Reduction of systematic defects with machine learning from design to fab

Yuansheng Ma; Le Hong; James Word; Fan Jiang; Vlad Liubich; Liang Cao; Srividya Jayaram; Doohwan Kwak; YoungChang Kim; Germain Fenger; Ananthan Raghunathan; Joerg Mellmann

doi:10.1117/12.2551703

23 March 2020 Reduction of systematic defects with machine learning from design to fab

Yuansheng Ma, Le Hong, James Word, Fan Jiang, Vlad Liubich, Liang Cao, Srividya Jayaram, Doohwan Kwak, YoungChang Kim, Germain Fenger, Ananthan Raghunathan, Joerg Mellmann

Author Affiliations +

Proceedings Volume 11329, Advanced Etch Technology for Nanopatterning IX; 1132909 (2020) https://doi.org/10.1117/12.2551703
Event: SPIE Advanced Lithography, 2020, San Jose, California, United States

Abstract

Maximizing yield in a modern semiconductor fab requires proper optimization of the design (layout), process technology, and fab process tool recipes. For the past decade the prevalence of systematic defects tied to design or design-process interactions have predominated over random defect sources. Previously Resolution Enhancement Technology (RET), Design For Manufacturability (DFM), and Design-Technology Co-optimization (DTCO) techniques were the successful response to eliminating systematic yield limiting patterns. Machine learning, with its ability to find trends and make predictions based on large volumes of data, provides a unique path towards further reduction in systematic defect levels. This talk will present methods based on the use of design and process info with machine learning and computational lithography methods to identify and eliminate yield limiting patterns in the design, improve the accuracy of mask generation with etch and resist modeling and OPC, and improve the productivity and accuracy of fab defect detection and diagnostics. This paper will present methods to improve EPE control and reduce systematic hotspots through both supervised and unsupervised machine learning. Specifically we will focus on 3 areas: - identifying and yield limiting patterns in the design phase. - improving the accuracy (EPE control) of mask generation with machine learning assisted etch and resist modeling and OPC. - improving the productivity and accuracy of fab defect detection and diagnostics with machine learning.

Conference Presentation

Citation Download Citation

Yuansheng Ma, Le Hong, James Word, Fan Jiang, Vlad Liubich, Liang Cao, Srividya Jayaram, Doohwan Kwak, YoungChang Kim, Germain Fenger, Ananthan Raghunathan, and Joerg Mellmann "Reduction of systematic defects with machine learning from design to fab", Proc. SPIE 11329, Advanced Etch Technology for Nanopatterning IX, 1132909 (23 March 2020); https://doi.org/10.1117/12.2551703

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