Empirical Modeling of a Graphene Field-Effect Transistor Sensor

Abstract

This theoretical nanoscience work explore the empirical method of extracting experimental radiation sensor data from published work and project the performance limit of the sensor in term of back gate voltage, resistance and radiation flux. We demonstrate an empirical regression modeling of an X-ray radiation sensor based on a graphene field effect transistor (GFET). The experimental data obtained from published work only showed the values of resistance, R versus back gate voltage, Vbg for X-ray flux, E of 0 kV, 30 kV and 40 kV. Least Squares Estimation (LSE) in nonlinear regression model is utilized to obtain the individual polynomial regression model of resistance versus back gate voltage model for each of the 3 X-ray flux. LSE with multiple nonlinear regression incorporating with Nimmo and Atkinsmethod is employ to get a unified polynomial regression based on the individual model. The model is simulated in MATLAB and provide fast execution time. The unified model for is able to predict the resistance versus back gate voltage for various X-ray radiation flux beyond these three values given values. In addition to that, we are able to find the limit of X-ray flux based on the initial characteristic. Moreover, our technique enable one to visualize the interrelationship between R, Vbg and E variables and perform multivariate analysis. The empirical regression model of GFET is shown to have good agreement with experimental data and theoretical prediction for multivariate such as back gate voltage, resistance and X-ray flux.