Improved Stability of Graphene-Coated CMOS ISFETs for Biosensing.

A polymer-assisted graphene transfer method is used to transfer sheets of monolayer and multilayer graphene onto the passivation layer of ion-sensitive field effect transistor arrays. The arrays are fabricated using commercial 0.35 µm complementary metal-oxide-semiconductor (CMOS) technology and contain 3874 pixels sensitive to pH changes on the top silicon nitride surface. By inhibiting dispersive ion transport and hydration of this underlying nitride layer, the transferred graphene sheets help address non-idealities in the sensor response while retaining some pH sensitivity due to the presence of ion adsorption sites. Improvements in hydrophilicity and electrical conductivity of the sensing surface after graphene transfer, as well as in-plane molecular diffusion along the graphene-nitride interface, also greatly improve spatial consistency across an array, allowing for ∼20% more pixels to remain within operating range and enhancing sensor reliability. Multilayer graphene offers a better performance trade-off than monolayer graphene, reducing drift rate by ∼25% and drift amplitude by ∼59% with minimal reduction in pH sensitivity. Monolayer graphene offers slightly better temporal and spatial uniformity in performance of a sensing array, which is associated with the consistency in layer thickness and a lower defect density.

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.