Towards Development of an ISFET-Based Smart pH Sensor: Enabling Machine Learning for Drift Compensation in IoT Applications

Monitoring of pH is crucial for several chemical and biochemical processes. ISFET (Ion-Sensitive Field-Effect Transistors)-based pH sensors are promising candidates for pH monitoring applications. However, ISFET devices are prone to temporal and temperature drifts, which severely affects the precision of pH measurements. In this work, we collect experimental data of temporal and temperature drifts in an ISFET sensor to formulate an accurate SPICE macro model, incorporating both temporal and temperature non-idealities. The developed macro model is utilized for generating training data for state-of-the-art machine learning models for drift compensation, with a primary focus on the temporal characteristics. We utilize recurrent neural networks (RNNs) to model the temporal characteristics of ISFET, and thus, compensate the non-ideality. The sensor data is collected in various pH buffer solutions and a data set of sequences containing time-dependent voltage readings are generated by the device and the RNNs are trained to learn the crucial features from the data and map them to the precise pH of the solution. We compare two variants of RNNs, i.e. LSTM (long short-term memory) and GRU (gated recurrent unit), and their bidirectional low computational cost variants - biLSTM and biGRU. Each model is tested in a memory-constrained environment with the availability of a 32-bit and 64-bit floating-point number. Empirically, we find biLSTMs to perform best, where the achieved root mean square error (RMSE) between the model predicted pH and the true pH of the test solution is less than 0.212 pH, with an average RMSE of 0.126 pH. For temperature drift compensation, we collect data for four different temperatures and adapt well-established MLPs (Multi-layer Perceptrons) to compensate the intrinsic temperature drift in the sensor. We observe an average RMSE of the model predicted pH to the true pH to be less than 0.286 pH. The developed RNN models were implemented on Xilinx ZCU104 FPGA development kit using PYNQ framework, which demonstrates low power consumption. The proposed framework establishes the efficacy of Machine Learning (ML) techniques for drift compensation in ISFET-based pH sensors for deployment in IoT applications.