Prediction of the surface roughness and material removal rate in chemical mechanical polishing of single-crystal SiC via a back-propagation neural network

Abstract

Chemical mechanical polishing (CMP) is a common method for realising the global planarisation and polishing of single-crystal SiC and other semiconductor substrates. The strong oxidant hydroxyl radicals (·OH) generated by the Fenton reaction can effectively oxidise and corrode the SiC substrate, and are thus used to improve the material removal rate (MRR) and surface roughness (Ra) after polishing of SiC during CMP. Therefore, it is necessary to study the material removal mechanism in detail. Based on the modified Preston equation, the effects of the CMP process parameters on the MRR and Ra after polishing of SiC and their relationship were studied, and a prediction model of the CMP process parameters, MRR, and Ra after polishing was also established based on a back-propagation neural network. The MRR initially increased and then decreased, and the Ra after polishing initially decreased and then increased, with increasing FeSO4 concentration, H2O2 concentration, and pH value. The MRR continuously increased with increasing abrasive particle size, abrasive concentration, polishing pressure, and polishing speed. However, the Ra continuously decreased with increasing abrasive particle size and abrasive concentration, increased with increasing polishing pressure, and initially decreased and then increased with increasing polishing speed. The established prediction model could accurately predict the relationship between the process parameters, MRR and Ra after polishing in CMP (relative prediction error of less than 10%), which could provide a theoretical basis for CMP of SiC.