A machine-learning assisted sensor for chemo-physical dual sensing based on ion-sensitive field-effect transistor architecture
Journal
IEEE Sensors Journal
Journal Volume
19
Journal Issue
21
Pages
9983-9990
Date Issued
2019
Author(s)
Abstract
Machine 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.
Subjects
ISFET; machine learning; Multi-modal sensor
Other Subjects
Backpropagation; 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 transistors
Type
journal article