Work

Electrothermal modeling of GaN high electron mobility transistors using a Monte Carlo-trained hybrid AI-thermal approach with microscopic physical insight

Year: 2025

Type: article

Abstract: Self-heating significantly impacts the performance and reliability of GaN high electron mobility transistors, but capturing these effects with electrothermal Monte Carlo (MC) simulations is computationally intensive. This paper presents the application of a hybrid AI-thermal model, previously tested on another device, to the electrothermal analysis of GaN HEMTs. This first component consists of an artificial neural network (ANN) trained on isothermal MC data to predict drain current and lattice temperature. To extend the framework, a set of ANN-based microscopic models is introduced, composed of three dedicated networks that reconstruct spatially resolved quantities—electric field, carrier velocity, and sheet electron density. The system is coupled with compact thermal resistance models and iterated until convergence. The proposed approach achieves excellent agreement with electrothermal MC simulations while reducing computation time by approximately an order of magnitude. In addition to global performance metrics, it provides detailed internal profiles under electrothermally consistent conditions, making it a practical tool for fast device evaluation, in-depth analysis, and integration into compact modeling flows.

Source: Journal of Applied Physics

DOI: 10.1063/5.0279834

Authors: S. García-Sánchez, I. Íñiguez-de-la-Torre, J. Mateos, T. González

Institution: Universidad de Salamanca

Language: English

Topic: GaN-based semiconductor devices and materials

Subfield: Condensed Matter Physics

Field: Physics and Astronomy

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: hybrid

Estimated APC paid: $3,500