Yield-Maximising Process Control Through Reinforcement Learning: AI-Driven Optimisation of Semiconductor Fabrication for U.S. Manufacturing Competitiveness

Authors

  • Emily Chen Associate Professor of Computer Science, City College of New York

Keywords:

yield-maximising process control, reinforcement learning, optimisation, semiconductor fabrication, machine learning

Abstract

Today's chips are designed by aligning billions of transistors in three dimensions. Not surprisingly, the complexity is exceeding our ability to understand them. Therefore, to handle the complexity, our best solution is simply to let the chips optimize themselves. That way, overly complicated tools and models are not needed to design and manufacture the complicated chips.

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Published

30-06-2026

How to Cite

[1]
“Yield-Maximising Process Control Through Reinforcement Learning: AI-Driven Optimisation of Semiconductor Fabrication for U.S. Manufacturing Competitiveness”, Human-Computer Interaction Persp., vol. 6, no. 1, pp. 46–57, Jun. 2026, Accessed: Jun. 05, 2026. [Online]. Available: https://www.thesciencebrigade.com/hcip/article/view/819