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

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.