Fast OPC repair flow based on machine learning

Bailing Shi; Rock Deng; Zhongli Shu; Yu Zhu; Yuanying Tu; Sun Chen

doi:10.1117/12.2552731

23 March 2020 Fast OPC repair flow based on machine learning

Bailing Shi, Rock Deng, Zhongli Shu, Yu Zhu, Yuanying Tu, Sun Chen

Proceedings Volume 11328, Design-Process-Technology Co-optimization for Manufacturability XIV; 113281B (2020) https://doi.org/10.1117/12.2552731
Event: SPIE Advanced Lithography, 2020, San Jose, California, United States

Abstract

In urgent post tape-out, runtime is a big challenging for backend layers’ OPC (Optical Proximity Correction). Sometimes there is no extra time for OPC to clean all the ORC (Optical Rule Check) hotspots with recipe tuning. So the repair flow is the good choice, the repair flow is costly final step, especially for Metal layer’s. In some challenging case, there will be thousands or millions hotspots which can’t pass various ORC criteria need to be into repair flow, thus not only makes our system overloading and the runtime is unacceptable. There are many applications for Machine Learning (ML) in IC field, such as ML-OPC, ML-Hotspot detection and ML-SRAF etc. And Calibre ML-OPC has powerful functionality which fits the requirement of repair flow very well. In this paper, we will introduce how to use Calibre ML-OPC to reduce the most of the defects to speed up the repair process and demonstrate the benefit comparing to the traditional repair flow.

Citation Download Citation

Bailing Shi, Rock Deng, Zhongli Shu, Yu Zhu, Yuanying Tu, and Sun Chen "Fast OPC repair flow based on machine learning", Proc. SPIE 11328, Design-Process-Technology Co-optimization for Manufacturability XIV, 113281B (23 March 2020); https://doi.org/10.1117/12.2552731

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