Detection and monitoring of defects/anomalies in random circuit patterns using autoencoder

Abstract

To detect anomalies/defects for random/arbitrary IC patterns, we decompose SEM images into small sub-images and apply an identical autoencoder to each of them. The astronomical varieties in random IC patterns are reduced into limited varieties in elementary patterns, which are coded onto limited dimension latent vectors in autoencoder. A wide variety of anomalies/defects are detected with neither design data nor prior knowledge about defects at a high SNR with practical throughput. It also captures a small sign of change in process conditions and pattern fidelity and will be effective for inspection in EUV patterning using high-speed SEMs.