Drift Prediction and Chemical Reaction Identification for ISFETs using Deep Learning

This paper demonstrates a novel framework utilising artificial neural networks (ANNs) to identify electrochemical signals, estimate drift and perform signal extraction for ISFET sensors. We propose a neural network based on the combination of Multi-Layer Perceptrons (MLPs) and Gated Recurrent Units (GRUs), to aid the analysis of chemical reactions for ISFETs by identifying the reaction origin and compensating for drift in real time. The model is trained and tested using Keras on an artificial dataset, achieving a reaction classification accuracy of 89.71 % with an average delay of 15.73s. We have also implemented the proposed model on an FPGA through high-level synthesis (HLS) with a tunable latency of 56254 clock cycles under an 100 MHz clock. This work paves the way for enhancing biosensors with ANNs, where a smart electrochemical imager with integrated edge processing can be implemented for various biomedical applications.