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

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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.

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Acknowledgements

The authors would like to thank New Vision Opto-Electronic Technology Co., Ltd., China, for data support of IZO-TFT devices and circuits. This work was supported by the Guangdong Natural Science Foundation under Grants 2018A030313018 and 2020A1515010567.

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Authors and Affiliations

  1. Department of Electronic Engineering, Jinan University, Guangzhou, 510630, People’s Republic of China

    Wanling Deng, Wanqin Zhang, You Peng, Junkai Huang & Zhi Luo

  2. State Key Laboratory of Luminescent Materials and Devices, South China University of Technology, Guangzhou, 510640, People’s Republic of China

    Weijing Wu

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  1. Wanling Deng
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  4. Weijing Wu
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Correspondence to Zhi Luo.

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Deng, W., Zhang, W., Peng, Y. et al. Compact modeling of metal-oxide TFTs based on artificial neural network and improved particle swarm optimization. J Comput Electron 20, 1043–1049 (2021). https://doi.org/10.1007/s10825-020-01641-z

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  • DOI: https://doi.org/10.1007/s10825-020-01641-z

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