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Problem statement: The Ion-Sensitive Field-Effect Transistor (ISFET) is a metal-oxide field-effect transistor-based sensor that reacts to ionic activity at the electrolye/membrane/gate interface. The ionic sensor faces issue of selectivity from interfering ions that contribute to the sensor electrical response in mixed solutions. Approach: We present the training data collection of ISFET voltage response for the purpose of post-processing stage neural network supervised learning. The role of the neural network is to estimate the main ionic activity from the interfering ion contribution in mixed solutions given time-independent input voltages. In this work, potassium ion (K+) and ammonium ion (NH4+) ISFET response data are collected with readout interface circuit that maintains constant voltage and current bias levels to the ISFET drain-source terminals. Sample solutions are prepared by keeping the main ion concentration fixed while the activity of an interfering ion varied based on the fixed interference method. Results: Sensor demonstrates linear relationship to the ion concentration within detection limit but has low repeatability of 0.52 regression factor and 0.16 mean squared error between similarly repeated measurements. We find that referencing the voltage response to the sensor response in DIW prior to measurement significantly improves the repeatability by 15.5% for correlation and 98.3% for MSE. Demonstration of multilayer perceptron feed-forward neural network estimation of ionic concentration from the data collection shows a recognition of >0.8 regression factor. Conclusion: Time-independent DC voltage response of ISFET of the proposed setup can be used as training data for neural network supervised learning for the estimation of K+ in mixed K+/NH4+ solutions

american journal of applied sciences

Knowledge Representation of Ion-Sensitive Field-Effect Transistor Voltage Response for Potassium Ion Concentration Detection in Mixed Potassium/Ammonium Ion Solutions