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Lapping is one of the standard essential methods to realise the global planarization of SiC and other semiconductor substrates. It is necessary to deeply study the mechanism to obtain SiC lapping process parameters with a strong comprehensive lapping performance (i.e., high material removal rate (MRRm), small surface roughness (Ra), and low total thickness variation (TTV)). The effects of the lapping process parameters and their interactions on lapping performance for SiC were investigated using orthogonal experiments; the effects on the MRRm, Ra, TTV, and optimal parameters under the conditions of a single evaluation index were investigated using intuitive analysis (range analysis, variance analysis, and effect curve analysis). The entropy value method and grey relational analysis were used to transform the multi-evaluation-index optimisation into a single-index optimisation about the grey relational grade (GRG) and to comprehensively evaluate the lapping performance of each process parameter. The results showed that the lapping plate types, abrasive size, and their interaction effect had the most significant effects on MRRm and Ra, with a contribution of over 85%. The interaction between the lapping plate types and abrasive size was also found to have the most significant effect on TTV, with a contribution of up to 51.07%. As the lapping plate’s hardness and abrasive size increased, the MRRm and Ra also gradually increased. As the lapping normal-pressure increased, MRRm increased, Ra gradually decreased, and TTV first decreased and then increased. MRRm, Ra, and TTV first increased and then decreased with increasing abrasive concentration. Compared to the optimisation results obtained by intuitive analysis, the process parameter optimised by the grey relational analysis resulted in a smooth surface with an MRRm of 90.2 μm/h, an Ra of 0.769 nm, and a TTV of 3 μm, with a significant improvement in the comprehensive lapping performance. This study reveals that a combination of orthogonal experiments and grey relational analysis can provide new ideas for optimising the process parameters of SiC.

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