A Bio-Inspired Analog Gas Sensing Front End

Abstract

With the Internet of Things (IoT) paradigm promising to deploy trillions of sensors, the search is on for effective means to efficiently derive useful information from the flood of sensor data through efficient hardware preprocessing. Of particular interest are computational paradigms that get their inspiration from biological sensory systems that seamlessly extract relevant information through highly efficient analog signal processing. Functions, such as feature extraction, learning, or recognition, could especially benefit from bio-inspired architectures. As an example in the case, this paper presents a bio-inspired analog gas sensing frontend for an artificial olfactory system. The analog front end implements a novel trainable feature extraction algorithm for metal-oxide gas sensor arrays. The algorithm extracts one composite feature of all analytes by performing the gradient decent algorithm during training and transforms the sensor responses into concentration-invariant spike patterns. An integrated circuit realization of the algorithm, implemented in a 65-nm CMOS technology, supports six-input channels, uses subthreshold analog circuits, and consumes 519-nW/channel in the training mode, and 463-nW/channel in the recognition mode.