Anomaly detection in random circuit patterns using autoencoder

Background: Systematic/stochastic pattern defects affect the production yield of integrated circuits (IC) containing trillions of 10-nm level features. Aim: We detect pattern anomalies/defects from images obtained from scanning electron microscopes (SEM) for random/arbitrary IC patterns without using design data. Approach: We decompose SEM images into small sub-images and apply an identical autoencoder to each of them to detect anomalies. 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. The discrepancy between autoencoder input and output represents a deviation of local pattern shapes from ideal or allowed ones and is used as an index of anomaly. Results: A wide variety of anomalies/defects are detected in regular and random IC patterns fabricated by extreme ultra-violet lithography without prior knowledge about anomalies at a high signal-to-noise ratio within a time shorter than the typical image acquisition time of SEMs. They include missing/necking in holes/trenches, collapsing/breaking in lines, various local pattern distortion/deformation, and tiny particles. Frequency and spatial distributions of the discrepancy index are sensitive to process changes and can be used for visualizing the sign or causes of anomalies. Conclusions: The method is effective for inspecting memory and random logic ICs with high-speed SEMs.