Complementary Metal–Oxide–Semiconductor Potentiometric Field-Effect Transistor Array Platform Using Sensor Learning for Multi-ion Imaging

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 Ca2+.
Uncoated pixels display pH sensitivity from the standard Si3N4 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 Ca2+ 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.