A wavelet-based procedure for process fault detection

IEEE Transactions on Semiconductor Manufacturing

Volumn 15, Issue 1, 2002, Pages 79-90

  Lada, Emily K ^a   Lu, Jye Chyi ^b   Wilson, James R ^c  

^a NORTH CAROLINA STATE UNIVERSITY   (United States)

^b Georgia Institute of Technology   (United States)

^c North Carolina State University   (United States)

Author keywords

Monte Carlo; Process fault detection; Resampling procedures; Reversed jackknifing; Wavelets

Indexed keywords

CHEMICAL VAPOR DEPOSITION; DATA ACQUISITION; FOURIER TRANSFORMS; MONTE CARLO METHODS; PROCESS CONTROL; STATISTICAL METHODS; WAVELET TRANSFORMS;

PROCESS FAULT DETECTION;

SEMICONDUCTOR DEVICE MANUFACTURE;

EID: 0036474782     PISSN: 08946507     EISSN: None     Source Type: Journal    
DOI: 10.1109/66.983447     Document Type: Article

References (22)

1. Modeling and fusing uncertain multi-sensory data
2. Real-time diagnosis of semiconductor manufacturing equipment using a hybrid neural network expert system
3. Process fault detection based on modeling and estimation methods - A survey
4. Feature-preserving data compression of stamping tonnage information using wavelets
5. Application of wavelets and neural networks to diagnostic system development, 2, an integrated framework and its application
6. Focused local learning with wavelet neural networks
7. A study on failure prediction in a plasma reactor
8. Adapting to unknown smoothness via wavelet shrinkage
9. Simultaneous noise suppression and signal compression using a library of orthonormal bases and the minimum description length criterion
10. 4 using real time in situ mass spectrometry
11. Model selection using wavelet decomposition and applications
12. Estimation of multiresponse simulation metamodels using control variates
13. An extension of Shapiro and Wilk's W test for normality to large samples