Reproduce “Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review” on OpenResearch

About this paper

Tao Han, Zahra Taheri, Hyunwoong Ko

This review paper examines the current landscape of Machine Learning and Deep Learning applications in semiconductor film deposition, identifying their limitations in physical consistency and data dependence. It then details Physics-Informed Neural Networks (PINNs) as a promising solution, demonstrating how they integrate physical laws to enhance model accuracy, interpretability, and generalizability, particularly useful when experimental data is sparse.

Key insights

• Physics-Informed Neural Networks (PINNs) integrate physical laws directly into neural network architectures, ensuring predictions are physically consistent and overcoming generalization issues of purely data-driven models.
• PINNs offer a robust solution to challenges in semiconductor manufacturing like data scarcity and the need for interpretability, as they can learn effectively from limited or noisy datasets.
• Effective training of PINNs for complex systems requires strategies to mitigate gradient imbalance between different loss components, such as loss reweighting and adaptive data resampling.

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