Multi-evaluation index and multi-process parameters optimisation of Electro-Fenton chemical mechanical polishing process for single-crystal SiC

To optimise the process parameters of Electro-Fenton chemical mechanical polishing (EF-CMP) for single-crystal SiC, an L 27 (3 13 ) orthogonal experimental design was implemented to investigate the effects of voltage (A), H 2 O 2 concentration (B), abrasive concentration (C), polishing pressure (D), and selected two-factor interactions on material removal rate (MRR), surface roughness Ra, maximum peak-to-valley roughness (Rt), and total thickness variation (TTV). Quadratic regression models were developed for the four responses, and range analysis, ANOVA, and effect curve analysis were used to identify the dominant factors for each single response. Because the optimal parameter combinations for MRR, Ra, Rt, and TTV were inconsistent, entropy-weighted grey relational analysis (GRA) was further applied to transform the multi-response optimisation problem into a single-objective problem based on the derived grey relational grade (GRG). The regression models showed good predictive ability, with adjusted R 2 values of 0.9958, 0.8810, 0.8323, and 0.8416 for MRR, Ra, Rt, and TTV, respectively, and all validation errors were within 20%. The highest GRG was obtained at 1.5 V, 7.5 wt% H 2 O 2 , 1.5 wt% abrasive concentration, and 0.04 MPa polishing pressure. Under these conditions, the MRR reached 2357.740 nm/h, while Ra, Rt, and TTV were 0.3720 nm, 6.3462 nm, and 3 μm, respectively. Compared with the single-response optimal combinations for MRR, Ra, Rt, and TTV, the final recommended GRA combination increased the GRG by 9.24%, 9.17%, 20.90%, and 31.14%, respectively. These results demonstrate that the proposed EF-CMP optimisation framework effectively balances polishing efficiency, surface quality, and wafer flatness in SiC polishing.