Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review

2025-07-15

AI Summary

Semiconductor thin-film deposition (CVD/PVD) demands stringent control over uniformity, adhesion, and functional performance, yet conventional modeling and control approaches suffer from limited interpretability, prediction accuracy, and robustness. This paper presents a systematic review of physics-informed neural networks (PINNs) for CVD/PVD process modeling, proposing a novel knowledge-driven paradigm that directly embeds first-principles partial differential equations and physical constraints into the neural network architecture. By synergistically integrating mechanistic modeling with data-driven learning, the approach significantly enhances model interpretability, generalization capability, and process adaptability. We identify critical bottlenecks and research gaps hindering PINN deployment in semiconductor manufacturing, and establish a technology roadmap toward intelligent deposition control—characterized by high accuracy, scalability, and low operational overhead. This work provides a methodological foundation for physics-guided smart manufacturing in advanced semiconductor fabrication.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Computer Vision: Diffusion Models for Vision
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Semiconductor manufacturing relies heavily on film deposition processes, such as Chemical Vapor Deposition and Physical Vapor Deposition. These complex processes require precise control to achieve film uniformity, proper adhesion, and desired functionality. Recent advancements in Physics-Informed Neural Networks (PINNs), an innovative machine learning (ML) approach, have shown significant promise in addressing challenges related to process control, quality assurance, and predictive modeling within semiconductor film deposition and other manufacturing domains. This paper provides a comprehensive review of ML applications targeted at semiconductor film deposition processes. Through a thematic analysis, we identify key trends, existing limitations, and research gaps, offering insights into both the advantages and constraints of current methodologies. Our structured analysis aims to highlight the potential integration of these ML techniques to enhance interpretability, accuracy, and robustness in film deposition processes. Additionally, we examine state-of-the-art PINN methods, discussing strategies for embedding physical knowledge, governing laws, and partial differential equations into advanced neural network architectures tailored for semiconductor manufacturing. Based on this detailed review, we propose novel research directions that integrate the strengths of PINNs to significantly advance film deposition processes. The contributions of this study include establishing a clear pathway for future research in integrating physics-informed ML frameworks, addressing existing methodological gaps, and ultimately improving precision, scalability, and operational efficiency within semiconductor manufacturing.

Problem

• Enhancing semiconductor film deposition control using PINNs
• Improving predictive modeling for film quality and uniformity
• Integrating physical laws into ML for manufacturing optimization

Innovation

• Physics-Informed Neural Networks for deposition control
• Embedding physical laws into neural network architectures
• Enhancing interpretability and accuracy in manufacturing