Fast model-driven target optimization methods using machine learnings

Marco Guajardo; Ahmed Omran; Howard Clark

doi:10.1117/12.2587122

23 April 2021 Fast model-driven target optimization methods using machine learnings

Marco Guajardo, Ahmed Omran, Howard Clark

Proceedings Volume 11614, Design-Process-Technology Co-optimization XV; 116140Z (2021) https://doi.org/10.1117/12.2587122
Event: SPIE Advanced Lithography, 2021, Online Only

Abstract

The goal of this paper is to explore machine learning solutions to improve the run-time of model-based retargeting in the mask synthesis flow. The purpose of retargeting is to re-size non-lithography friendly designs so that the design geometries are shifted to a more lithography-robust design space. However, current model-based approaches can take significant run-time. As a result, this step is rarely done in production settings. Different machine learning solutions for resolution enhancement techniques (RETs) have been previously proposed. For instance, to model optical proximity correction (OPC) or inverse lithography (ILT). In this paper, we compare and expand some of these solutions. In the end, we will discuss the experimental results that can achieve a nearly 360x run-time improvement while maintaining similar accuracy to traditional retargeting techniques.

Conference Presentation

Citation Download Citation

Marco Guajardo, Ahmed Omran, and Howard Clark "Fast model-driven target optimization methods using machine learnings", Proc. SPIE 11614, Design-Process-Technology Co-optimization XV, 116140Z (23 April 2021); https://doi.org/10.1117/12.2587122

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