Refragmentation through machine learning classifier for fast and accurate optical proximity correction

Gangmin Cho; Yonghwi Kwon; Taeyoung Kim; Youngsoo Shin

doi:10.1117/12.2614339

26 May 2022 Refragmentation through machine learning classifier for fast and accurate optical proximity correction

Gangmin Cho, Yonghwi Kwon, Taeyoung Kim, Youngsoo Shin

Proceedings Volume 12052, DTCO and Computational Patterning; 120520S (2022) https://doi.org/10.1117/12.2614339
Event: SPIE Advanced Lithography + Patterning, 2022, San Jose, California, United States

Abstract

MB-OPC contains a fragmentation step, in which each polygon edge is divided into a number of segments. Simple empirical rules dictate how the edges should be divided. Fragmentation strongly affects MB-OPC in its quality and runtime since OPC correction is performed segment by segment. Refragmentation using random forest classifier (RFC) is proposed. A segment and its surroundings are modeled using a number of features, which drive RFC to decide whether a segment should be further divided or not. It complements rule-based fragmentation in that small number of critical segments, which are not short enough and cause longer MB-OPC iterations, are quickly identified. When refragmentation is applied in standard rule-based fragmentation and MB-OPC flow, maximum EPE is reduced substantially (from 3.8nm to 2.4nm) with very marginal increase in the number of segments (7k to 7,093), yet OPC iterations are reduced (10 down to 8).

Conference Presentation

Citation Download Citation

Gangmin Cho, Yonghwi Kwon, Taeyoung Kim, and Youngsoo Shin "Refragmentation through machine learning classifier for fast and accurate optical proximity correction", Proc. SPIE 12052, DTCO and Computational Patterning, 120520S (26 May 2022); https://doi.org/10.1117/12.2614339

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