| dc.description.abstract | SiC is an important raw material for third-generation
semiconductors. Because of its wide energy gap, high temperature
resistance, high pressure resistance, and low loss, it is one of the main
development directions of the current industry. The hardness of SiC is
extremely high, which also causes the consumption of production time
and cost. Chemical mechanical polishing is currently the main polishing
technology for semiconductor wafers. In order to effectively improve
the material removal rate of the SiC wafer polishing process,
femtosecond laser assisted processing technology is used to modify the
surface of SiC wafers and remove materials to make it effective Shorten
the follow-up chemical mechanical grinding processing time, increase
production capacity, and increase the industrial application of SiC. This
paper takes the above as the starting point and aims to establish a SiC
laser surface processing quality prediction model. Collect laser-related
parameters and processing quality of SiC laser surface processing, and
build models with common machine learning algorithms such as
decision trees, random forests, Extra Trees, Gradient Boosting and
Stacking, Understand the relationship between laser processing
parameters and processing quality, stabilize processing quality, and
prepare for future research related to SiC laser processing. | en_US |