A sensor data mining process for identifying root causes associated with low yield in semiconductor manufacturing

• Published in: Data technologies and applications 2023-06, Vol.57 (3), p.397-417
• Main Authors: Kim, Eunji, An, Jinwon, Cho, Hyun-Chang, Cho, Sungzoon, Lee, Byeongeon
• Format: Article
• Language: English
• Subjects: Case studies; Data mining; Diagnosis; Engineers; Environment; Equipment Maintenance; Industry 4.0; Manufacturing; Manufacturing Industry; Networks; Neural networks; Preventive maintenance; Recipes; Root cause analysis; Scientific visualization; Semiconductors; Sensors; Support vector machines; Teaching Methods
• ISSN: 2514-9288; 2514-9318; 2514-9288
• DOI: 10.1108/DTA-08-2022-0341

PurposeThe purpose of this paper is to identify the root cause of low yield problems in the semiconductor manufacturing process using sensor data continuously collected from manufacturing equipment and describe the process environment in the equipment.Design/methodology/approachThis paper proposes a sensor data mining process based on the sequential modeling of random forests for low yield diagnosis. The process consists of sequential steps: problem definition, data preparation, excursion time and critical sensor identification, data visualization and root cause identification.FindingsA case study is conducted using real-world data collected from a semiconductor manufacturer in South Korea to demonstrate the effectiveness of the diagnosis process. The proposed model successfully identified the excursion time and critical sensors previously identified by domain engineers using costly manual examination.Originality/valueThe proposed procedure helps domain engineers narrow down the excursion time and critical sensors from the massive sensor data. The procedure's outcome is highly interpretable, informative and easy to visualize.