Advanced Signal Processing

Das et al. [37] studied the coefficient of friction signal in order to determine the end-point detection more effectively. As shown in Fig. 4.35, the raw COF data were analyzed and the noise levels were filtered out to more precisely determine the end point of the process. The variance sequential probability ratio test (SPRT) method was adopted to analyze and filter the raw data. The coefficient of friction signal can thus be processed and analyzed to detect the end point more effectively. 

FIGURE 4.35 Variance SPRT for the patterned copper CMP process (from Ref 37). 

FIGURE 4.36 Energy from online analysis of the in-control and out-of-control AE data (from Ref. 38). 

FIGURE 4.37 Joint time-frequency domains of the AE signal for slurry 1 (a) and slurry 2 (b). The AE signal was filtered by a Debouche 05 wavelet filter. Only middle bands were selected and processed by the marching pursuit joint time-frequency domain algorithm (from Ref 27). 

Tribometrology is a science of precise, repeatable, and reproducible measurements of friction and wear (material removal) as long as the conditions of the test are accurately replicated. Coefficient of friction, acoustic emission signal, 

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SV Vaseghi. Advanced Signal Processing and Digital Noise Reduction. John Wiley Sons, New York, NY, 1996. 

Savitzky, A. and Golay, M.J.E. (1964) Anal. Chem., 36, 1627. Vaseghi, S.V. (1996) Advanced Signal Processing and Digital Noise Reduction, Wiley-Teubner, Chichester - Leipzig. 

E. Deprettere. Example of combined algorithm development and architecture design. Proc. Advanced Signal Processing Algorithms, Architectures, and Implementations III, San Diego, California, July 1992. 

Nonlinear Optical Systems Principles, Phenomena, and Advanced Signal Processing, Le Nguyen Binh and Dang Van Liet... 

Introduction Underwater Propagation Underwater Sound Systems Components and Processes Signal Processing Functions Advanced Signal Processing Application... 

Advanced signal processing techniques provides more elaborated approaches to the analysis and interpretation. In our study, we continue to promote the use of a continuous wavelet transform. We believe that its higher computational complexity are comparable to the Fourier transformation but the continuous wavelet transform makes the convenient assessment of amplitude, scale and phase of signal oscillations. Wavelet transform allows us to isolate a given structure in time and frequency space. Let us define the wavelet trans-... 

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