A New Compact Z2-FET Model Based on Artificial Neural Network and Its Applications

The Z $^{\text{2}}$ -FET is a compact device fabricated using fully depleted silicon-on-insulator (FD-SOI) technology, and it operates with band-modulation mechanism. Due to its sharp-switching and hysteresis characteristics, the Z $^{\text{2}}$ -FET has shown promising applications in one-transistor dynamic random access memory (1T-DRAM) and artificial spiking neuron. In this article, we develop a novel compact Z $^{\text{2}}$ -FET model using an artificial neural network (ANN) approach. In light of its unique gate-controlled hysteresis behavior, our model innovatively treats the Z $^{\text{2}}$ -FET as a hybrid-controlled voltage source, significantly simplifying the modeling process and ensuring good accuracy in circuit simulations. To capture the transient behavior of the Z $^{\text{2}}$ -FET, a supplementary ANN (SA) model is established based on the unique gate charge storage effect and feedback mechanism of the Z $^{\text{2}}$ -FET. Using this model, the SPICE simulation results agree well with TCAD simulations, demonstrating its accuracy and reliability. Furthermore, we propose a methodology for modeling laboratory-manufactured Z $^{\text{2}}$ -FET devices based on measured data, which is crucial for the practical application of the developed model.