Shift endpoint trace selection algorithm and wavelet analysis to detect the endpoint using optical emission spectroscopy

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• 作者：Sihem Ben Zakour ; Hassen Taleb
• 关键词：Dimension reduction ; OES ; plasma etching process ; wavelet analysis ; CV ; SNR
• 刊名：Photonic Sensors
• 出版年：2016
• 出版时间：June 2016
• 年：2016
• 卷：6
• 期：2
• 页码：158-168
• 全文大小：1,151 KB
• 刊物类别：Physics and Astronomy
• 刊物主题：Chinese Library of Science
  Laser Technology and Physics and Photonics
  Microwaves, RF and Optical Engineering
  Measurement Science and Instrumentation
  Optics, Optoelectronics, Plasmonics and Optical Devices
  
• 出版者：University of Electronic Science and Technology of China, co-published with Springer
• ISSN：2190-7439

文摘

Endpoint detection (EPD) is very important undertaking on the side of getting a good understanding and figuring out if a plasma etching process is done on the right way. It is truly a crucial part of supplying repeatable effects in every single wafer. When the film to be etched has been completely erased, the endpoint is reached. In order to ensure the desired device performance on the produced integrated circuit, many sensors are used to detect the endpoint, such as the optical, electrical, acoustical/vibrational, thermal, and frictional. But, except the optical sensor, the other ones show their weaknesses due to the environmental conditions which affect the exactness of reaching endpoint. Unfortunately, some exposed area to the film to be etched is very low (<0.5%), reflecting low signal and showing the incapacity of the traditional endpoint detection method to determine the wind-up of the etch process. This work has provided a means to improve the endpoint detection sensitivity by collecting a huge numbers of full spectral data containing 1201 spectra for each run, then a new unsophisticated algorithm is proposed to select the important endpoint traces named shift endpoint trace selection (SETS). Then, a sensitivity analysis of linear methods named principal component analysis (PCA) and factor analysis (FA), and the nonlinear method called wavelet analysis (WA) for both approximation and details will be studied to compare performances of the methods mentioned above. The signal to noise ratio (SNR) is not only computed based on the main etch (ME) period but also the over etch (OE) period. Moreover, a new unused statistic for EPD, coefficient of variation (CV), is proposed to reach the endpoint in plasma etches process.