OPC accuracy improvement through deep-learning based etch model

Weina Shi; Liang Zhu; Yuqian Chen; Jiao Huang; Jinze Wang; Qian Xie; Bilun Zhang; Yunfei Xi; Wenhao Pan; Yuanxia Zheng; Yongfa Fan; Jin Cheng; Yu Zhao; Leiwu Zheng

doi:10.1117/12.2613926

25 May 2022 OPC accuracy improvement through deep-learning based etch model

Weina Shi, Liang Zhu, Yuqian Chen, Jiao Huang, Jinze Wang, Qian Xie, Bilun Zhang, Yunfei Xi, Wenhao Pan, Yuanxia Zheng, Yongfa Fan, Jin Cheng, Yu Zhao, Leiwu Zheng

Author Affiliations +

Proceedings Volume 12056, Advanced Etch Technology and Process Integration for Nanopatterning XI; 1205607 (2022) https://doi.org/10.1117/12.2613926
Event: SPIE Advanced Lithography + Patterning, 2022, San Jose, California, United States

Abstract

This paper demonstrates a full-chip OPC correction flow based on deep-learning etch model in a DUV litho-etch case. The flow leverages SEM metrology (eP5 fast E-beam tool, ASML-HMI) to collect massive data, automated metrology software (MXP, ASML-Brion) to extract high quality gauges, and deep-learning etch modeling (Newron etch, ASML-Brion) to capture complicated etch behaviors. The model calibration and verification are performed using a combined data from a test and real chip wafer to ensure sufficient pattern coverage. The model performance of Newron etch is benchmarked against a term-based etch model, wherein Newron etch model shows significant accuracy improvement in the model calibration (<50% for test patterns and <35% for real chip pattern). The Newron etch model is proven stable with a comparable performance in the model verification. Particularly, strong loading effects from underlying sublayer are observed in the full chip wafer, and effectively captured by the Newron etch with a sublayer-aware model form. The calibrated Newron etch model is successfully applied in a model-based etch OPC tape-out with new mask design rules but the same litho-etch process conditions. Compared to the term-based model, Newron etch also shows significant accuracy improvement.

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Citation Download Citation

Weina Shi, Liang Zhu, Yuqian Chen, Jiao Huang, Jinze Wang, Qian Xie, Bilun Zhang, Yunfei Xi, Wenhao Pan, Yuanxia Zheng, Yongfa Fan, Jin Cheng, Yu Zhao, and Leiwu Zheng "OPC accuracy improvement through deep-learning based etch model", Proc. SPIE 12056, Advanced Etch Technology and Process Integration for Nanopatterning XI, 1205607 (25 May 2022); https://doi.org/10.1117/12.2613926

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