Machine learning in chemical–mechanical planarization: A comprehensive review of trends, applications, and challenges

Chemical–mechanical planarization (CMP) is a critical and complex process in semiconductor manufacturing, where high precision and tight tolerances demand sophisticated process control. Machine learning (ML) has emerged as a promising tool to support this need. However, the literature is fragmented across domains and often lacks concrete guidance on deploying ML methods in real-world CMP environments. This review addresses that gap and provides actionable insights for both academic researchers and industrial practitioners. This work systematically analyzes peer-reviewed publications at the intersection of ML and CMP. It offers three significant contributions: First, it provides a detailed assessment of the types and sources of data used in CMP-related ML studies, distinguishing between equipment-level data, process-level data, and product-level data. Second, it introduces a structured classification of ML applications across four key areas: Research and Development, Real-Time Process Control, Equipment Health Monitoring, and Post-Process Feedback and Analysis. Third, it synthesizes commonly reported challenges along the data science pipeline. Where available, practical solutions and implementation strategies are discussed. Across these areas, existing studies illustrate ML's potential to, among other things, enable more adaptive control, reduce metrology demands, enhance defect detection, and support layout- and process-aware design optimization, highlighting its growing role in CMP process innovation. The review concludes by outlining open research gaps and proposing future directions, including the development of benchmark datasets, integration of domain knowledge, and extension of modeling efforts to additional targets such as dishing and within-wafer non-uniformity. While focused on CMP, many challenges discussed, such as data leakage, sparse target labeling, and hybrid modeling, are relevant across engineering informatics. Addressing them in CMP can inform the broader development of robust and adaptable ML systems in intelligent manufacturing.