Fault detection of plasma etchers using optical emission spectra

摘要

The objective of this paper is to investigate the suitability ofnusing optical emission spectroscopy (OES) for the fault detection andnclassification of plasma etchers. The OES sensor system used in thisnstudy can collect spectra at up to 512 different wavelengths. Multiplenscans of the spectra are taken from a wafer, and the spectra data arenavailable for multiple wafers. As a result, the amount of the OES datanis typically large. This poses a difficulty in extracting relevantninformation for fault detection and classification. In this paper, wenpropose the use of multiway principal component analysis (PCA) tonanalyze the sensitivity of the multiple scans within a wafer withnrespect to typical faults such as etch stop, which is a fault thatnoccurs when the polymer deposition rate is larger than the etch rate.nSeveral PCA-based schemes are tested for the purpose of fault detectionnand wavelength selection. A sphere criterion is proposed for wavelengthnselection and compared with an existing method in the literature. Tonconstruct the final monitoring model, the OES data of selectednwavelengths are properly scaled to calculate fault detection indices.nReduction in the number of wavelengths implies reduced cost fornimplementing the fault detection system. All experiments are conductednon an Applied Materials 5300 oxide etcher at Advanced Micro Devicesn(AMD) in Austin, TX