Hsu W.EChang Y.HHuang Y.JHuang J.CCHIH-TING LIN2021-09-022021-09-02201919386737https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070076085&doi=10.1149%2f08906.0031ecst&partnerID=40&md5=bb16605ba3b9a96ceca2a0c99ffb3d2ehttps://scholars.lib.ntu.edu.tw/handle/123456789/580724Multi-modal sensing system is essential in internet-of-things (IoT). Making the sensor systems small, low power and cost effective is the ultimate goal to fulfill the growing needs. To achieve this requirement, we present a single-device-dual-sensor by transforming a conventional dual-gate ion sensitive field-effect transistor (DG-ISFET) to a pH/light bi-functional sensing device. In order to realize an effective dual-sensor, we introduce a sequential control method and back-propagation neural network (BPNN) to DG-ISFET in order to distinguish and quantify the signal from pH and light generated by the same device. The BPNN models are capable for practical applications to measure pH value and light intensity. Based on the results, the light interference of DG-ISFET can be transformed into effective illumination sensing quantity for dual-modal sensing applications. ? The Electrochemical SocietyBackpropagation; Cost effectiveness; Internet of things; Ion sensitive field effect transistors; Photonics; Back-propagation neural networks; Internet of Things (IOT); Light intensity; Multi-modal sensing; Sensing applications; Sensing devices; Sensor systems; Sequential control; Neural networksconference paper10.1149/08906.0031ecst2-s2.0-85070076085