A machine learning-based solution for modulated image analysis in lithography process stability diagnosis

Peng Wu; Qi Zhu; Jihong Yang; Changjie Sun; Yiming Zhu; Abhishek Vikram; Ye Chen; Guojie Cheng; Hui Wang; Qing Zhang; Wenkui Liao

doi:10.1117/12.2613403

26 May 2022 A machine learning-based solution for modulated image analysis in lithography process stability diagnosis

Peng Wu, Qi Zhu, Jihong Yang, Changjie Sun, Yiming Zhu, Abhishek Vikram, Ye Chen, Guojie Cheng, Hui Wang, Qing Zhang, Wenkui Liao

Author Affiliations +

Proceedings Volume 12052, DTCO and Computational Patterning; 1205213 (2022) https://doi.org/10.1117/12.2613403
Event: SPIE Advanced Lithography + Patterning, 2022, San Jose, California, United States

Abstract

Automated image analysis and image classification system that employs machine learning has been developed and applied to the PWQ/FEM flow to enhance the Process Stability Diagnosis (PSD) solution, which can now handle a significant volume of wafer images while realizing a 4X reduction in time to results. Moving the task of image analysis and classification to the computer has the added benefit of avoiding person-to-person inconsistencies in classification. The data flow consists of an automated machine learning-enabled process window analysis system that relies on CDSEM images taken on a FEM or Focus/Exposure Matrix wafer. We report results based on CDSEM images containing both contact hole and line features. The system enables full-wafer SEM image auto-classification and process window characterization.

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Peng Wu, Qi Zhu, Jihong Yang, Changjie Sun, Yiming Zhu, Abhishek Vikram, Ye Chen, Guojie Cheng, Hui Wang, Qing Zhang, and Wenkui Liao "A machine learning-based solution for modulated image analysis in lithography process stability diagnosis", Proc. SPIE 12052, DTCO and Computational Patterning, 1205213 (26 May 2022); https://doi.org/10.1117/12.2613403

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