Work

Hybrid AI-Thermal Model Trained via Monte Carlo Simulations to Study Self-Heating Effects

Year: 2024

Type: article

Abstract: This article presents a hybrid artificial intelligence (AI)-thermal model for the determination of the current and lattice temperature of a device under a given bias voltage. The model is based on a neural network trained with isothermal Monte Carlo simulations and the coupling of any thermal model where the lattice temperature depends on the dissipated power. The proposed procedure has been validated on a gallium nitride (GaN)-based self-switching diode, although its application to other electronic devices, such as transistors, is also straightforward. The proposed method allows for a significant reduction in computational cost, in addition to enabling the investigation of various thermal models in an efficient manner. It is capable of reproducing the results that would be obtained through electrothermal Monte Carlo simulations, which are particularly computationally expensive.

Source: IEEE Transactions on Electron Devices

DOI: 10.1109/ted.2024.3438120

Authors: Sergio García-Sánchez, Ignacio Íñiguez-de-la-Torre, Tomás González, Javier Mateos

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: green

Funder: Junta de Castilla y León