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The automated wafer inspection and quality control is a complex andtime-consuming task, which can speed up using neuromorphic memristivearchitectures, as a separate inspection device or integrating directly intosensors. This paper presents the performance analysis and comparison ofdifferent neuromorphic architectures for patterned wafer quality inspection andclassification. The application of non-volatile memristive devices in thesearchitectures ensures low power consumption, small on-chip area scalability. Wedemonstrate that Long-Short Term Memory (LSTM) outperforms other architecturesfor the same number of training iterations, and has relatively low on-chip areaand power consumption.

Wafer Quality Inspection using Memristive LSTM, ANN, DNN and HTM