An artificial olfactory sensory neuron for selective gas detection with in-sensor computing

Abstract

We present a neuromorphic sensory module for gas detection using a two-in-one typed olfactory neuron for in-sensor computing. The module integrates a sensor for gas detection and a neuron for generating spike signals and delivering them into the post-synapse. The sensing ability is enabled by catalytic metal particles on a silicon nanowire field-effect transistor (Si-NW FET), while the neuronal ability is also realized by the Si-NW FET itself, which encodes spike signals for a spiking neural network (SNN). By mounting palladium (Pd) and platinum (Pt) nanoparticles on the Si-NW FET, we demonstrate the module to classify H2 and NH3 using a single-layer perceptron (SLP) with the sensory neurons and FET-based synapses. Power demand and manufacturing cost efficiency are important considerations in mobile applications and edge computing in the Internet-of-Things era. This in-sensor module-based SNN hardware provides a cost-effective solution that is inherently more power and form-factor efficient over existing designs.