Effective OPC model calibration using machine learning

Yi-Hao Huang; Shin-Shing Yeh; Yung-Ching Mai; Chia-Chi Lin; Jun-Cheng Lai

doi:10.1117/12.2611337

26 May 2022 Effective OPC model calibration using machine learning

Yi-Hao Huang, Shin-Shing Yeh, Yung-Ching Mai, Chia-Chi Lin, Jun-Cheng Lai

Proceedings Volume 12052, DTCO and Computational Patterning; 120521C (2022) https://doi.org/10.1117/12.2611337
Event: SPIE Advanced Lithography + Patterning, 2022, San Jose, California, United States

Conference Poster

Abstract

Optical Proximity Correction (OPC) is one of the most important techniques in improving the resolution and pattern fidelity of optical lithography in the semiconductor industry. As the feature sizes and the process margin in nanometer technology become smaller, OPC models also need to be more accurate. However, improving the model accuracy often requires collecting more SEM data, which in turn results in a longer time for the entire flow. Therefore, an efficient method that can improve the accuracy of the OPC model while reducing the data collection time is crucial. Furthermore, machine learning has recently been applied to the lithography optimizations with some success, so it will be a useful technique to further optimize gauge sampling. This paper proposes an efficient OPC model gauge sampling flow using machine learning methods through data preprocessing, feature transformation and clustering to divide sample data into clusters and select a small amount of representative sample data from them to calibrate the model, achieving the same effect as using all data to calibrate. In order to test and verify the proposed approach, we use various types of patterns including line-space, contact hole, etc., to verify our results. By optimizing the gauge sampling flow, we can reduce the gauge requirements and modeling run time without sacrificing the accuracy and stability of the model.

Conference Presentation

Citation Download Citation

Yi-Hao Huang, Shin-Shing Yeh, Yung-Ching Mai, Chia-Chi Lin, and Jun-Cheng Lai "Effective OPC model calibration using machine learning", Proc. SPIE 12052, DTCO and Computational Patterning, 120521C (26 May 2022); https://doi.org/10.1117/12.2611337

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