PCA-Wavelet Coefficients for T2 Chart to Detect Endpoint in CMP Process

The development of the semiconductor industry, with advances in sensors oblige us 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 this paper, a sequence of an Acoustical emission (AE) waveform signals are collected during the progression of the CMP process will be monitored using PCA- Wavelet daubechie Coefficients based on T² chart. In order to detect the endpoint, we 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. Also, a comparative study is presented to show the out-performance of PCA-Wavelet Hotelling chart in detecting the endpoint.