Deep-learning-based gas identification by timevariant illumination of a single micro-LEDembedded gas sensor

Electronic nose(e-nose)technology for selectively identifying a target gas through chemoresistive sensors has gained much attention for various applications,such as smart factory and personal health monitoring.To overcome the crossreactivity problem of chemoresistive sensors to various gas species,herein,we propose a novel sensing strategy based on a single micro-LED(μLED)-embedded photoactivated(μLP)gas sensor,utilizing the time-variant illumination for identifying the species and concentrations of various target gases.A fast-changing pseudorandom voltage input is applied to the μLED to generate forced transient sensor responses.A deep neural network is employed to analyze the obtained complex transient signals for gas detection and concentration estimation.The proposed sensor system achieves high classification(~96.99%)and quantification(mean absolute percentage error~31.99%)accuracies for various toxic gases(methanol,ethanol,acetone,and nitrogen dioxide)with a single gas sensor consuming 0.53 mW.The proposed method may significantly improve the efficiency of e-nose technology in terms of cost,space,and power consumption.