Abstract
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.
Subject
Library and Information Sciences,Information Systems
Reference32 articles.
1. Random forests;Machine Learning,2001
2. Big data analytic for multivariate fault detection and classification in semiconductor manufacturing,2017
3. An empirical study of design-of-experiment data mining for yield-loss diagnosis for semiconductor manufacturing;Journal of Intelligent Manufacturing,2014
4. A framework for root cause detection of sub-batch processing system for semiconductor manufacturing big data analytics;IEEE Transactions on Semiconductor Manufacturing,2014
5. Analysing semiconductor manufacturing big data for root cause detection of excursion for yield enhancement;International Journal of Production Research,2015
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献