Automatic Design of Structural Parameters for GaN HEMT Using Genetic Algorithm and Artificial Neural Networks

In this paper, an automatic optimization technique of structural parameters for gallium nitride high-electron-mobility transistors (GaN HEMT) is proposed. Given the design targets, including breakdown voltage (BJI) and specific on-resistance (R
on,sp
), this technique can provide the structural parameters of GaN HEMT to meet the targets based on automatic iteration and optimize process using artificial neural networks (ANN) and genetic algorithms (GA). The results show that, when evaluated through technology computer-aided design (TCAD) simulations, designs obtained from the proposed technique deviate from the expected specifications by 2.6% and 0.98%, respectively. Additionally, the efficiency of the proposed method is reflected in its runtime, with the automated design time for each case is within 2 minutes. We believe that the design approach is crucial in accelerating the design closure for GaN transistors.