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arXiv cs.LG
arXiv cs.LG
8/4/2026
A Physics-Chemistry-Informed Neural Network (PCINN) for Real-Time Spatial-ALD Coverage Prediction and Reliable Kinetics Inversion

A Physics-Chemistry-Informed Neural Network (PCINN) for Real-Time Spatial-ALD Coverage Prediction and Reliable Kinetics Inversion

Short summary

A physics-chemistry-informed neural network (PCINN) achieves CFD-level accuracy for spatial ALD coverage prediction at ~7ms per query, roughly 50,000x faster than CFD. The architecture hard-codes known surface kinetics as a trainable chemistry layer while a small network learns only the operating-condition to concentration closure, keeping it interpretable and invertible. An identifiability analysis shows adsorption energy and desorption rate are robustly recoverable, while adsorption prefactor is only identifiable across multiple temperatures.

  • •PCINN surrogate predicts SALD coverage in ~7ms with R²=0.998 from only 30 training cases
  • •Hybrid architecture hard-codes known kinetics, making the model interpretable and invertible
  • •Fisher information analysis identifies which kinetic parameters are reliably recoverable from data

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