AI-guided reliability diagnosis for 5,7nm automotive process

Dongin Kim; Hyung Joo Lee; Sanghyun Choi; Seungpyo Hong; Seungjae Lee; Doohwan Kwak; Srividya Jayaram; Seungwon Paek; Minho Kwon; Yeongdo Kim; Hyobe Jung; Ivan Kissiov; Melody Tao; Andres Torres; Nathan Greeneltch; Ho Lee

doi:10.1117/12.2662880

27 April 2023 AI-guided reliability diagnosis for 5,7nm automotive process

Dongin Kim, Hyung Joo Lee, Sanghyun Choi, Seungpyo Hong, Seungjae Lee, Doohwan Kwak, Srividya Jayaram, Seungwon Paek, Minho Kwon, Yeongdo Kim, Hyobe Jung, Ivan Kissiov, Melody Tao, Andres Torres, Nathan Greeneltch, Ho Lee

Author Affiliations +

Proceedings Volume 12496, Metrology, Inspection, and Process Control XXXVII; 124963J (2023) https://doi.org/10.1117/12.2662880
Event: SPIE Advanced Lithography + Patterning, 2023, San Jose, California, United States

Abstract

Automotive semiconductor products demand high reliability. The current process of performing electrical test after fab-out may not be sufficient for efficient reliability management. This paper proposes an AI solution for improving the reliability of automotive semiconductor products. The solution includes two unique concepts: fab-data augmentation (FDA) to estimate missing values using partially available measurement data during the fabrication process and real-time prediction of reliability using machine learning (ML) models. The ML model is also used to identify and rank critical process steps that impact reliability, and to predict the reliability of wafers in real time. This allows low reliability wafers to be screened out early during the chip fabrication process, improving the overall reliability of the final product.

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Dongin Kim, Hyung Joo Lee, Sanghyun Choi, Seungpyo Hong, Seungjae Lee, Doohwan Kwak, Srividya Jayaram, Seungwon Paek, Minho Kwon, Yeongdo Kim, Hyobe Jung, Ivan Kissiov, Melody Tao, Andres Torres, Nathan Greeneltch, and Ho Lee "AI-guided reliability diagnosis for 5,7nm automotive process", Proc. SPIE 12496, Metrology, Inspection, and Process Control XXXVII, 124963J (27 April 2023); https://doi.org/10.1117/12.2662880

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