Machine learning OPC with generative adversarial networks

Weilun Ciou; Tony Hu; Yi-Yien Tsai; Chung-Te Hsuan; Elvis Yang; Ta-Hung Yang; Kuang-Chao Chen

doi:10.1117/12.2606715

26 May 2022 Machine learning OPC with generative adversarial networks

Weilun Ciou, Tony Hu, Yi-Yien Tsai, Chung-Te Hsuan, Elvis Yang, Ta-Hung Yang, Kuang-Chao Chen

Proceedings Volume 12052, DTCO and Computational Patterning; 120520Z (2022) https://doi.org/10.1117/12.2606715
Event: SPIE Advanced Lithography + Patterning, 2022, San Jose, California, United States

Abstract

With the algorithmic breakthroughs in machine learning, increasingly more applications have been developed for computational lithography. In this paper, a pixelated mask synthesis method including After Development Inspection (ADI) contour and mask feature generation, was proposed by utilizing deep learning technique. Two Generative Adversarial Networks (GANs) were constructed, the first network was for mask to contour prediction, and the consecutive network was to perform design to mask correction. A pix2pix model was first trained to learn the correspondences between mask image and paired ADI contour image collected on wafer, thus the capability of printing prediction can be established. The well trained mask-to-contour model was then implemented as the simulator component of machine learning mask correction (ML-OPC) framework. Next, another unsupervised GAN formed the front-end of ML-OPC framework to synthesize mask patterns from any given design layout. Generated mask patterns were eventually optimized through minimizing pixel difference between design target and corresponding contour generated by mask-to-contour model. The experimental results demonstrated that our ML-OPC framework can mimic conventional OPC model to produce exquisite mask patterns and contours.

Conference Presentation

Citation Download Citation

Weilun Ciou, Tony Hu, Yi-Yien Tsai, Chung-Te Hsuan, Elvis Yang, Ta-Hung Yang, and Kuang-Chao Chen "Machine learning OPC with generative adversarial networks", Proc. SPIE 12052, DTCO and Computational Patterning, 120520Z (26 May 2022); https://doi.org/10.1117/12.2606715

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