Defect detection and classification on imec iN5 node BEoL test vehicle with multibeam scanning electron microscope

Abstract

We present an automated application for defect detection and classification from ZEISS multibeam scanning electron microscope (MultiSEM (R)) images, based on machine learning (ML) technology. We acquire MultiSEM images of a semiconductor wafer suited for process window characterization at the imec iN5 logic node and use a dedicated application to train ML models for defect detection and classification. We show the user flow for training and execution, and the resulting capture and nuisance rates. Due to straightforward parallelization, the application is designed for the large amounts of data generated rapidly by the MultiSEM.