CoWoS Advanced Packaging

The CoWoS Advanced Packaging bottleneck refers to capacity constraints in TSMC's Chip-on-Wafer-on-Substrate (CoWoS) technology, a specialized packaging method essential for integrating high-density logic chips with high-bandwidth memory (HBM) in high-end AI accelerators. This process enables the heterogeneous integration required for next-generation architectures, such as those demanding extreme compute and memory bandwidth. It matters because CoWoS throughput directly limits the production volume of advanced AI GPUs, creating a supply-demand imbalance that affects the scalability of AI infrastructure deployments. Unlike front-end wafer fabrication, where capacity has expanded, packaging remains the gating factor, as it handles the final assembly of complex multi-die systems.

Currently, despite TSMC's substantial capacity expansions through 2024—including the introduction of CoWoS-L for larger interposers—the shift to architectures like NVIDIA's Blackwell platform (utilizing CoWoS-L) and AMD's Instinct MI325X sustains a persistent gap. Recent announcements at CES 2026, including NVIDIA's NVL72 Rubin rack-scale AI system and AMD's Instinct MI500-series GPUs promising significant performance uplifts by 2027, underscore escalating demand for CoWoS-equipped chips. These developments, alongside ongoing product roadmaps from major players, indicate that packaging constraints continue to govern availability, even as wafer fabrication scales.

Key players include TSMC as the primary source of CoWoS capacity, with affected parties encompassing NVIDIA, AMD, and Broadcom, whose AI accelerator designs rely heavily on this technology. These companies face production delays and allocation challenges, impacting their ability to meet data center demands. The bottleneck spans the Foundry & Logic and Packaging & Test segments of the supply chain.

Looking ahead, TSMC's continued investments in CoWoS capacity, including advanced nodes like CoWoS-R, suggest gradual improvements, though demand from evolving AI architectures will likely maintain pressure through 2026 and into 2027. Resolutions depend on execution of expansion plans and potential diversification efforts, with visibility limited by ongoing market dynamics.