Ion-selective field-effect transistor sensor response for neural network supervised learning

Ion-selective field transistors (ISFETs) are electrochemical sensors that can detect ion activities but have low selectivity issues for mixed-ion environments. This paper presents the data extraction and investigation of K+ ISFET sensors for neural network as post-processing stage. The environment for the sensor application is potassium and calcium mixed ions that is represented by solutions prepared based on the fixed interference method. The device measurement approach used is MOSFET semiconductor characterization technique, with further extracted data from the transfer characteristics subjected to statistical analysis. Results show that interfering ions influence the sensitivity graph of sensors detecting the main ion by shifting the gradient by 17%. Mean value of voltage response across the interfering ion range results in shifts up to 60 mV. Analysis of variance test gives a small -value indicating noticeable mean value of voltage response relating to change of main ion activity despite a large error variance possibly from the interfering ionic activity purposely added to the solutions. Extracted data from the solutions is then subjected to neural network pattern recognition by supervised learning method giving 73.7% correct recognition.