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

Abstract Semiconductor manufacturing relies heavily on specialized film deposition processes, such as Chemical Vapor Deposition and Physical Vapor Deposition. These complex processes require precise control over critical parameters—including temperature, pressure, and material flow rates—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 specifically 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. Reviewed studies are categorized into four key areas: (1) Process Control and Optimization; (2) Defect Image Recognition and Classification; (3) Tool Preventative Maintenance Prediction and Hardware Anomaly Detection; and (4) Atomic Layer Deposition Precursor Finding. 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.