AI, fuelled by advanced chips, set to power their manufacture: industry veteran - South China Morning Post

This flywheel is not "AI helping make chips"—it is a generational shift in manufacturing paradigm. For three decades, yield gains depended on engineer intuition and SPC. Now, end-to-end neural networks compress EUV overlay correction, defect classification, and recipe optimization into a single inference loop. The cascade is structural: upstream EDA must be rebuilt as AI-native; downstream CoWoS bonding precision demands data-driven calibration. The real compliance risk is not the silicon but the manufacturing data. EUV recipe parameters, defect atlases, yield models—these are the true crown jewels. If export controls extend to "AI-assisted manufacturing toolchains," the damage will be more lethal than equipment restrictions, severing the feedback loop of process iteration entirely. In competitive positioning, TSMC's AI-native fab strategy already holds an 18-month window. If Samsung and Intel fail to complete production-line AI restructuring by 2026, the yield gap will widen from 3-5 percentage points into a structural chasm. ASML and Applied Materials are pivoting from selling equipment to selling process intelligence. The 12-24 month tail effect: as node scaling hits diminishing returns, AI-assisted manufacturing becomes the core lever for equivalent advanced process. Mid-tier foundries unable to fund AI infrastructure will be accelerated out of the market. This is not efficiency optimization—it is a redefinition of the survival threshold.