Feasibility study of machine learning modeling for full chip optical proximity correction

Yuping Cui; Jing Xue; Jing Sha; Zheng Guo Chen; Larry Zhuang; Derren Dunn; Jeffrery Shearer; Li-Jin Chen; Rich Wu; Charlie King

doi:10.1117/12.2614060

13 June 2022 Feasibility study of machine learning modeling for full chip optical proximity correction

Yuping Cui, Jing Xue, Jing Sha, Zheng Guo Chen, Larry Zhuang, Derren Dunn, Jeffrery Shearer, Li-Jin Chen, Rich Wu, Charlie King

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Proceedings Volume 12052, DTCO and Computational Patterning; 120520R (2022) https://doi.org/10.1117/12.2614060
Event: SPIE Advanced Lithography + Patterning, 2022, San Jose, California, United States

Abstract

In photolithography, we need accurate models as computation engine for optical proximity correction (OPC). Traditional OPC modeling consists of a series of components for photo mask, optical exposure system, and resist materials. These models are trained using compact model forms based on wafer-level critical dimension (CD) or edge placement error (EPE) measurements. In recent years, advancements in neural networks and machine learning have had significant advancements. In this work, we evaluated advanced neural network-based resist models on a Tensor Flow machine learning platform. This work describes resist and optical response of machine learning (ML) model through process window to achieve improved model representation of lithography process. Using ML OPC vias mask as an example, we will show improved accuracy through dose and focus process conditions and verify model accuracy with physical hardware data. Also, we will compare multiple neural network-based modeling approaches, investigate the ML models’ impacts on OPC correction and verification recipes, and dataprep runtime. The machine learning based OPC with ML model and best practice will be implemented in cloud production environment.

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Yuping Cui, Jing Xue, Jing Sha, Zheng Guo Chen, Larry Zhuang, Derren Dunn, Jeffrery Shearer, Li-Jin Chen, Rich Wu, and Charlie King "Feasibility study of machine learning modeling for full chip optical proximity correction", Proc. SPIE 12052, DTCO and Computational Patterning, 120520R (13 June 2022); https://doi.org/10.1117/12.2614060

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KEYWORDS

Optical proximity correction

Machine learning

Data modeling

Neural networks

Photomasks

Process modeling

Lithography

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