Using generative artifical intelligence for charged-particle beam image defect detection

Abstract

A non-transitory computer readable medium that stores a set of instructions that is executable by at least one processor of a computing device to cause the computing device to perform operations for defect detection. The operations include: inputting an image clip of a charged-particle beam inspection (CPBI) image to a variational autoencoder (VAE) model that has been trained with multiple training image clips. Latent space parameters are received from the VAE. Key performance indicators (KPIs) are calculated based on the received latent space parameters and a reconstruction error. Defects are determined in the CPBI image based on the calculated KPIs.

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