Reassessing Exposure to Global Chip Equipment Makers Amid China's Booming Semiconductor Self-Reliance Drive

The global semiconductor industry is at a crossroads. China's aggressive push for self-reliance in chip production—driven by state-backed policies and a $200 billion investment in domestic semiconductor infrastructure—has triggered a seismic shift in supply chain dynamics. While this surge in legacy-node (28nm and above) manufacturing capacity is hailed as a strategic win for China, it poses a latent threat to global chip equipment makers like ASML, Lam Research, and Tokyo Electron. As oversupply risks materialize and demand for advanced tools dilutes, investors must recalibrate their portfolios to prioritize firms insulated from China's self-reliance-driven volatility.

The Oversupply Conundrum: China's Legacy-Node Expansion

China's mature-node semiconductor production capacity is projected to grow by 5% annually through 2025, reaching 14 million wafers per month (wpm) in legacy nodes (50nm and above). This expansion is fueled by a combination of domestic demand for automotive, IoT, and industrial chips, as well as government mandates to reduce reliance on foreign semiconductors. However, the rapid buildout of 8-inch and 12-inch wafer fabs—many of which are still in the planning phase—risks creating a mismatch between supply and demand.

By 2025, China's market share in mature-node production is expected to rise to 30–35%, up from 27% in 2023. Yet, utilization rates for these facilities remain a critical wildcard. While 70–90% utilization is typical for high-reliability applications (e.g., automotive), consumer electronics and industrial sectors face more volatile demand. If utilization rates dip below 60% for extended periods, the industry could face a classic oversupply crisis, with Chinese foundries undercutting global competitors on price.

Strategic Risks for Equipment Makers: ASML, Lam, and Tokyo Electron

The revenue exposure of leading equipment makers to China's legacy-node expansion is both a blessing and a curse.

• ASML: The Dutch lithography giant's DUV (Deep Ultraviolet) systems, used for 28nm and above, accounted for 29% of its 2023 revenue. However, U.S. and Dutch export controls—tightened in late 2024—have curtailed DUV shipments to China. ASML now projects China's contribution to normalize to 20% of total revenue by 2025, a 48% decline from 2023 levels.
• Lam Research: China contributed 31% of its Q1 2025 revenue ($1.46 billion), driven by investments in mature-node wafer fabrication. However, the company warns that further export restrictions could erode long-term demand for its etch and deposition tools.
• Tokyo Electron: The Japanese firm's China revenue surged to 34% in 2024, but this figure has since dipped to 30% as it diversifies its geographic footprint.

These trends signal a valuation correction for equipment makers. While short-term demand for DUV and mature-node tools remains robust, the long-term outlook is clouded by geopolitical tensions and the risk of China's self-reliance drive creating a “shadow market” that bypasses Western suppliers.

Reallocation Strategy: AI-Driven Design and Materials Players

To mitigate exposure to China's legacy-node oversupply, investors should pivot toward firms at the forefront of AI-driven chip design and materials innovation. These companies are less reliant on mature-node production and are positioned to capitalize on the next phase of the semiconductor revolution.

1. AI Chip Design Firms with Low China Exposure
2. Cerebras Systems: Specializes in wafer-scale AI processors, bypassing traditional node-based manufacturing. Its systems are tailored for high-performance computing (HPC) and are less sensitive to legacy-node oversupply.
3. Graphcore: Focuses on intelligent processing units (IPUs) optimized for machine learning workloads. Its design-centric approach reduces dependency on foundry capacity expansions.

4. SambaNova: Develops AI accelerators with proprietary architectures, avoiding reliance on China's mature-node ecosystem.

5. Materials Players in Critical AI Supply Chains

6. HBM (High-Bandwidth Memory) Suppliers: Companies like SK Hynix and Micron Technology are pivotal for AI chips, which require massive memory bandwidth. China's inability to produce HBM domestically creates a long-term demand tailwind.

7. EDA (Electronic Design Automation) Tools: Synopsys and Cadence Design Systems dominate the EDA market, which is critical for AI chip design. While China is developing its own EDA tools, these firms remain indispensable for advanced AI architectures.

8. Advanced Packaging and Interconnects

9. Amkor Technology: A leader in packaging for AI GPUs and CPUs, Amkor benefits from the shift toward heterogeneous integration, which is less impacted by legacy-node oversupply.
10. Xilinx (AMD): Its Versal ACAPs (Adaptive Compute Acceleration Platforms) are designed for AI inference and edge computing, leveraging advanced packaging to avoid reliance on mature-node foundries.

The Road Ahead: Balancing Risk and Opportunity

China's self-reliance drive is reshaping the semiconductor landscape, but it also creates opportunities for firms that can navigate the transition. Equipment makers like ASML and Lam Research remain essential for legacy-node production, but their valuations are increasingly tied to geopolitical outcomes. In contrast, AI-driven design and materials players offer a more insulated path to growth, leveraging innovation rather than capacity expansion.

Investors should consider a strategic reallocation: reducing exposure to equipment makers with high China legacy-node exposure and increasing allocations to AI-focused firms and materials suppliers. This approach not only mitigates oversupply risks but also aligns with the long-term trajectory of the semiconductor industry—where AI, HPC, and advanced packaging will dominate the next decade.

In the end, the key to navigating this volatile market lies in foresight. As China's semiconductor ambitions collide with global supply chain realities, the winners will be those who adapt—not just to the present, but to the future of AI-driven innovation.