Endpoint in plasma etch process using new modified w-multivariate charts and windowed regression
| Autor: | Sihem Ben Zakour, Hassen Taleb |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Figuring
Polynomial regression 0209 industrial biotechnology Multivariate statistics Monitoring profiles Multivariate control charts 02 engineering and technology 01 natural sciences Industrial and Manufacturing Engineering 010104 statistics & probability Noise 020901 industrial engineering & automation Wavelet Endpoint detection Statistics ddc:650 Control chart Plasma etch process Mean-shift 0101 mathematics Windowed regression Statistic Mathematics |
| Popis: | Endpoint detection is very important undertaking on the side of getting a good understanding and figuring out if a plasma etching process is done in the right way, especially if the etched area is very small (0.1%). It truly is a crucial part of supplying repeatable effects in every single wafer. When the film being etched has been completely cleared, the endpoint is reached. To ensure the desired device performance on the produced integrated circuit, the high optical emission spectroscopy (OES) sensor is employed. The huge number of gathered wavelengths (profiles) is then analyzed and pre-processed using a new proposed simple algorithm named Spectra peak selection (SPS) to select the important wavelengths, then we employ wavelet analysis (WA) to enhance the performance of detection by suppressing noise and redundant information. The selected and treated OES wavelengths are then used in modified multivariate control charts (MEWMA and Hotelling) for three statistics (mean, SD and CV) and windowed polynomial regression for mean. The employ of three aforementioned statistics is motivated by controlling mean shift, variance shift and their ratio (CV) if both mean and SD are not stable. The control charts show their performance in detecting endpoint especially W-mean Hotelling chart and the worst result is given by CV statistic. As the best detection of endpoint is given by the W-Hotelling mean statistic, this statistic will be used to construct a windowed wavelet Hotelling polynomial regression. This latter can only identify the window containing endpoint phenomenon. |
| Databáze: | OpenAIRE |
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