Next Article in Journal
Measurement of Fluorescence in a Rhodamine-123 Doped Self-Assembled “Giant” Mesostructured Silica Sphere Using a Smartphone as Optical Hardware
Previous Article in Journal
An Asynchronous Multi-Sensor Micro Control Unit for Wireless Body Sensor Networks (WBSNs)
Article Menu

Export Article

Open AccessArticle
Sensors 2011, 11(7), 7037-7054; doi:10.3390/s110707037

Real-Time Fault Classification for Plasma Processes

Department of Power Mechanical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan
*
Author to whom correspondence should be addressed.
Received: 19 May 2011 / Revised: 28 June 2011 / Accepted: 30 June 2011 / Published: 6 July 2011
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [667 KB, uploaded 21 June 2014]   |  

Abstract

Plasma process tools, which usually cost several millions of US dollars, are often used in the semiconductor fabrication etching process. If the plasma process is halted due to some process fault, the productivity will be reduced and the cost will increase. In order to maximize the product/wafer yield and tool productivity, a timely and effective fault process detection is required in a plasma reactor. The classification of fault events can help the users to quickly identify fault processes, and thus can save downtime of the plasma tool. In this work, optical emission spectroscopy (OES) is employed as the metrology sensor for in-situ process monitoring. Splitting into twelve different match rates by spectrum bands, the matching rate indicator in our previous work (Yang, R.; Chen, R.S. Sensors 2010, 10, 5703-5723) is used to detect the fault process. Based on the match data, a real-time classification of plasma faults is achieved by a novel method, developed in this study. Experiments were conducted to validate the novel fault classification. From the experimental results, we may conclude that the proposed method is feasible inasmuch that the overall accuracy rate of the classification for fault event shifts is 27 out of 28 or about 96.4% in success. View Full-Text
Keywords: process/equipment fault detection; fault classification; optic emission spectrum (OES) process/equipment fault detection; fault classification; optic emission spectrum (OES)
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Yang, R.; Chen, R. Real-Time Fault Classification for Plasma Processes. Sensors 2011, 11, 7037-7054.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top