Fault detection of plasma etchers using optical emission spectra

The objective of this paper is to investigate the suitability of using opti cal emission spectroscopy (OES) for the fault detection and classification of plasma etchers. The OES sensor system used in this study can collect spe ctra at up to 512 different wavelengths, Multiple scans of the spectra are taken from a wafer, and the spectra data are available for multiple wafers, As a result, the amount of the OES data is typically large, This poses a d ifficulty in extracting relevant information for fault detection and classi fication, In this paper, we propose the use of multiway principal component analysis (PCA) to analyze the sensitivity of the multiple scans within a w afer with respect to typical faults such as etch stop, which is a fault tha t occurs when the polymer deposition rate is larger than the etch rate, Sev eral PCA-based schemes are tested for the purpose of fault detection and wa velength selection, A sphere criterion is proposed for wavelength selection and compared with an existing method in the literature. To construct the f inal monitoring model, the OES data of selected wavelengths are properly sc aled to calculate fault detection indices, Reduction in the number of wavel engths implies reduced cost for implementing the fault detection system. Al l experiments are conducted on an Applied Materials 5300 oxide etcher at Ad vanced Micro Devices (AMD) in Austin, TX.