NDLI: Advanced Semiconductor Manufacturing Using Big Data

Content Provider	IEEE Xplore Digital Library
Author	Tsuda, T. Inoue, S. Kayahara, A. Imai, S.-I. Tanaka, T. Sato, N. Yasuda, S.
Copyright Year	1988
Abstract	This paper describes the development and the actual utilization of fab-wide fault detection and classification (FDC) for the advanced semiconductor manufacturing using big data. In the fab-wide FDC, the collection of equipment's big data for the FDC judgment is required; hence, we developed the equipment monitoring system that handles the data in a superior method in high speed and in real time. We succeeded in stopping equipment and lots automatically when the equipment was detected as fault condition. In addition, we developed the environment that enables immediate data collection for analysis by the data aggregation and merging functions, which extracts keys correlating to yield from the equipment's parameter. Furthermore, we succeeded in development of the high-speed and high-accuracy process control system that implemented virtual metrology and the run-to-run function for the purpose to reduce process variation.
Sponsorship	IEEE Electron Devices Society IEEE Components, Packaging, and Manufacturing Technology Society IEEE Reliability Society IEEE Solid-State Circuits Society
Starting Page	229
Ending Page	235
Page Count	7
File Size	2566765
File Format	PDF
ISSN	08946507
Volume Number	28
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Monitoring Big data Data analysis Manufacturing Fault detection Process control big data, chemical mechanical polishing (CMP) Advanced process control (APC) equipment engineering system (EES), fault detection and classification (FDC), machine-to-machine (M2M) virtual metrology (VM) big data chemical mechanical polishing (CMP) equipment engineering system (EES) fault detection and classification (FDC) machine-to-machine (M2M) non-production wafer (NPW) run-to-run (R2R)
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Condensed Matter Physics Electronic, Optical and Magnetic Materials Electrical and Electronic Engineering

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Fuzzy predictive R2R control to CMP process

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Accuracy and Real-Time Considerations for Implementing Various Virtual Metrology Algorithms

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Advanced Semiconductor Manufacturing Using Big Data

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