Optical proximity correction with PID control through reinforcement learning

Taeyoung Kim; Gangmin Cho; Youngsoo Shin

doi:10.1117/12.2658484

28 April 2023 Optical proximity correction with PID control through reinforcement learning

Taeyoung Kim, Gangmin Cho, Youngsoo Shin

Proceedings Volume 12495, DTCO and Computational Patterning II; 1249524 (2023) https://doi.org/10.1117/12.2658484
Event: SPIE Advanced Lithography + Patterning, 2023, San Jose, California, United States

Conference Poster

Abstract

Model-based optical proximity correction (MB-OPC) consists of fragmentation which is decomposed into segments and iterative simulations and corrections with a feedback system. Mask bias for each segment is iteratively corrected by heuristic rule-based PID control. Although mask pattern is various, the same PID parameters are adopted. We apply reinforcement learning (RL) as a PID parameters predictor. Pattern-aware adaptive PID control through RL has the benefit of EPE convergence. RL model receives layout features and PFT values as its inputs. The reward of RL model is designed for minimizing EPE from the current mask.

Citation Download Citation

Taeyoung Kim, Gangmin Cho, and Youngsoo Shin "Optical proximity correction with PID control through reinforcement learning", Proc. SPIE 12495, DTCO and Computational Patterning II, 1249524 (28 April 2023); https://doi.org/10.1117/12.2658484

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