Neural-based smart CMOS sensors for on-line pattern classification applications

We review previous work on CMOS photoreceptors and neural-based smart sensors that are VLSI realization of a neural network classifier with an integrated photoreceptor array. These sensors are designed for on-line pattern classification applications requiring image capture or non-contact measurement. Photoreceptors are based on Field-Effect-Modified parasitic phototransistors in CMOS technology. Several designs have been implemented. A pre-programmed smart photosensor fabricated in 3 μ CMOS has been successfully tested and a programmable version has been fabricated in 1.2 μ. The latest design of smart sensor is based on a novel unified synapse-neuron building block that results in a highly modular, scalable and area-efficient VLSI architecture. A test circuit containing an 8×8 photosensitive array and a fully-connected programmable neural network with N=54 inputs, m=8 hidden neurons and k=4 output neurons has been designed in the 1.2 μ technology. Based on the new architecture synaptic density has been doubled, and we have been able to increase the size of the optical input array as well as neural classifier itself