Characteristics prediction and optimization of InP HBT using machine learning

This study introduces a novel application of machine learning using indium phosphide heterojunction bipolar transistors as an example. The objective is to predict the device performance and optimize the device structure by utilizing an artificial neural network (ANN) to calculate the device direct current (DC) and frequency characteristics. To this end, we develop a physics-inspired ANN that emphasizes the significance of the first-order partial derivative of the current over voltage. The ANN is trained on a data set generated by technology computer-aided design simulations, covering a range of voltage setups, device geometries, and doping concentrations. The resulting model accurately predicts the DC and frequency characteristics of the device, and obtain key performance indicators such as the DC current amplification factor, cut-off frequency, and maximum oscillation frequency. This approach can significantly speed up the device parameter optimization and provide a potential numerical tool for design technology co-optimization.