Machine learning modeling using process context and exposure data for overlay prediction

W. H. Wang; Irina Brinster; Mohsen Maniat; Fatima Anis; Yen-Hui Lee; Sven Boese; C. F. Tseng; Wei-Yuan Chu; Boris Habets; C. H. Huang; Elvis Yang; T. H. Yang; K. C. Chen

doi:10.1117/12.2613202

26 May 2022 Machine learning modeling using process context and exposure data for overlay prediction

W. H. Wang, Irina Brinster, Mohsen Maniat, Fatima Anis, Yen-Hui Lee, Sven Boese, C. F. Tseng, Wei-Yuan Chu, Boris Habets, C. H. Huang, Elvis Yang, T. H. Yang, K. C. Chen

Author Affiliations +

Proceedings Volume 12053, Metrology, Inspection, and Process Control XXXVI; 120531Q (2022) https://doi.org/10.1117/12.2613202
Event: SPIE Advanced Lithography + Patterning, 2022, San Jose, California, United States

Conference Poster

Abstract

Overlay is one of the critical parameters and directly impacts yield. Due to high metrology cost, only a small number of wafers are measured per lot. To this end, virtual metrology (VM) aims to provide valuable information about the nonmeasured wafers with little to no additional cost. VM leverages historical per-wafer measurements from exposure tools and processing equipment collected at previous process steps to report overlay on every wafer. As data-driven approaches gain more adoption in the semiconductor manufacturing, machine learning (ML) is a natural choice to tackle this task. In this paper, we present the strategies of learning overlay prediction models from exposure and process context data as well as the steps for achieving desired prediction performance, including data preparation, feature selection, best modeling methods, hyperparameters tuning and objective. We demonstrate our methodology on a large HVM dataset under stable APC conditions.

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W. H. Wang, Irina Brinster, Mohsen Maniat, Fatima Anis, Yen-Hui Lee, Sven Boese, C. F. Tseng, Wei-Yuan Chu, Boris Habets, C. H. Huang, Elvis Yang, T. H. Yang, and K. C. Chen "Machine learning modeling using process context and exposure data for overlay prediction", Proc. SPIE 12053, Metrology, Inspection, and Process Control XXXVI, 120531Q (26 May 2022); https://doi.org/10.1117/12.2613202

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