A Mixture-Gas Multisensor Interface With On-Chip Classification and On-Edge Regression

This article presents a mixture-gas detectable edge-computing device with two-step on-sensor classification and regression capabilities. The mixture-gas classification is achieved through a proposed analog on-chip artificial intelligence (AI) circuit and an analog autonormalization circuit for better AI accuracy is integrated together in the readout integrated circuit (ROIC). For on-edge mixture-gas concentration analysis, a proposed cross-iterative tuning (CIT) regression algorithm is embedded in the edge device. For system-level feasibility, an edge-computing Internet-of-Things (IoT) device prototype with metal–oxide–semiconductor (MOS) sensor devices is manufactured based on the ROIC and experimentally verified to achieve 25.9% better classification and 10.9% better regression.