Method and system for event detection in plasma processes

Abstract

Plasma events are detected by analyzing the spectral emissions of a plasma process of a substrate. A plasma is monitored by a spectrometer which produces plasma emission data which includes an intensity value for each individual wavelength which is a large quantity of information. The plasma emission data is processed with an algorithm or combination of algorithms to reduce the quantity of the plasma emission data. A peak finding algorithm which identifies the wavelengths of light which are associated with a plasma process allowing the other wavelengths to be ignored. A data reduction algorithm provides a single value representative of the intensity of the emitted light from each peak. A noise reduction algorithm removes noise from the spectral signal by eliminating signals at wavelengths which do not exceed a threshold intensity and do not exceed a threshold wavelength span. The data may also be processed with principal component analysis to further reduce the optical emission data. By reducing the optical emission data, the neural network can provide faster data analysis. The neural network of the inventive system identifies the plasma event after being trained and can control the processing equipment when plasma events are detected.