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As memory giants realign capacity toward high-value advanced processes, mature node capacity faces severe compression. This structural shift has caused severe shortages in legacy chips, compelling industrial and automotive customers to adopt SLC as an alternative amid critical MLC deficits. Under the twin pressures of demand spillover and supply disruptions, SLC prices are expected to witness an explosive, structural rally in 2H, shifting the market dynamics from shipment-driven to price-driven.
As AI chips require increasingly stable power delivery, Si‑Cap (silicon capacitors) and their embedded/integrated packaging have become an important technological development direction, and key companies have successively announced silicon‑capacitor‑related plans, including SEMCO, which on April 14th 2026 announced plans to expand AI packaging production lines in Vietnam to produce Si‑Cap embedded substrates, and then in May revealed it had secured a long‑term contract of about US$1 billion from a major North American customer, and Analog Devices, which on May 19th 2026 announced a cash acquisition of the US Si‑Cap company Empower for US$1.5 billion in order to enter the AI supply chain.
This report mainly provides an in‑depth analysis of (1) the technical background of Si‑Cap, (2) the technological differences among major Si‑Cap vendors (Murata, SEMCO, AP Memory, Empower, TSMC, Samsung, Intel), and (3) the embedded/integrated Si‑Cap packaging requirements in the AI era, with the aim of explaining the technical principles of Si‑Cap, the reasons for growing demand, and the competitive landscape among existing vendors.
The surge in demand for AI chips and high-speed servers is driving the demand for high-end fiberglass cloth. However, constrained by technical barriers, equipment bottlenecks, and yield challenges, the release of new capacity remains slow, resulting in a persistent supply-demand imbalance for critical materials. Consequently, high-end products are expected to remain in short supply in the short term.
Global semiconductor supply chains are shifting from efficiency to resilience amid escalating export controls, driving manufacturing diversification. Governments race to subsidize domestic ecosystems, while China pursues dual-track strategies; material self-sufficiency remains a lasting challenge.
Driven by AI infrastructure, major DRAM makers shift capacity to advanced nodes, tightening mature processes. To secure supply and control costs, brands downgrade to older generation consumer DRAM, shifting shortages from DDR4 down to DDR2 and boosting prices. Taiwanese suppliers pivot strategies accordingly, reflecting a persistent global structural DRAM shortage.
In January 2026, NVIDIA introduced the CMX Context Memory Storage Platform, managed by the BlueField‑4 DPU, to extend the memory hierarchy between local SSD and shared storage and address the massive KV cache storage demands of the AI inference era. In addition, NVIDIA and Arm have successively launched CPU racks to meet the CPU requirements of agentic AI, creating an incremental market for CPU RAM.
This report provides an in‑depth analysis of: (1) memory demand in AI inference; (2) SSD POD demand driven by KV cache offloading; and (3) CPU RAM demand driven by agentic AI. The goal is to explain why memory capacity needs are expanding in the AI inference era, review current solutions, and outline the future structure of emerging memory demand.
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.
