End-to-End Prediction of Sodium Concentration from Uncalibrated Sodium ISFETs

Abstract

Ion-selective field-effect transistors (ISFETs) are widely used for chemical sensing in biomedical and environmental applications. They require calibration before deployment in the field because of individual sensor response variations and temporal drift in their readout. However, calibration can be time-consuming if a large number of ISFETs are to be deployed. This work proposes an end-to-end prediction neural network where individual sensor calibrations are not needed. We train the network to predict the ionic concentration by using a simulated dataset of responses from a physical ISFET model to varying sodium concentrations. The model includes the known non-idealities of real ISFET sensors. Our network also outputs a confidence interval for the prediction which can be useful for determining the quality of the prediction. On a dataset of real sodium ISFET recordings, our end-to-end prediction network gave a decrease of at least 42% in the prediction error of sodium concentration compared to that from ISFETs calibrated using two manual methods.