Highly Selective Multiple Gases Detection Using a Thermal-Conductivity-Based MEMS Resonator and Machine Learning

Abstract

There is an urgent need to develop innovative and highly selective gas sensors for environmental, residential, and industrial applications. Here, we propose a highly selective multiple gases detection system using an electro-thermally heated silicon micro-resonator and machine-learning algorithms. The device is based on the cooling/heating effect of gases on the thermal stresses of a resonating structure. As a case study, we demonstrate sensitive and selective responses toward He, Ar, and CO2. To generate the unique signature markers for each gas, multiple datasets are collected at three different concentration levels (4%, 10%, and 25%). A principal component analysis (PCA) is conducted using seven customized highly significant and unique signature markers. The markers are obtained through data processing from the response of all gases. Quadratic discriminant and medium Gaussian support vector machines (SVMs) are used for training based on the seven customized features, which yield a high classification accuracy of 100 %. The proposed gas-sensing approach can be promising for the development of intelligent and highly selective compact size sensing platforms without the need for special materials for coating and functionalization.