Abstract
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.
Highlights
IntroductionQi et al (2016) proposed to monitor profiles using generalized linear models during Phase II in which the explanatory variables can be fixed design or random arbitrary design
As the traditional statistical process control (SPC) employs a single measurement(s) of a single unit to monitor any change in mean or in variance, Monitoring profiles will gather a set of values over a range having the shape of curve to detect the variation between successive curves
Hotelling and multivariate exponentially weighted moving average (MEWMA) statistics for the selected wavelength will be calculated during the interval 50–872, at different levels of decomposition and with different wavelet types
Summary
Qi et al (2016) proposed to monitor profiles using generalized linear models during Phase II in which the explanatory variables can be fixed design or random arbitrary design They presented a new control chart based on the weighted likelihood ratio test. There is a good literature review of wavelet analysis in statistical process monitoring introduced by Ganesan et al (2004) If this profile is conforming to the target profile (a steadystate process), it will be defined as: Yi 1⁄4 f0ðxiÞ þ i ð3Þ where i index of the ith observation from n, the total number of observations; f0 is the known standard signal, taken from in control process and established in Phase I; i are independent and identically distributed normal (i.i.d.) random variables with mean zero and variance r2. The endpoint data (wavelength channels or time channels) are presented by the matrix X notation composed by four components: initial transient (IT), main etch (ME), endpoint (EP), and over etch (OE) and it is expressed as follows:
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