Applications of machine learning at the limits of form-dependent scattering for defect metrology

Mark-Alexander Henn; Hui Zhou; Richard M. Silver; Bryan M. Barnes

doi:10.1117/12.2517285

26 March 2019 Applications of machine learning at the limits of form-dependent scattering for defect metrology

Mark-Alexander Henn, Hui Zhou, Richard M. Silver, Bryan M. Barnes

Author Affiliations +

Proceedings Volume 10959, Metrology, Inspection, and Process Control for Microlithography XXXIII; 109590Z (2019) https://doi.org/10.1117/12.2517285
Event: SPIE Advanced Lithography, 2019, San Jose, California, United States

Abstract

Undetected patterning defects on semiconductor wafers can have severe consequences, both financially and technologically. Industry is challenged to find reliable and easy-to-implement methods for defect detection. In this paper we present robust machine learning techniques that can be applied to classify defect images. We demonstrate the basic principles of an algorithm that uses a convolutional neural network and discuss how such networks can be improved not only in their architecture but also tailored to the specific challenges of defect inspection through more specialized performance metrics. These advances may lead to more cost-efficient measurements by adjusting the decision threshold to minimize the number of wrong defect detections.

Conference Presentation

Citation Download Citation

Mark-Alexander Henn, Hui Zhou, Richard M. Silver, and Bryan M. Barnes "Applications of machine learning at the limits of form-dependent scattering for defect metrology", Proc. SPIE 10959, Metrology, Inspection, and Process Control for Microlithography XXXIII, 109590Z (26 March 2019); https://doi.org/10.1117/12.2517285

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