Machine Learning Techniques for Performance Enhancement of Si3N4-gate ISFET pH Sensor

This paper presents the performance enhancement of Si
₃
N
₄
-gate Ion-Sensitive Field-Effect Transistor (IS-FET) based pH sensor using machine learning (ML) techniques. The temporal and temperature characteristics of the ISFET device are modeled using SPICE tool. The developed macromodel incorporates the electrochemical and device parameters, which enhances the robustness of the model in order to produce accurate characteristics of ISFET device over a wide temperature range as well as long term usage. The ISFET readout circuit tends to show a drift in the measured pH values with temperature and time variations.To make the device functioning more accurate, we incorporate state-of-the-art models based on ML techniques. We show how such auxiliary ML models reduce the effects of most common undesired ambience variations to get consistent output from CVCC (Constant Voltage Constant Current) Read Out Integrated Circuit (ROIC). We first simulate the quality data from the CVCC ROIC developed using ISFET macromodel. Next, we learn the auxiliary ML model parameters focused on capturing ROIC ambience dependence. This includes learning how individual ambient factors and their correlation impact the ROIC functioning. To make the compensations effective for a wide range of practical scenarios, we carry out compensation on a wide range of data values with temperature ranging from 15-65
^o
C and time ranging from 0-50 hours. Over this range, we compare our various ML models and observe that Random Forest is the most efficient in terms of lowest error rate to carry out the temperature and temporal drift compensation of pH values obtained from ISFET-based pH sensor circuitry.