Digital neural processing unit for electronic nose

In a biological nose, the environment usually suggests a number of common odors. The classification process checks sensed information against existing knowledge. This similarity with Reinforcement Learning neural networks suggests challenging implementation problems. A VLSIC digital design and implementation of a Reinforcement Artificial Neural Network (RANN) for chemical classification, in an electronic nose is presented. The chip is designed to classify chemical gases among four possible volatile organic compounds. The system consists of four neurons and twelve synapses. A neuron has been implemented on a tiny chip, using 2.0 μm n-well CMOS technology, at Orbit Semiconductors, through the MOSIS facilities. Simulation results demonstrated proper operation. Standalone experiments are satisfactory, with off-chip weight storage and weight update. Electronic nose system testing is under way