Endpoint Detection in Plasma Etching Using FFT and SVM

Abstract

As semiconductor devices are highly integrated, etching control is much more difficult in semiconductor industry. Plasma etching has better performance than wet etching especially in small open area etching. In semiconductor manufacturing industry, the optical emission spectroscopy (OES) is widely used to collect data from plasma chamber. The enormous OES data need to be transformed for accutate endpoint detection. There are some research results regarding endpoint detection in plasma etching using various methods such as Principal Component Analysis (PCA), Partial Least Squares (PLS), etc. In this paper a new approach is proposed for endpoint detection of small open area wafers in plasma etching. The new approach utilizes a Fast Fourier Transform (FFT), which is an efficient algorithm to detect frequency changes in signal, and this property is applied to OES data to detect endpoint in etching process. By applying FFT, noise is reduced compared to PCA data. The data after FFT is used to detect endpoint using Support Vector Machine (SVM). The combination of FFT and SVM performs excellent in terms of endpoint detection, and the performance of proposed FFT/SVM algorithm is compared to the performance of PCA/SVM algorithm.