Physics-Informed Deep Learning for Solving Coupled Electron and Phonon Boltzmann Transport Equations

Electron-phonon (e -ph) coupling and transport are ubiquitous in modern electronic devices. The coupled electron and phonon Boltzmann transport equations (BTEs) hold great potential for the simulation of ther-mal transport in metal and semiconductor systems. However, solving the BTEs is often computationally challenging owing to their high dimensional complexity and a wide span of heat carrier properties, which hinder large-scale thermal modeling at the device level. In this work, we present a physics-informed neural network framework for solving the coupled electron and phonon BTEs. Instead of relying on labeled data, the proposed framework directly learns the spatiotemporal solutions (i.e., the electron and phonon distri-bution functions) within a parameterized space by enforcing physical laws. The efficacy of this framework is demonstrated through its ability to accurately resolve temperature profiles in low-dimensional thermal transport problems and visualize the ultrafast electron and phonon dynamics in laser heating experiments on thin metal films. The results indicate that our approach can accurately describe nonequilibrium e -ph energy transfer with improved efficiency, opening alternative avenues for the predictive design and optimization of micro-and nanostructures.