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arXiv Machine Learning By Luke Ragan, Matteo G. C. Alasio, Enrico Bellotti, Luca Dal Negro

Physics Informed Neural Networks for Solving the Drift-Diffusion Equations

Read the original on arXiv Machine Learning →

The paper presents a Physics Informed Neural Network (PINN) approach to solve the drift‑diffusion equations that govern semiconductor devices. By applying advanced training techniques, the authors achieve errors comparable to commercial TCAD software while handling extreme gradients and discontinuities in a PN‑junction. The method also enables inverse design, retrieving doping profiles from desired electric field or potential distributions under non‑equilibrium bias conditions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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