A computational model of neural learning to predict graphene based ISFET
Journal of Electronic Materials, 2019•Springer
In this study, the graphene ion-sensitive field-effect transistor in an electrolyte solution with
different K+ concentration has been investigated. It is found that by measuring the gate
voltage changes, the K+ concentration in the electrolyte can be determined because of the
interaction between the K+ ions and the gate. For prediction purpose, the artificial neural
network has been employed to predict the I–V characteristic, and it demonstrated superior
performance.
different K+ concentration has been investigated. It is found that by measuring the gate
voltage changes, the K+ concentration in the electrolyte can be determined because of the
interaction between the K+ ions and the gate. For prediction purpose, the artificial neural
network has been employed to predict the I–V characteristic, and it demonstrated superior
performance.
Abstract
In this study, the graphene ion-sensitive field-effect transistor in an electrolyte solution with different K+ concentration has been investigated. It is found that by measuring the gate voltage changes, the K+ concentration in the electrolyte can be determined because of the interaction between the K+ ions and the gate. For prediction purpose, the artificial neural network has been employed to predict the I–V characteristic, and it demonstrated superior performance.
Springer
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