A robust hybrid neural architecture for an industrial sensor application

A programmable hybrid neural network architecture has been used to implement a smart optical sensor with focal-plane pattern classification for a flexible manufacturing cell environment. The network contains an integrated photosensitive array based on modified photo BJTs as input to a fully-connected multilayer feedforward (MLFF) neural classifier. The architecture features a distributed neuron realization that employs a number of active nonlinear resistor circuits operating in parallel. It minimizes the effect of parameter variations due to non-uniform device fabrication over the die surface. Moreover, due to the modularity of the architecture and locality of interconnections, synaptic density has been doubled in comparison with a conventional realization. A photosensor-classifier chip consisting of a 2-D array of 64 neural-based smart pixels and additional neural network circuits has been fabricated. The proposed architecture has been implemented in both CMOS and BiCMOS process technologies as part of a sensor optimization study.