wistron, Wistron, Inventec, Foxconn, and Gigabyte, are expected to see operational benefits further reflected in the latter half of the year. Foxconn and Inventec are the main suppliers of GPU modules and GPU substrates, respectively, and are likely to witness noticeable shipment growth starting in the third quarter.

Furthermore, AI servers not only incorporate multiple GPU modules but also exhibit improvements in aspects such as chassis height, weight, and thermal design power (TDP) compared to standard servers. As a result, cooling solution providers like Asia Vital Components, Auras Technology, and SUNON; power supply companies such as Delta Electronics and Lite-On Technology; chassis manufacturers Chenbro; rail industry players like King Slide, and PCB/CCL manufacturers such as EMC, GCE are also poised to benefit from the increasing demand for AI servers.

(Source: https://ctee.com.tw/news/tech/915830.html)

Looking at the impact of AI server development on the PCB industry, mainstream AI servers, compared to general servers, incorporate 4 to 8 GPUs. Due to the need for high-frequency and high-speed data transmission, the number of PCB layers increases, and there’s an upgrade in the adoption of CCL grade as well. This surge in GPU integration drives the AI server PCB output value to surpass that of general servers by several times. However, this advancement also brings about higher technological barriers, presenting an opportunity for high-tech PCB manufacturers to benefit.

TrendForce’s perspective: 

• The increased value of AI server PCBs primarily comes from GPU boards.

Taking the NVIDIA DGX A100 as an example, its PCB can be divided into CPU boards, GPU boards, and accessory boards. The overall value of the PCB is about 5 to 6 times higher than that of a general server, with approximately 94% of the incremental value attributed to the GPU boards. This is mainly due to the fact that general servers typically do not include GPUs, while the NVIDIA DGX A100 is equipped with 8 GPUs.

Further analysis reveals that CPU boards, which consist of CPU boards, CPU mainboards, and functional accessory boards, make up about 20% of the overall AI server PCB value. On the other hand, GPU boards, including GPU boards, NV Switch, OAM (OCP Accelerator Module), and UBB (Unit Baseboard), account for around 79% of the total AI server PCB value. Accessory boards, composed of components such as power supplies, HDD, and cooling systems, contribute to only about 1% of the overall AI server PCB value.

• The technological barriers of AI servers are rising, leading to a decrease in the number of suppliers.

Since AI servers require multiple card interconnections with more extensive and denser wiring compared to general servers, and AI GPUs have more pins and an increased number of memory chips, GPU board assemblies may reach 20 layers or more. With the increase in the number of layers, the yield rate decreases.

Additionally, due to the demand for high-frequency and high-speed transmission, CCL materials have evolved from Low Loss grade to Ultra Low Loss grade. As the technological barriers rise, the number of manufacturers capable of entering the AI server supply chain also decreases.

Currently, the suppliers for CPU boards in AI servers include Ibiden, AT&S, Shinko, and Unimicron, while the mainboard PCB suppliers consist of GCE and Tripod. For GPU boards, Ibiden serves as the supplier, and for OAM PCBs, Unimicron and Zhending are the suppliers, with GCE, ACCL, and Tripod currently undergoing certification. The CCL suppliers include EMC. For UBB PCBs, the suppliers are GCE, WUS, and ACCL, with TUC and Panasonic being the CCL suppliers.

Regarding ABF boards, Taiwanese manufacturers have not yet obtained orders for NVIDIA AI GPUs. The main reason for this is the limited production volume of NVIDIA AI GPUs, with an estimated output of only about 1.5 million units in 2023. Additionally, Ibiden’s yield rate for ABF boards with 16 layers or more is approximately 10% to 20% higher than that of Taiwanese manufacturers. However, with TSMC’s continuous expansion of CoWoS capacity, it is expected that the production volume of NVIDIA AI GPUs will reach over 2.7 million units in 2024, and Taiwanese ABF board manufacturers are likely to gain a low single-digit percentage market share.

(Photo credit: Google)

AI Chips and High-Performance Computing (HPC) have been continuously shaking up the entire supply chain, with CoWoS packaging technology being the latest area to experience the tremors.

