Ion-sensitive field-effect transistor selectivity with back-propagation neural network

The ion-sensitive field-effect transistor (ISFET) produces voltage signals, in a similar manner to the metal-oxide field-effect transistor, sensitive to ionic concentration change. When immersed in ionic solution with mixed ions of similar chemical characteristics, ISFETs respond with deceptive voltage signals due to the interfering ion contribution over the main ion of interest. In this paper, we applied back-propagation neural network to data acquired from titration of potassium ion (K+) and ammonium ion (NH4+). The role of the post-processing is to extract main ionic concentration level in the presence of an interfering ion. Primary data from measured observations with actual device variation and background ion was fed to a feedforward multilayer perceptron trained with several methods of back-propagation. Results show that neural network trained with backpropagation algorithm is able to improve concentration information by gives 15% improvement with 4 sensor array compared to direct estimation without post-processing. Additionally, averaging from multiple classifiers is shown to give a further 5% improvement on the regression factor between output and targeted values.