Hybrid modelling routine for metal-oxide TFTs based on particle swarm optimisation and artificial neural network

摘要

An effective and robust hybrid algorithm consisting of particle swarm optimisation (PSO) and limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) method based on artificial neural network (ANN) is proposed for modelling flexible metal-oxide thin-film transistors (TFTs). The L-BFGS method as an optimiser is exploited to update the parameters of ANN and speed up the training process. A mutation strategy for PSO is derived to enhance the searching ability further. With the great global searching ability, PSO is implemented to find a hopeful initial position in solution space for the next ANN model. The simulation result shows a high accuracy not only in I-V curve fitting but also in small-signal parameter ($g_m$gm, $g_d$gd, etc.) predictions, which have not been exposed in the training process. The measured DC characteristics of In-Zn-O TFTs are used to verify the proposed ANN model, which has the benefits of rapid fitting from the L-BFGS algorithm and universal searching ability from PSO.

关键词

curve fitting thin film transistors neural nets optimisation particle swarm optimisation artificial neural network effective hybrid algorithm robust hybrid algorithm PSO flexible metal-oxide thin-film transistors L-BFGS method optimiser training process great global searching ability ANN model L-BFGS algorithm universal searching ability hybrid modelling routine metal-oxide TFTs