PCA - Wavelet Coefficients for T2 Chart to Detect Endpoint in CMP Process
The development of the semiconductor industry, with advances in sensors oblige one to deal with large datasets do not stop increasing, while monitoring devices are becoming more and more complexes and sophisticated. As the measurement points become closer. Among the complex monitoring process, the Detection of the End of Polishing (EPD) during the Chemical Mechanical Planarization (CMP) process is considered as a critical task in semiconductor manufacturing. In order to detect the endpoint, the authors should not only to remove the noise from the obtained acoustical waveform signal, but also reduce dimensionality of monitored coefficient, by employing discrete wavelet algorithm for cleaning signals, Principal Component Analysis (PCA) for reducing dimensionality.