CoWoS Advanced Packaging

CoWoS Advanced Packaging refers to TSMC's Chip-on-Wafer-on-Substrate technology, a sophisticated 2.5D/3D integration method critical for high-performance computing applications, particularly AI accelerators. This bottleneck arises because CoWoS enables the dense integration of multiple chips, such as GPUs and high-bandwidth memory (HBM), onto a single package, delivering the interconnect density and thermal efficiency required for next-generation AI workloads. It matters due to its role as the primary constraint in the supply chain for high-end semiconductors; while front-end wafer fabrication capacity has scaled, packaging throughput remains the limiting factor, directly impacting the availability of AI chips amid surging demand from data centers and hyperscalers.

The current state reflects persistent supply-demand imbalances despite TSMC's capacity expansions through 2024, including the introduction of CoWoS-L for larger packages suited to architectures like NVIDIA's Blackwell and AMD's Instinct MI325X. Recent developments, such as NVIDIA's announcements of rack-scale systems like NVL72 Rubin and DLSS 4.5 at CES 2026, alongside AMD's MI500-series roadmap promising significant performance uplifts, underscore escalating requirements for advanced packaging. Geopolitical factors, including US export controls prompting upfront payments for NVIDIA H200 orders to China and the rise of domestic alternatives from Baidu and Huawei, further complicate global supply dynamics without alleviating the core packaging constraint.

Key players include TSMC as the primary source of CoWoS capacity, with affected parties encompassing NVIDIA, AMD, and Broadcom, all reliant on this technology for their AI and high-performance logic products. These companies face production delays as packaging availability governs overall output, spanning the Foundry & Logic and Packaging & Test segments.

Looking ahead, TSMC continues to ramp CoWoS iterations, but the transition to newer architectures will likely sustain the gap through at least 2026, with throughput improvements expected gradually. Resolution depends on sustained capital investment and process optimizations, though demand from evolving AI systems suggests measured progress rather than immediate relief.