Bio-inspired involute groove pad textures for enhanced material removal and surface quality in SiC chemical mechanical polishing
Optimizing polishing pad texture is critical for the chemical mechanical polishing (CMP) of hard materials like silicon carbide (SiC). This study introduces a novel bio-inspired involute groove design for polishing pads, drawing inspiration from the nautilus chamber. Based on reactor theory and a modified Preston's equation, the influence of groove geometry on slurry dynamics and contact pressure was investigated. Three involute groove textures with varying base circle radii of 40, 50, and 60 mm were systematically analyzed through fluid dynamics and stress simulations. Simulations predicted that the 60 mm radius texture (LP-C) yields the most uniform slurry flow and a desirable Gaussian-like pressure distribution. Subsequent CMP experiments on SiC substrates validated these findings; the 60 mm radius pad enhanced the material removal rate by at least 18.87 % and reduced surface roughness by 15.10 % compared to the other designs. These results provide a validated design strategy for advanced pad textures, demonstrating that optimizing involute groove geometry is a highly effective approach to improving CMP performance for challenging ceramic materials. • Theoretical analysis of Preston's equation revealed the significant impact of surface grooves on CMP results. • Flow field simulations evaluated the slurry flow and force uniformity with different base circle radii during CMP. • The stress distribution simulation optimized the Gaussian-like material removal pattern of involute grooves. • SiC polishing experiments validated the machining efficiency and quality of various textured-pads.
Institutions
- Chang'an University (CN)
- Beijing Machine Tool Research Institute (CN)
- Xi'an Jiaotong University (CN)
Publication Details
- Journal
- Journal of Materials Research and Technology
- Published
- 2025-09-23
- DOI
- https://doi.org/10.1016/j.jmrt.2025.09.172
- Citations
- 1
- Primary Topic
- Computational and Text Analysis Methods
- Type
- article
- Field-Weighted Citation Impact
- 5.34