In the previous piece, “HBM and 2.5D Packaging: the Essential Backbone Behind AI Server,” we discovered that the leading AI chip players, Nvidia and AMD, have been dedicated users of TSMC’s CoWoS technology. Much of the groundbreaking tech used in their flagship product series – such as Nvidia’s A100 and H100, and AMD’s Instinct MI250X and MI300 – have their roots in TSMC’s CoWoS tech.

However, with AI’s exponential growth, chip demand from not just Nvidia and AMD has skyrocketed, but other giants like Google and Amazon are also catching up in the AI field, bringing an onslaught of chip demand. The surge of orders is already testing the limits of TSMC’s CoWoS capacity. While TSMC is planning to increase its production in the latter half of 2023, there’s a snag – the lead time of the packaging equipment is proving to be a bottleneck, severely curtailing the pace of this necessary capacity expansion.

Nvidia Shakes the foundation of the CoWoS Supply Chain

In these times of booming demand, maintaining a stable supply is viewed as the primary goal for chipmakers, including Nvidia. While TSMC is struggling to keep up with customer needs, other chipmakers are starting to tweak their outsourcing strategies, moving towards a more diversified supply chain model. This shift is now opening opportunities for other foundries and OSATs.

Interestingly, in this reshuffling of the supply chain, UMC (United Microelectronics Corporation) is reportedly becoming one of Nvidia’s key partners in the interposer sector for the first time, with plans for capacity expansion on the horizon.

From a technical viewpoint, interposer has always been the cornerstone of TSMC’s CoWoS process and technology progression. As the interposer area enlarges, it allows for more memory stack particles and core components to be integrated. This is crucial for increasingly complex multi-chip designs, underscoring Nvidia’s intention to support UMC as a backup resource to safeguard supply continuity.

Meanwhile, as Nvidia secures production capacity, it is observed that the two leading OSAT companies, Amkor and SPIL (as part of ASE), are establishing themselves in the Chip-on-Wafer (CoW) and Wafer-on-Substrate (WoS) processes.

The ASE Group is no stranger to the 2.5D packaging arena. It unveiled its proprietary 2.5D packaging tech as early as 2017, a technology capable of integrating core computational elements and High Bandwidth Memory (HBM) onto the silicon interposer. This approach was once utilized in AMD’s MI200 series server GPU. Also under the ASE Group umbrella, SPIL boasts unique Fan-Out Embedded Bridge (FO-EB) technology. Bypassing silicon interposers, the platform leverages silicon bridges and redistribution layers (RDL) for integration, which provides ASE another competitive edge.

Could Samsung’s Turnkey Service Break New Ground?

In the shifting landscape of the supply chain, the Samsung Device Solutions division’s turnkey service, spanning from foundry operations to Advanced Package (AVP), stands out as an emerging player that can’t be ignored.

After its 2018 split, Samsung Foundry started taking orders beyond System LSI for business stability. In 2023, the AVP department, initially serving Samsung’s memory and foundry businesses, has also expanded its reach to external clients.

Our research indicates that Samsung’s AVP division is making aggressive strides into the AI field. Currently in active talks with key customers in the U.S. and China, Samsung is positioning its foundry-to-packaging turnkey solutions and standalone advanced packaging processes as viable, mature options.

In terms of technology roadmap, Samsung has invested significantly in 2.5D packaging R&D. Mirroring TSMC, the company launched two 2.5D packaging technologies in 2021: the I-Cube4, capable of integrating four HBM stacks and one core component onto a silicon interposer, and the H-Cube, designed to extend packaging area by integrating HDI PCB beneath the ABF substrate, primarily for designs incorporating six or more HBM stack particles.

Besides, recognizing Japan’s dominance in packaging materials and technologies, Samsung recently launched a R&D center there to swiftly upscale its AVP business.

Given all these circumstances, it seems to be only a matter of time before Samsung carves out its own significant share in the AI chip market. Despite TSMC’s industry dominance and pivotal role in AI chip advancements, the rising demand for advanced packaging is set to undeniably reshape supply chain dynamics and the future of the semiconductor industry.

(Source: Nvidia)