Astute Analytica predicts that the global AI in semiconductor market will expand from US$ 71.91 billion in 2024 to US$ 321.66 billion by 2033, driven by transformer inference compute demand, innovations such as TSMC’s chip-on-wafer-on-substrate flow and Intel’s Foveros Direct, and strong hyperscaler and edge AI growth.
Key points
Market value escalates from US$ 71.91 billion (2024) to US$ 321.66 billion (2033) at 18.11% CAGR.
Advanced packaging: TSMC’s CoWoS and Intel’s Foveros Direct hybrid bonding drive performance and power gains.
Foundry expansions: TSMC, Samsung and Intel add over four million sub-5 nm wafer starts annually for AI demand.
Q&A
What factors drive the AI in semiconductor market?
What is chip-on-wafer-on-substrate (CoWoS)?
How does Intel’s Foveros Direct differ from other packaging methods?
Why are foundry capacity expansions critical for AI?
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Academy
Introduction to AI Accelerators and Semiconductor Packaging
AI accelerators are specialized processors designed to run machine-learning models efficiently, offering higher performance and energy savings compared to general-purpose CPUs. As artificial intelligence workloads grow in complexity—particularly for transformer-based inference in data centers and edge devices—traditional scaling approaches hit power and density limits. To keep pace, the semiconductor industry leverages advanced node-level optimizations, heterogenous integration, and novel packaging techniques.
Key Packaging Technologies
- 2.5D Packaging (CoWoS): Chip-on-Wafer-on-Substrate by TSMC stacks compute and memory dies on a silicon interposer, shortening inter-die traces and boosting bandwidth.
- 3D Hybrid Bonding (Foveros): Intel’s direct copper-copper bonding connects logic and memory tiles at micron pitches, increasing density and reducing resistive losses.
- Fan-Out Panel-Level Packaging (FOPLP): Panel-scale substrates allow larger passive areas and multi-die integration without yield penalties, useful for multi-chip edge modules.
Chiplets and Modular Scaling
Rather than monolithic SoCs, architects now assemble processors from pre-verified chiplets—compute cores, memory caches, I/O hubs—linked via standards like UCIe. This approach reduces NRE costs, accelerates time-to-market, and fosters an ecosystem where different vendors supply interoperable IP blocks. Chiplet modules can mix leading-edge logic with mature-node memory optimized for specific AI tasks.
Foundry Roadmaps and Node Choices
Foundries such as TSMC, Samsung, and Intel optimize roadmaps around AI demand. Sub-5 nm nodes deploy power gating at block level to maximize tops-per-watt. Specialized nodes (e.g., 12 nm matrix engines) serve industrial vision and automotive autonomy with long-life requirements. High-NA EUV scanners from ASML enable smaller half-pitches and maintain technology leadership in EUV lithography.
Edge AI and Power-Efficiency Trade-Offs
Edge AI devices—from smartphones to automotive SoCs—must balance compute power with battery and thermal constraints. Techniques include mixed-precision MAC arrays, near-compute SRAM caches, and on-package Die-Stacked HBM for localized high-bandwidth memory. Innovations in power-delivery networks and vapor-chamber cooling are co-designed with die placement to optimize form-factor performance.
Future Trends
- In-Memory Compute: Embedding simple arithmetic units in DRAM stacks to perform sparse matrix multiplications and reduce data movement energy.
- AI-Driven EDA Tools: Machine-learning algorithms automate placement, routing, and verification to compress tape-out cycles from years to months.
- Geographic Diversification: New hubs in the US, Europe, Middle East, and India focus on localized specialization and supply-chain resilience.
Understanding these technologies and their interplay is essential for anticipating the next wave of AI system performance improvements, from hyperscale data centers to always-on edge devices.