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.

人工神经网络（ANN）的应用可以为电路仿真中使用的半导体器件提供非常准确和快速的模型。在本文中，我们已应用基于有限内存Broyden-Fletcher-Goldfarb-Shanno（L-BFGS）方法的多层感知器（MLP）神经网络来建模柔性金属氧化物薄膜晶体管（TFT）。利用改进的粒子群算法（PSO）为神经网络模型寻找合适的初始参数，该算法由基于质心对立的学习算法和基于欧几里得距离的变异策略组成，进一步提高了搜索能力。这种混合建模例程可以提高I–V和小信号参数（\（g_d \），\（g_m \）的预测精度等特性），与实验数据非常吻合，并充分证明了该模型的有效性。此外，该模型通过Verilog-A实施到模拟器中。TFT的电路级测试表明，具有PSO的ANN紧凑模型可以准确估算金属氧化物TFT电路的性能。