Abstract
This work describes an array of 1024 ion-sensitive field-effect transistors (ISFETs) using sensor-learning techniques to perform multi-ion imaging for concurrent detection of potassium, sodium, calcium, and hydrogen. Analyte-specific ionophore membranes are deposited on the surface of the ISFET array chip, yielding pixels with quasi-Nernstian sensitivity to K(+), Na(+), or Ca(2+). Uncoated pixels display pH sensitivity from the standard Si(3)N(4) passivation layer. The platform is then trained by inducing a change in single-ion concentration and measuring the responses of all pixels. Sensor learning relies on offline training algorithms including k-means clustering and density-based spatial clustering of applications with noise to yield membrane mapping and sensitivity of each pixel to target electrolytes. We demonstrate multi-ion imaging with an average error of 3.7% (K(+)), 4.6% (Na(+)), and 1.8% (pH) for each ion, respectively, while Ca(2+) incurs a larger error of 24.2% and hence is included to demonstrate versatility. We validate the platform with a brain dialysate fluid sample and demonstrate reading by comparing with a gold-standard spectrometry technique.