A Novel Neural Network-Based Transistor Compact Model Including Self-Heating

We develop a SPICE-compatible neural network-based compact model to accurately capture the temperature dependence and self-heating effects in Field Effect Transistors (FETs). The model is based on artificial neural networks with no semi-empirical temperature equations. The transfer and activation functions are optimized to improve the accuracy of the model. A new temperature relaxation model is proposed, which allows training the model using ambient temperature data without iteratively extracting the self-heating parameters. The proposed method can simply generate the ambient and dynamic self-heating characteristics for circuit simulations. The model can accurately reproduce the current-voltage (IV), capacitance-voltage (CV), and transient characteristics of FETs across a broad temperature range with a speed advantage of up to 12X versus BSIM-CMG.