Implementing Machine Learning OPC on product layouts

Hesham Abdelghany; Kevin Hooker; Marco Guajardo; Chia-Chun Lu

doi:10.1117/12.2552398

26 March 2020 Implementing Machine Learning OPC on product layouts

Hesham Abdelghany, Kevin Hooker, Marco Guajardo, Chia-Chun Lu

Proceedings Volume 11328, Design-Process-Technology Co-optimization for Manufacturability XIV; 1132805 (2020) https://doi.org/10.1117/12.2552398
Event: SPIE Advanced Lithography, 2020, San Jose, California, United States

Abstract

As feature resolution and process variations continue to shrink for new nodes of both DUV and EUV lithography, the density and number of devices on advanced semiconductor masks continue to increase rapidly. These advances cause significantly increased pressure on the accuracy and efficiency of OPC and assist feature (AF) optimization methods for each subsequent process technology. Several publications and industry presentations have discussed the use of neural networks or other machine learning techniques to provide improvements in efficiency for OPC main feature optimization or AF placement. In this paper, we present results of a method for using machine-learning to predict OPC mask segment displacements. We will review several detailed examples showing the accuracy and overall OPC TAT benefits of our method for advanced node manufacturing test cases. We will also discuss the experiments testing the amount and diversity of training data required to achieve true production level OPC stability.

Conference Presentation

Citation Download Citation

Hesham Abdelghany, Kevin Hooker, Marco Guajardo, and Chia-Chun Lu "Implementing Machine Learning OPC on product layouts", Proc. SPIE 11328, Design-Process-Technology Co-optimization for Manufacturability XIV, 1132805 (26 March 2020); https://doi.org/10.1117/12.2552398

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