Open-source differentiable lithography imaging framework

Guojin Chen; Hao Geng; Bei Yu; David Z. Pan

doi:10.1117/12.3009980

10 April 2024 Open-source differentiable lithography imaging framework

Guojin Chen, Hao Geng, Bei Yu, David Z. Pan

Proceedings Volume 12954, DTCO and Computational Patterning III; 129540J (2024) https://doi.org/10.1117/12.3009980
Event: SPIE Advanced Lithography + Patterning, 2024, San Jose, California, United States

Abstract

The rapid evolution of the electronics industry, driven by Moore’s law and the proliferation of integrated circuits, has led to significant advancements in modern society, including the Internet, wireless communication, and artificial intelligence (AI). Central to this progress is optical lithography, a critical technology in semiconductor manufacturing that accounts for approximately 30% to 40% of production costs. As semiconductor nodes shrink and transistor numbers increase, optical lithography becomes increasingly vital in current integrated circuit (IC) fabrication technology. This paper introduces an open-source differentiable lithography imaging framework that leverages the principles of differentiable programming and the computational power of GPUs to enhance the precision of lithography modeling and simplify the optimization of resolution enhancement techniques (RETs). The framework models the core components of lithography as differentiable segments, allowing for the implementation of standard scalar imaging models, including the Abbe and Hopkins models, as well as their approximation models. The paper introduces a computational lithography framework that optimizes semiconductor manufacturing processes using advanced computational techniques and differentiable programming. It compares imaging models and provides tools for enhancing resolution, demonstrating improved semiconductor patterning performance. The open-sourced framework represents a significant advancement in lithography technology, facilitating collaboration in the field. The source code is available at https://github.com/TorchOPC/TorchLitho.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Guojin Chen, Hao Geng, Bei Yu, and David Z. Pan "Open-source differentiable lithography imaging framework", Proc. SPIE 12954, DTCO and Computational Patterning III, 129540J (10 April 2024); https://doi.org/10.1117/12.3009980

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