Predictive models for the surface roughness and subsurface damage depth of semiconductor materials in precision grinding

Abstract Workpiece rotational grinding is widely used in the ultra-precision machining of hard and brittle semiconductor materials, including single-crystal silicon, silicon carbide, and gallium arsenide. Surface roughness and subsurface damage depth (SDD) are crucial indicators for evaluating the surface quality of these materials after grinding. Existing prediction models lack general applicability and do not accurately account for the complex material behavior under grinding conditions. This paper introduces novel models for predicting both surface roughness and SDD in hard and brittle semiconductor materials. The surface roughness model uniquely incorporates the material’s elastic recovery properties,…

• NN
  National Natural Science Foundation of China

  Award: 52375411