Hotspot pattern synthesis using generative network with hotspot probability model

Byungho Choi; Gangmin Cho; Yonghwi Kwon; Youngsoo Shin

doi:10.1117/12.2614346

26 May 2022 Hotspot pattern synthesis using generative network with hotspot probability model

Byungho Choi, Gangmin Cho, Yonghwi Kwon, Youngsoo Shin

Proceedings Volume 12052, DTCO and Computational Patterning; 120520M (2022) https://doi.org/10.1117/12.2614346
Event: SPIE Advanced Lithography + Patterning, 2022, San Jose, California, United States

Abstract

Diversity of known hotspot patterns is important for hotspot detection and correction. Deliberate synthesis of hotspot patterns can improve such diversity. Machine learning generative network is a popular tool for image synthesis, but it should be trained with known hotspots anyway. We propose U-net hotspot generator. A key is to train the generator with CNN hotspot probability model, i.e. the generator is trained such that output is a variant of input image with high hotspot probability. The method allows any patterns, even coldspots, to be provided to the generator, which then yields their hotspot variants. Efficiency of hotspot generator is demonstrated through experiments.

Conference Presentation

Citation Download Citation

Byungho Choi, Gangmin Cho, Yonghwi Kwon, and Youngsoo Shin "Hotspot pattern synthesis using generative network with hotspot probability model", Proc. SPIE 12052, DTCO and Computational Patterning, 120520M (26 May 2022); https://doi.org/10.1117/12.2614346

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