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
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 optimize the number of wrongly classified no-defect images.
Metrology, Inspection, and Process Control for Microlithography XXXIII
, Zhou, H.
, Silver, R.
and Barnes, B.
Applications of machine learning at the limits of form-dependent scattering for defect metrology, Metrology, Inspection, and Process Control for Microlithography XXXIII, San Jose, CA, [online], https://doi.org/10.1117/12.2517285
(Accessed November 29, 2023)