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10 April 2024 Test pattern generation by conditional generative model labeled by image parameters
Tianshuai Qi, Peng Xu, Jingwei Xu, Song Sun, Juan Wei, Guangyu Sun, Zhao Liu, Qingchen Cao, Jiangliu Shi, Zhenjie Yao, Xijin Zhao, Chun Zhang
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Proceedings Volume 12954, DTCO and Computational Patterning III; 129541C (2024) https://doi.org/10.1117/12.3010111
Event: SPIE Advanced Lithography + Patterning, 2024, San Jose, California, United States
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Abstract
Test pattern selection plays a vital role in the model calibration in the optical proximity correction process. Traditional OPC resist models mainly use the image parameters such as the minimum intensity, the maximum intensity, the slope of intensity along the cut lines crossing the gauge points as their input parameters to calculate the resist contour position. To guarantee the accuracy of the resist model over the whole design layout, it is important that the image parameter space of the test patterns used to calibrate the OPC model covers the image parameter space of the original design layout. We present a method to generate test patterns based on the provided image parameters. The method is based on the adversarial neural network. With this method, we can prepare the test patterns with the desired image parameter coverage.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Tianshuai Qi, Peng Xu, Jingwei Xu, Song Sun, Juan Wei, Guangyu Sun, Zhao Liu, Qingchen Cao, Jiangliu Shi, Zhenjie Yao, Xijin Zhao, and Chun Zhang "Test pattern generation by conditional generative model labeled by image parameters", Proc. SPIE 12954, DTCO and Computational Patterning III, 129541C (10 April 2024); https://doi.org/10.1117/12.3010111
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KEYWORDS
Optical proximity correction
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