Near‐Sensor 2D Pattern Classification System Based on 3D Memristor Array

ABSTRACT The proposed work demonstrates a 3D memristor crossbar‐based pattern classification system. Analogue signals from a 2D sensor array are directly applied to the memristor crossbar circuit using TSVs for computation. The proposed near‐sensor computing system reduces the wiring complexity between the sensor unit and the compute unit significantly. The 3D memristor crossbar occupies a very small area compared to an equivalent 2D crossbar. We have leveraged ex‐situ training for the memristor‐based neural network using the two extreme resistance levels of the device ( R ON , R OFF ) which enables simple training circuit. To process a 100 × 100 sensor array, the proposed approach requires 5.6× less wiring between the sensor array and the memristor crossbar circuit compared to a non‐stacked approach.