Title :
Fault detection in reactive ion etching systems using one-class support vector machines
Author :
Sarmiento, Tomás ; Hong, Sang J. ; May, Gary S.
Author_Institution :
Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA
Abstract :
A robust method to detect faults in reactive ion etching systems using optical emission spectroscopy data is proposed. The approach is based on one-class support vector machines (SVMs). Unlike previously proposed fault detection methods, this approach only requires data collected during normal equipment operation to be trained. The results obtained suggest that this technique can detect equipment faults with exceptional accuracy. The SVM used detected all faults, yielding a detection accuracy of 100% with zero false alarms
Keywords :
fault diagnosis; integrated circuit manufacture; production engineering computing; sputter etching; support vector machines; ultraviolet spectra; visible spectra; equipment fault detection; equipment operation; one-class support vector machines; optical emission spectroscopy data; reactive ion etching systems; zero false alarms; Circuit faults; Electrical fault detection; Etching; Fault detection; Neural networks; Particle beam optics; Plasma applications; Stimulated emission; Support vector machine classification; Support vector machines;
Conference_Titel :
Advanced Semiconductor Manufacturing Conference and Workshop, 2005 IEEE/SEMI
Conference_Location :
Munich
Print_ISBN :
0-7803-8997-2
DOI :
10.1109/ASMC.2005.1438783
