The need for power integrity in AI servers is extending from the board level directly into the package. This shift is driving a transition in capacitor technology, moving beyond a sole reliance on traditional multi-layer ceramic capacitors (MLCCs) toward a layered, complementary approach utilizing both silicon capacitors and MLCCs. While MLCCs remain the primary components for system-level decoupling, filtering, and voltage regulation across PCBs, VRMs, power shelves, and power modules, silicon capacitors offer distinct localized advantages. Their thin profile, low equivalent series inductance (ESL), excellent high-frequency characteristics, and stable capacitance under DC bias and temperature fluctuations make them ideal for near-die decoupling around GPUs, ASICs, HBMs, and within advanced packages. As AI accelerators increasingly adopt chiplets, HBM stacking, and high-power packaging, silicon capacitors are poised to become vital complementary components for package-level power integrity in AI and HPC applications.
Beyond full-rack solutions, NVIDIA is also actively promoting its standalone Vera CPU to capture the AI inference market. However, given the LPDRAM supply bottleneck, NVIDIA has decided to reduce the memory module capacity on its next-generation platforms to ensure shipment volumes align with market share targets. As AI applications continue to expand, the AI server ecosystem has the potential to become the single largest outlet for global LPDRAM, surpassing smartphone applications.
Capital shifts to high-performance chips have squeezed mature processes, triggering structural shortages in high-reliability memory. As value revaluation outpaces volume growth, market revenue surges. The industry enters a price plateau, shifting competition to supply stability and long-term contract management.
On February 16th 2026, MediaTek CEO Rick Tsai stated in his ISSCC 2026 keynote that the basic unit for measuring AI compute efficiency will no longer be just the AI chip, but the entire AI system, including interconnect architecture, power, and cooling. On May 4th 2026, GUC likewise announced a strategic technology partnership with Wiwynn to jointly develop rack/pod‑scale solutions, indicating that competition among AI chip design houses is expanding from the chip scale to the rack/pod scale.
This report therefore focuses on an in‑depth analysis of: (1) NVIDIA’s AI rack/pod‑scale strategy, (2) the rack/pod‑scale AI deployments of major CSPs, and (3) rack/pod‑scale solutions from ASIC design and service providers. The goal is to clarify each vendor’s current AI rack/pod‑scale layout and future trends, and to examine the competitive strategies of Taiwanese ASIC design and service providers.
Since 2H25, accelerated AI infrastructure buildouts by CSPs have surged HBM demand and crowded out conventional DRAM capacity, pushing the broader DRAM market into shortage. Constrained by annual pricing mechanisms, HBM per-wafer output value and margins fell below DDR5 RDIMM in 1Q26. As 2027 HBM4 supply negotiations launch in 2Q26, TrendForce expects suppliers to push through substantial contract price hikes, reflecting acute supply-demand imbalance and rising next-generation manufacturing costs.
This report first explains why HBM plays a key role in the evolution of LLM and AIGC applications. It then examines the HBM upgrade roadmap and explores how heterogeneous memory integration strategies can expand capacity and increase bandwidth to meet future AIGC demand.
As technical iteration narrows the capability gap between models, Chinese open-source models are expanding their global presence by compensating weaker hardware with software optimization and scale. Under compute constraints, Chinese models can still maintain solid performance and a strong price–performance advantage, based on a fundamental reshaping of “model design and system engineering.” The launch of DeepSeek‑V4, the first Chinese model to achieve domestic substitution in the inference stage, has become a flagship example, and is driving China’s AI industry to further deepen an integrated “AI hardware–software co‑design” development path.
On April 23, 2026, TSMC unveiled at the 2026 North America Technology Symposium its Compact Universal Photonic Engine (COUPE) platform built on Co-Packaged Optics (CPO) technology, with mass production targeted for the second half of 2026. However, CPO testing is the critical technology that must be mastered before COUPE can move into a mass production phase. As a result, not only international giants such as FormFactor, ficonTEC, and Keysight, but also Taiwanese players including MPI, Chroma ATE, Hon Prec, Enlitech, and MSS have all rolled out corresponding solutions, hoping to capture share in this new incremental market.
This report mainly provides an in-depth analysis of: (1) demand drivers for CPO test equipment; (2) technical bottlenecks and testing challenges of CPO; (3) CPO test insertion levels; and (4) Taiwan-based CPO test equipment suppliers. The goal is to map out the growth drivers and technology development of CPO testing, and to take stock of the current state of Taiwan's CPO test equipment supply chain.
As U.S. export controls extend from chips to AI infrastructure, the traditional optical interconnect industry is undergoing supply chain restructuring. While optical modules are not subject to blanket bans, they are now governed by system‑level controls. By leveraging its semiconductor ecosystem and investing in SiPh, CPO, advanced packaging and testing capabilities, Taiwan can capture North American supply‑chain de‑risking demand and reposition itself as a strategic AI partner.
The semiconductor industry is undergoing a structural shift in 2026, with competition moving beyond process node scaling toward system-level architecture. On the technology front, 2nm gate-all-around (GAA) structures offer ultra-low leakage through full gate control, addressing key challenges in edge AI. Meanwhile, advanced packaging technologies such as CoWoS have evolved into a strategic focal point for defining overall system performance.
On the application front, the need for inference is driving the rapid growth of ASICs, as companies like Amazon Web Services, Google, Meta, and Alibaba develop their own architectures and co-optimized software and hardware to regain control over compute resources. At the same time, AI-powered EDA tools are removing productivity hurdles and promoting design democratization, opening new avenues for IC innovation and encouraging wider ASIC adoption.
