Publication | Open Access
Material removal rate model of single crystal diamond near atomic scale chemical mechanical polishing based on SOA
10
Citations
41
References
2025
Year
Traditional mechanical polishing faces significant challenges in achieving ideal surface quality on single crystal diamond (SCD). Chemical mechanical polishing (CMP), combining chemical oxidation and mechanical grinding, offers an effective solution for improving the surface quality of hard and brittle materials. Understanding the material removal characteristics of SCD during CMP is crucial. In this study, the instantaneous contact velocity model of the workpiece and the abrasive particles is established by geometric kinematics, and the instantaneous contact velocity of the abrasive particles at any point on the surface of the workpiece is obtained. Based on the Greenwood and Williamson elastic contact model, the microscopic contact model between the workpiece and the polishing pad is studied, and the effective contact area between the workpiece and the polishing pad and the number of abrasive particles in the effective contact area are obtained. Considering the elastic-plastic deformation in the CMP process of SCD, the micro-contact model between single abrasive particle and workpiece surface is further established, and then the material removal rate (MRR) prediction model of SCD CMP is established. Experimental results reveal the impact of polishing pressure and speed on MRR and surface roughness. The theoretical MRR values align with experimental trends, and the Seagull Optimization Algorithm (SOA) refines the model, reducing the average relative error between theoretical and experimental MRR to 5.98 % and 5.81 %, respectively. The results demonstrate the model's validity. The SCD with near atomic scale ultra-smooth surface is obtained, and the sub-nanometer surface roughness Sa is 0.311 nm. At the same time, the interaction mechanism between chemical oxidation and mechanical removal is investigated through atomic-scale analysis using the ReaxFF molecular dynamics model.
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