Open AccessHybrid modelling routine for metal‐oxide TFTs based on particle swarm optimisation and artificial neural network
Author(s) -
Peng You,
Deng Wanling,
Wu Weijing,
Luo Zhi,
Huang Junkai
Publication year - 2020
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2019.4001
Subject(s) - particle swarm optimization , broyden–fletcher–goldfarb–shanno algorithm , artificial neural network , position (finance) , computer science , process (computing) , hybrid algorithm (constraint satisfaction) , algorithm , artificial intelligence , computer network , constraint satisfaction , asynchronous communication , finance , probabilistic logic , economics , constraint logic programming , operating system
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 , g d , 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.