Enhancing model accuracy and calibration efficiency with image-based pattern selection using machine learning techniques

Ren-Cheng Sun; Dae-Kwon Kang; Chester Jia; Meng Liu; De-Bao Shao; Young-Seok Kim; Jangho Shin; Mark Simmons; Qian Zhao; Mu Feng; Yiqiong Zhao; Shibing Wang; Sungho Kim; Sungwoo Ko; Shinyoung Kim; Jaeseung Choi; Chanha Park

doi:10.1117/12.2584692

22 February 2021 Enhancing model accuracy and calibration efficiency with image-based pattern selection using machine learning techniques

Ren-Cheng Sun, Dae-Kwon Kang, Chester Jia, Meng Liu, De-Bao Shao, Young-Seok Kim, Jangho Shin, Mark Simmons, Qian Zhao, Mu Feng, Yiqiong Zhao, Shibing Wang, Sungho Kim, Sungwoo Ko, Shinyoung Kim, Jaeseung Choi, Chanha Park

Author Affiliations +

Proceedings Volume 11613, Optical Microlithography XXXIV; 116130Y (2021) https://doi.org/10.1117/12.2584692
Event: SPIE Advanced Lithography, 2021, Online Only

Conference Poster

Abstract

Calibration pattern coverage is critical for achieving a high quality, computational lithographic model. An optimized calibration pattern set carries sufficient physics for tuning model parameters and controlling pattern redundancy as well as saving metrology costs. In addition, as advanced technology nodes require tighter full chip specifications and full contour prediction accuracy, pattern selection needs accommodate these and consider contour fidelity EP (Edge Placement) gauges beyond conventional test pattern sets and cutline gauge scopes. Here we demonstrate an innovative pattern selection workflow to support this industry trend. 1) It is capable of processing a massive candidate pattern set at the full chip level. 2) It considers physical signals from all of the candidate pattern contours. 3) It implements our unsupervised machine learning technology to process the massive amount of physical signals. 4) It offers our users flexibility for customization and tuning for different selection and layer needs. This new pattern selection solution, connected with ASML Brion’s MXP (Metrology of eXtreme Performance) contour fidelity gauges and superior, accurate Newron (deep learning) resist model, fulfills the advanced technology node demands for OPC modeling, thus offering full chip prediction power.

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Ren-Cheng Sun, Dae-Kwon Kang, Chester Jia, Meng Liu, De-Bao Shao, Young-Seok Kim, Jangho Shin, Mark Simmons, Qian Zhao, Mu Feng, Yiqiong Zhao, Shibing Wang, Sungho Kim, Sungwoo Ko, Shinyoung Kim, Jaeseung Choi, and Chanha Park "Enhancing model accuracy and calibration efficiency with image-based pattern selection using machine learning techniques", Proc. SPIE 11613, Optical Microlithography XXXIV, 116130Y (22 February 2021); https://doi.org/10.1117/12.2584692

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