Work

Fast and Accurate Deep Learning-Based Poisson Solver for Single Event Simulations

Year: 2025

Type: article

Abstract: We investigated the possibilities of enhancement of the random-walk drift-diffusion (RWDD) simulations of charge transport in single-events phenomena by modeling electrostatic effects. We first developed a fast and versatile physical Poisson solver based on a simple finite-difference formulation, which was then evaluated, especially in the context of particle simulations, by comparison with technology computer-aided design (TCAD) and irradiation experiments (alpha particles and heavy ions). Capitalizing on this physical solver, we then developed an artificial intelligence (AI)-based solver, using a convolutional neural network (CNN), to further accelerate solving electrostatics and make it usable at large scale in industrial simulators.

Source: IEEE Transactions on Nuclear Science

DOI: 10.1109/tns.2025.3555324

Authors: Sayah El Hajji, Gilles Gasiot, Jean-Luc Autran, Thomas Thery, Vincent Correas, Victor Malherbe

Institutions: STMicroelectronics (France), Centre National de la Recherche Scientifique, Aix-Marseille Université, Institut des Matériaux, de Microélectronique et des Nanosciences de Provence, Institut de Physique de Rennes

Language: English

Topic: Radiation Effects in Electronics

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4408907059

FWCI:

Cites: 44

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Related to: 10