Compact modeling of metal-oxide TFTs based on artificial neural network and improved particle swarm optimization

Abstract

The application of artificial neural network (ANN) can give a very accurate and fast model for semiconductor devices used in circuit simulations. In this paper, we have applied multi-layer perceptron (MLP) neural network based on limited memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) method to model the flexible metal-oxide thin-film transistors (TFTs). An improved particle swarm optimization (PSO) is employed to find suitable initial parameters for the ANN model, which consists of a centroid opposition-based learning algorithm and a mutation strategy based on Euclidean distance to enhance the searching ability further. This hybrid modeling routine can improve the accuracy of predictions of both the I–V and small signal parameters ( $$g_d$$ , $$g_m$$ , etc.) characteristics, which are in good agreement with experimental data and fully demonstrate the validity of the proposed model. Furthermore, the model is implemented into a simulator with Verilog-A. The circuit-level tests of TFT show that the ANN compact model with PSO enables accurate performance estimation of metal-oxide TFT circuits.