Beyond the EPE Budget: Why Chips Now Advance on AI, Data, and Trust
Semiconductor Engineering's latest analysis argues the next era of chip scaling depends on better models, richer measurement data, explainable AI, and cross-industry collaboration — and the shift reaches all the way to Penang.
Semiconductor Engineering has published an analysis arguing that the semiconductor industry can no longer rely on tightening the "EPE budget" — the tiny margin of placement error a chip can tolerate — and must instead rebuild computational lithography around four things: higher-fidelity models, richer metrology data, explainable AI, and cross-industry collaboration. In plain terms, chipmaking is shifting from a hardware-limited business to a software-and-data-limited one, where the winners are those who measure more, model better, and can trust what their AI recommends. For Malaysia, whose Penang–Kulim corridor handles a large share of global chip assembly, test, and equipment, the signal is direct: process data and AI engineering talent are becoming the scarce assets.
AI Summary
Semiconductor Engineering has published an analysis arguing that the semiconductor industry can no longer rely on tightening the "EPE budget" — the tiny margin of placement error a chip can tolerate — and must instead rebuild computational lithography around four things: higher-fidelity models, richer metrology data, explainable AI, and cross-industry collaboration. In plain terms, chipmaking is shifting from a hardware-limited business to a software-and-data-limited one, where the winners are those who measure more, model better, and can trust what their AI recommends. For Malaysia, whose Penang–Kulim corridor handles a large share of global chip assembly, test, and equipment, the signal is direct: process data and AI engineering talent are becoming the scarce assets.
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