Surface finish prediction models for precision grinding of silicon

Conventional grinding of silicon substrates results in poor surface quality unless they are machined in ductile mode on expensive ultra-precision machine tools. However, precision grinding can be used to generate massive ductile surfaces on silicon so that the polishing time can be reduced immensely and surface quality improved. However, precision grinding has to be planned with reliability in advance and the process has to be performed with high rates of reproducibility. Therefore, this work reports the empirical models developed for surface parameters R _a, R _max, and R _t with precision grinding parameters, depths of cut, feed rates, and spindle speeds using conventional numerical control machine tools with Box–Behnken design. Second-order models are developed for the surface parameters in relation to the grinding parameters. Analysis of variance is used to show the parameters as well as their interactions that influence the roughness models. The models are capable of navigating the design space. Also, the results show large amounts of ductile streaks at depth of cut of 20?μm, feed rate of 6.25?mm/min, and spindle speed of 70,000?rpm with a 43-nm R _a. Optimization experiments by desirability function generate 37-nm R _a, 400-nm R _max, and 880-nm R _t with massive ductile surfaces.