The roughness of the contact surface exerts a vital role in rubbing. It is still a significant challenge to understand the microscopic contact of the rough surface at the atomic level. Herein, the rough surface with a special root mean square (RMS) value is constructed by multivariate Weierstrass–Mandelbrot (W–M) function and the rubbing process during that the chemical mechanical polishing (CMP) process of diamond is mimicked utilizing the reactive force field molecular dynamics (ReaxFF MD) simulation. It is found that the contact area A/A₀ is positively related with the load, and the friction force F depends on the number of interfacial bridge bonds. Increasing the surface roughness will increase the friction force and friction coefficient. The model with low roughness and high lubrication has less friction force, and the presence of polishing liquid molecules can decrease the friction force and friction coefficient. The RMS value and the degree of damage show a functional relationship with the applied load and lubrication, i.e., the RMS value decreases more under larger load and higher lubrication, and the diamond substrate occurs severer damage under larger load and lower lubrication. This work will generate fresh insight into the understanding of the microscopic contact of the rough surface at the atomic level.

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, revealing the significant impact of these properties on prediction accuracy. The SDD model is distinguished by its analysis of the interactions between abrasive grits and the workpiece, as well as the mechanisms governing stress-induced damage evolution. The surface roughness model and SDD model both establish a stable relationship with the grit depth of cut (GDC). Additionally, we have developed an analytical relationship between the GDC and grinding process parameters. This, in turn, enables the establishment of an analytical framework for predicting surface roughness and SDD based on grinding process parameters, which cannot be achieved by previous models. The models were validated through systematic experiments on three different semiconductor materials, demonstrating excellent agreement with experimental data, with prediction errors of 6.3% for surface roughness and 6.9% for SDD. Additionally, this study identifies variations in elastic recovery and material plasticity as critical factors influencing surface roughness and SDD across different materials. These findings significantly advance the accuracy of predictive models and broaden their applicability for grinding hard and brittle semiconductor materials.

Silicon carbide (SiC) is a promising semiconductor material for making high-performance power electronics with higher withstand voltage and lower loss. The development of cost-effective machining technology for fabricating SiC wafers requires a complete understanding of the deformation and removal mechanism. In this study, molecular dynamics (MD) simulations were carried out to investigate the origins of the differences in elastic–plastic deformation characteristics of the SiC polytypes, including 3C-SiC, 4H-SiC and 6H-SiC, during nanoindentation. The atomic structures, pair correlation function and dislocation distribution during nanoindentation were extracted and analyzed. The main factors that cause elastic–plastic deformation have been revealed. The simulation results show that the deformation mechanisms of SiC polytypes are all dominated by amorphous phase transformation and dislocation behaviors. Most of the amorphous atoms recovered after completed unload. Dislocation analysis shows that the dislocations of 3C-SiC are mainly perfect dislocations during loading, while the perfect dislocations in 4H-SiC and 6H-SiC are relatively few. In addition, 4H-SiC also formed two types of stacking faults.

Diamond has attracted extensive attention from many scholars due to its characteristics, whereas the high-efficiency and ultra-low damage machining regarding diamond is still a bottleneck restricting its applications. Herein, a new polishing equipment was built, and a new method of photochemical mechanical polishing (PCMP) combining mechanical + chemical + optical field coupling was proposed to solve the problem of high-efficiency and ultra-low damage machining of diamond. The experimental results show that the sub-nanoscale surface of Ra 0.071 nm, rms 0.090 nm, Rz 0.943 nm, and the micro removal rate per hour can be obtained after PCMP for the first time. Transmission electron microscope (TEM) and X-ray photoelectron spectroscopy (XPS) results manifest that the damage of the diamond substrate after PCMP is amorphous carbon damage, and the damage thickness of amorphous carbon is only 0.6 nm. The PCMP model of ReaxFF molecular dynamics (MD) simulation was constructed to elucidate the microscopic mechanism during the diamond PCMP process. Simulation results show that ultraviolet (UV) irradiation increases the activity of diamond by decreasing the stability of substrate and weakening the C-C bonds, thus promoting the oxidation of diamond surface. Our experimental and theoretical research provides an insight for realizing high-efficiency and ultra-low damage machining of diamond.