Improvement of principal component analysis modeling for plasma etch processes through discrete wavelet transform and automatic variable selection

To cope with a cost-effective manufacturing approach driven by more than Moore's law era, plasma etching which is one of the major processes in semiconductor manufacturing has developed plasma sensors and their applications. Among the plasma sensors, optical emission spectroscopy (OES) has been widely utilized and its high dimensionality has required multivariate analysis (MVA) techniques such as principal component analysis (PCA). PCA, however, might devaluate physical meaning of target process during its statistical calculation. In addition, inherent noise from charge coupled devices (CCD) array in OES might deteriorate PCA model performance. Therefore, it is desirable to pre-select physically important variables and to filter out noisy signals before modeling OES based plasma data. For these purposes, this paper introduces a peak wavelength selection algorithm for selecting physically meaningful wavelength in plasma and discrete wavelet transform (DWT) for filtering out noisy signals from a CCD array. The effectiveness of the PCA model introduced in this paper is verified by comparing fault detection capabilities of conventional PCA model under the various source power or pressure faulty situations in a capacitively coupled plasma etcher. Even though the conventional PCA model fails to detect all of the faulty situations under the tests, the PCA model introduced in this paper successively detect even extremely small variation such as 0.67% of source power fault. The results introduced in this paper is expected to contribute to OES based plasma monitoring capability in plasma etching for more than Moore's law era.