Machine learning assisted effective OPC verification hotspot capture

Lianghong Yin; Marko Chew; Shumay Shang; Le Hong; Fan Jiang; Ilhami Torunoglu

doi:10.1117/12.2687752

21 November 2023 Machine learning assisted effective OPC verification hotspot capture

Lianghong Yin, Marko Chew, Shumay Shang, Le Hong, Fan Jiang, Ilhami Torunoglu

Proceedings Volume 12751, Photomask Technology 2023; 127510W (2023) https://doi.org/10.1117/12.2687752
Event: SPIE Photomask Technology + EUV Lithography, 2023, Monterey, California, United States

Abstract

In this paper, we present our innovative work of using Siemens EDA Calibre® Machine Learning (ML) assisted Optical and Process Correction (OPC) verification tool to effectively capture all kinds of hotspots using one single constraint across the whole layout for each failing mechanism, for example one constraint for bridging failing mechanism, one constraint for pinching failing mechanism, etc. The pattern differentiation is accomplished by ML classifier. The output data volume is controlled by using classification limiting function instead of tuned constraints. This work significantly improves the effectiveness of capturing and not missing real hotspots yet simplifies the OPC verification recipe setup and engineering workload. The unique hotspots count on full chip using this new strategy can be at thousand level. This makes the Machine Learning assisted hotspot capture new strategy practical to prepare hotspot monitoring points for wafer verification, for example SEM inspection.

Conference Presentation

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Lianghong Yin, Marko Chew, Shumay Shang, Le Hong, Fan Jiang, and Ilhami Torunoglu "Machine learning assisted effective OPC verification hotspot capture", Proc. SPIE 12751, Photomask Technology 2023, 127510W (21 November 2023); https://doi.org/10.1117/12.2687752

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