Chemical mechanical polishing (CMP), as a widely used global smoothing technique, requires a high polishing rate and a low surface roughness. However, it is difficult to meet these requirements with single-step polishing. To obtain a perfect 4H-SiC wafer surface, we studied a two-step CMP 4H-SiC wafer using a-Al ₂ O ₃ -based slurry and nano-SiO ₂ -based slurry. Before the two-step process was determined, the effect of concentration of KMnO ₄ and pH on the polishing rate of 4H-SiC was first studied with Al ₂ O ₃ as the abrasive, and the maximum polishing rate of 1400 nm h ⁻¹ was determined under the condition of pH of 2.00 and KMnO ₄ concentration of 6.5 wt% in the step. Furthermore, the effect of H ₂ O ₂ concentration on the polishing rate of 4H-SiC was investigated using SiO ₂ as the abrasive and V ₂ O ₅ as the catalyst. It was determined that under the condition of V ₂ O ₅ concentration of 4 wt% and H ₂ O ₂ concentration of 10 wt%, the maximum polishing rate of the second step polishing was 150 nm h ⁻¹ , and a perfect surface was obtained, with a surface roughness of 0.066 nm. In addition, the in situ friction coefficients have used to characterize the two-step process. At the end, the CMP mechanisms of the above two slurry have been deduced and preliminarily demonstrated.

Plasma activation has been investigated for its ability to induce a strong bonding energy even at low-temperature annealing. In this paper, Ge, Si, and surface hydrophilicities with oxygen and nitrogen plasma activation are analyzed by contact angle measurement. Compared to wet chemical treatments, such as solutions containing ammonium hydroxide, the hydrophilicity of Ge wafer surface is strongly enhanced by or plasma activation. For germanium, a highly hydrophilic and smooth surface has been obtained by plasma activation only for 10 s. The contact angle measurements indicate that plasma is more remarkable than plasma in the same activation conditions. A higher surface roughness, which is only observed in the plasma activated sample, is decreased greatly after rinsing in deionized water.

Genetic programming (GP) is a widely recognized and powerful approach for symbolic regression (SR) problems. However, existing GP methods rely on a single form to solve the problem, which limits their search diversity and increases the likelihood of getting stuck in local optima, especially in complex scenarios. In this paper, we propose a general multiform GP framework to improve the performance of GP on complicated SR problems. As far as we know, this paper is the first attempt to integrate the multiform optimization paradigm with GP to accelerate the search performance. The key idea of the proposed framework is to construct multiple forms to solve the same problem cooperatively at the same time. During the evolution process, knowledge gained from different forms is shared among the solvers to improve the search diversity and efficiency. A knowledge transfer mechanism is specifically designed to facilitate knowledge transfer among GP solvers with different modeling forms. In addition, an adaptive resource control mechanism is designed to reallocate computing resources according to the problem-solving efficiency of different solvers to further improve search efficiency. To demonstrate the effectiveness of the proposed framework, a multiform GEP algorithm (MF-GEP) is designed and tested on 20 problems, including physical datasets, synthetic datasets, and real-world datasets. The experimental results have demonstrated the effectiveness of the proposed framework.