Toward Intelligent Chemical Mechanical Polishing: Integrating Multiscale Modeling and Machine Learning

Abstract

Chemical mechanical polishing (CMP) remains the cornerstone planarization technology in advanced semiconductor manufacturing. As device architectures transition toward FinFETs, gate-all-around structures, heterogeneous integration, and wide-bandgap semiconductors, CMP must simultaneously deliver angstrom-level flatness, high selectivity, low damage, and improved sustainability. Against this background, research in CMP is moving beyond empirical trial-and-error toward a more integrated paradigm that combines consumables design, physics-based modeling, and data-driven intelligence. This review systematically summarizes recent progress in CMP from four interrelated perspectives. First, we examine advances in slurry and abrasive design, including morphology engineering, porous and core–shell abrasives, defect-controlled ceria systems, and environmentally friendlier chemical formulations, together with the emergence of photo-, electro-, ultrasonic-, plasma-, and gas-assisted CMP. Second, we review the multiscale modeling landscape, spanning macroscale planarization and profile evolution, mesoscale contact mechanics and slurry transport, and atomistic simulations based on first-principles calculations and reactive molecular dynamics. Third, we assess the rapidly growing role of machine learning in CMP, with emphasis on material removal rate prediction, surface quality evaluation, process monitoring, virtual metrology, and intelligent run-to-run control. Fourth, we discuss sustainability-oriented CMP, including source reduction, wastewater treatment and recycling, and the use of modeling and digital control to reduce non-productive resource consumption. By synthesizing developments across these areas, this manuscript highlights the current gaps in cross-scale model integration, physics-informed intelligent control, transferability across tools and materials, and life-cycle-oriented process design. We conclude by outlining how a closed-loop CMP ecosystem—linking materials innovation, mechanistic understanding, in situ sensing, and intelligent optimization—may support the next generation of high-efficiency, green, and manufacturable planarization technologies.