A monitoring method of semiconductor manufacturing processes using Internet of Things–based big data analysis. (July 2017)

• Record Type: Journal Article
• Authors: Jang, Seok-Woo; Kim, Gye-Young
• Is Part Of: International journal of distributed sensor networks. Volume 13:Number 7(2017)
• Publication Date: 2017-07
• Subjects: Monitoring -- learning -- prediction -- matched pattern -- Internet of Things -- reference model; Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1177/1550147717721810
• Languages: English
• ISSNs: 1550-1329

Abstract

This article proposes an intelligent monitoring system for semiconductor manufacturing equipment, which determines spec-in or spec-out for a wafer in process, using Internet of Things–based big data analysis. The proposed system consists of three phases: initialization, learning, and prediction in real time. The initialization sets the weights and the effective steps for all parameters of equipment to be monitored. The learning performs a clustering to assign similar patterns to the same class. The patterns consist of a multiple time-series produced by semiconductor manufacturing equipment and an after clean inspection measured by the corresponding tester. We modify the Line, Buzo, and Gray algorithm for classifying the time-series patterns. The modified Line, Buzo, and Gray algorithm outputs a reference model for every cluster. The prediction compares a time-series entered in real time with the reference model using statistical dynamic time warping to find the best matched pattern and then calculates a predicted after clean inspection by combining the measured after clean inspection, the dissimilarity, and the weights. Finally, it determines spec-in or spec-out for the wafer. We will present experimental results that show how the proposed system is applied on the data acquired from semiconductor etching equipment.