Optimization and Predictive Modeling of SiC Wafer Dicing Using a Thin Diamond Grinding Wheel via RSM and NSGA-II

To investigate how the process parameters of ultra-thin diamond grinding wheel dicing affect the dicing quality of silicon carbide (SiC) wafers, single-factor experiments were designed. This study examined the influence of key process parameters, including spindle speed, feed rate, and first dicing depth, on the maximum chip width on the front side W₁ and the maximum chip width on the back side W₂, thereby determining their optimal parameter ranges. Subsequently, a quadratic polynomial prediction model was established using response surface analysis to analyze the interactive effects among the grinding wheel dicing process parameters. Finally, the prediction model was optimized using the genetic algorithm NSGA-II, and the optimal parameter combination for the two response variables was determined: a spindle speed of 31,960 r/min, a feed rate of 2.0019 mm/s, and a first dicing depth of 197.51 μm, yielding an average W₁ of 4.8852 μm and W₂ of 18.5360 μm. The relative errors between the predicted and average experimental values are 2.83% for W₁ and 4.43% for W₂. Both errors are below 5%, confirming the validity of the model. Therefore, the model serves as a practical reference for planning subsequent dicing processes using ultra-thin diamond grinding wheels.