Presentation Information
[24p-P07-2]Development of a Resin Chemical Resistance Classification Model Using Machine Learning for Use in Semiconductor Cleaning Equipment
〇Mitsuru Yambe1, Shogo Kunieda1, Yosuke Hanawa1, Hitoshi Kamijima2, Toshiaki Shintani2, Takuo Okude2, Yoshihiro Hayashi3, Ryo Yoshida3 (1.SCREEN Holdings Co., Ltd., 2.Research Institute of Systems Planning. Inc., 3.The Institute of Statistical Mathematics)
Keywords:
Machine Learning,Polymer,Materials Informatics
Resin materials are used in the piping and rotating tables of semiconductor cleaning equipment, but these are in constant contact with the chemical solutions used to clean semiconductor wafers, so high chemical resistance is required. For this reason, the resins used in equipment must be tested for chemical resistance, which is cost- and time-consuming. In order to reduce these costs, it is necessary to create a machine learning model that predicts the chemical resistance of resin materials. In this report, we present the results of validation of a chemical resistance classification model for resins using publicly available data.