Study on Amorphous InGaZnO Thin-Film Transistor Modeling Method Based on Artificial Neural Network

Abstract

In this work, two approaches of forward design neural network and reverse design neural network were proposed to accelerate the design of passivation-layer structured amorphous indium gallium zinc oxide thin-film transistor (a-IGZO TFT). It was based on a neural network with the back-propagation neural network (BPNN) and general regression neural network (GRNN) as general approximators. The forward design neural network utilized the density-of-states (DOS) key parameters of a-IGZO film as input signals, and could quickly predict characteristic curves with high accuracy. The forward design effectively improved the problem of complex input/output layer parameters in the existing methods, which was significant for the prediction and optimization of a-IGZO TFT device performance. And the reverse design neural network adopts the DOS key parameters of a-IGZO film as the output signal to achieve the rapid prediction of DOS parameters of a-IGZO film. The inverse design effectively compensated the drawback that a-IGZO TFT required artificial tuning of DOS key parameters to achieve characteristic curve fitting. All in all, the neural network model can effectively determine whether the output parameters of the network meet the design objectives and whether the output parameters need to be changed by adjusting the input parameters to eventually achieve the performance prediction and material parameters optimization of a-IGZO TFT.

Keywords

• a-IGZO TFT
• density-of-states
• reverse design neural network
• radio-frequency power