Hsu, W.-E.W.-E.HsuChang, Y.-H.Y.-H.ChangCHIH-TING LIN2020-06-112020-06-1120191530437Xhttps://scholars.lib.ntu.edu.tw/handle/123456789/499153Machine learning has become an emerging method for next-generation smart technologies. It also promotes a development-paradigm shift in sensing technologies, which are essential in various smart applications. In this paper, we develop a data-driven method for a monolithic ion-sensitive field-effect transistor (ISFET) to have both photon and pH bi-detection capabilities simultaneously. The proposed methods are executed based on sequential-bias-reconfiguration of the ISFET. Utilizing support vector machine and back-propagation neural network, the photocurrent and ion-induced current can be calculated and decoupled. To evaluate the proposed method, semi-quantification by classification methods and quantification by regression methods are both examined. This paper has experimentally demonstrated the dual-detection capability of a pH range from 5 to 9 and an intensity range from 0 to 760μW/cm2 with prediction error less than 1.5%. To fulfill low-computation requirement for applications, we further optimize the proposed algorithm by feature reduction to balance performances between accuracy, complexity of data acquisition, and data processing. With the developed machine-learning sensing device, therefore, we successfully demonstrate potentials of data-driven sensing devices in future applications. © 2001-2012 IEEE.ISFET; machine learning; Multi-modal sensorBackpropagation; Data acquisition; Data handling; Ions; Learning systems; Machine learning; Neural networks; Regression analysis; Support vector machines; Back propagation neural networks; Balance performance; Classification methods; Data-driven methods; Detection capability; Future applications; Multimodal sensor; Smart applications; Ion sensitive field effect transistorsjournal article10.1109/JSEN.2019.29270382-s2.0-85073231465https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073231465&doi=10.1109%2fJSEN.2019.2927038&partnerID=40&md5=3d9e2935f3be4a8f3565f3333caaddb3