General neural network-based static performance prediction model construction techniques for gate-all-around and planar field effect transistor

Abstract

—This paper proposes general neural network-based static performance prediction model construction techniques for gate-all-around (GAA) and planar field effect transistor (FET). Firstly, a unique data preprocessing method named quasi-linear transformation is proposed to improve the prediction accuracy. By introducing transformation functions to process the input, the relationship between the input and output is simplified, thereby facilitating the model training. Secondly, an improved weighted loss function scheme that considers a more comprehensive evaluation criterion to enhance the training process is proposed. Compared with traditional artificial neural networks, the average prediction error of the output and transfer curves is reduced by 33 % and 25 % for GAA and planar FET, respectively. Meanwhile, the proposed model demonstrates strong extrapolation ability. Moreover, compared to traditional methods of obtaining static characteristic curves, this method is more efficient. Furthermore. the proposed neural network-based static performance prediction model is converted to Verilog-A model, demonstrating potential in circuit simulation.