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Wavelet-based multiscale analysis approaches have revolutionized the tasks of signal processing, such as image and data compression. However, the scope of wavelet-based methods in the fields of statistical applications, such as process monitoring, density estimation, and defect identification, are still in their early stages of evolution. Recent literature contains some applications of wavelet-based methods in monitoring, such as tool-life monitoring, bearing defect monitoring, and monitoring of ultra-precision processes. This paper presents a novel application of a wavelet-based multiscale method in a nanomachining process [chemical mechanical planarization (CMP)] of wafer fabrication. The application involves identification of delamination defect of low-k dielectric layers by analyzing the nonstationary acoustic emission (AE) signal and coefficient of friction (CoF) signal collected during copper damascene (Cu-low k) CMP process. An offline strategy and a moving window-based strategy for online implementation of the wavelet monitoring approach are developed. Both offline and moving window-based strategies are implemented on the data collected from two different sources. The results show that the wavelet-based approach using the AE signal offers an efficient means for real-time detection of delamination defects in CMP processes. Such an online strategy, in contrast to the existing offline approaches, offers a viable tool for CMP process control. The results also indicate that the CoF signal is insensitive to delamination defect.