A Neuromorphic Electronic Nose Design

TL;DR

This paper presents a neuromorphic analog front-end for Metal Oxide sensors that encodes gas concentration in spike timing differences, inspired by mammalian olfactory systems, enabling efficient real-time gas detection.

Contribution

It introduces a novel analog front-end circuit for MOx sensors that encodes gas concentration in spike timing, inspired by biological olfaction, and demonstrates its effectiveness for gas identification and estimation.

Findings

Time difference between pulses decreases with higher gas concentration.

Sensor array front-end successfully identifies gases and estimates concentrations.

Analog spike encoding offers potential for power-efficient, real-time gas sensing.

Abstract

Rapid detection of gas concentration is important in different domains like gas leakage monitoring, pollution control, and so on, for the prevention of health hazards. Out of different types of gas sensors, Metal oxide (MOx) sensors are extensively used in such applications because of their portability, low cost, and high sensitivity for specific gases. However, how to effectively sample the MOx data for the real-time detection of gas and its concentration level remains an open question. Here, we introduce a simple analog front-end for one MOx sensor that encodes the gas concentration in the time difference between pulses of two separate pathways. This front-end design is inspired by the spiking output of a mammalian olfactory bulb. We show that for a gas pulse injected in a constant airflow, the time difference between pulses decreases with increasing gas concentration, similar to the spike time difference between the two principal output neurons in the olfactory bulb. The circuit design is further extended to a MOx sensor array, and this sensor array front-end was tested in the same environment for gas identification and concentration estimation. Encoding of gas stimulus features in analog spikes at the sensor level itself may result in data and power-efficient real-time gas sensing systems in the future that can ultimately be used in uncontrolled and turbulent environments for longer periods without data explosion.