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

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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.
Funding Information
  • MCIN/AEI/10 (0000DONOTUSETHIS0000.13039/501100011033, PID2020-115842RB-I00, PDC2023-145896-I00)
  • Junta de Castilla y León and Fondo Europeo de Desarrollo Regional (SA136P23)
  • Fundación General de la Universidad de Salamanca

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