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