System and method for determining endpoint in etch processes using partial least squares discriminant analysis in the time domain of optical emission spectra
First Claim
1. A method creating a predictive model of an event in an etch process using partial least squares discriminant analysis (PLS-DA) comprising:
- collecting calibration data for an etch process;
using at least a portion of the calibration data, identifying a feature associated with the event;
creating a predictor matrix, wherein the predictor matrix includes data for the feature associated with the event;
creating a response matrix, wherein the response matrix is comprised of a first discriminate variable value for the feature associated with the event; and
finding a predictive model for the event by regressing the response matrix and the predictor matrix using PLS regression.
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Abstract
The present invention is directed to a system, method and software product for creating a predictive model of the endpoint of etch processes using Partial Least Squares Discriminant Analysis (PLS-DA). Calibration data is collected from a calibration wafer using optical emission spectroscopy (OES). The data may be non-periodic or periodic with time and periodic signals may be sampled synchronously or non-synchronously. The OES data is arranged in a spectra matrix X having one row for each data sample. The OES data is processed depending upon whether or not it is synchronous. Synchronous data is arranged in an unfolded spectra matrix X having one row for each period of data samples. A previewed endpoint signal is plotted using wavelengths known to exhibit good endpoint characteristics. Regions of stable intensity values in the endpoint plot that are associated with either the etch region or the post-etch region are identified by sample number. An X-block is created from the processed OES data samples associated with the two regions of stable intensity values. Non-periodic OES data and asynchronously sampled periodic OES data are arranged in a X-block by one sample per row. Synchronously sampled periodic OES data are arranged in the X-block by one period per row. A y-block is created by assigning a discriminate variable value of “1” to OES samples associated with the class, i.e. the etch, and assigning a discriminate value of “0” to all samples not in the class, i.e. the post-etch. A b-vector is regressed from the X- and y-blocks using PLS and is used with the appropriate algorithm for processing real-time OES data from a production etch process for detecting an endpoint.
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Citations
29 Claims
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1. A method creating a predictive model of an event in an etch process using partial least squares discriminant analysis (PLS-DA) comprising:
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collecting calibration data for an etch process;
using at least a portion of the calibration data, identifying a feature associated with the event;
creating a predictor matrix, wherein the predictor matrix includes data for the feature associated with the event;
creating a response matrix, wherein the response matrix is comprised of a first discriminate variable value for the feature associated with the event; and
finding a predictive model for the event by regressing the response matrix and the predictor matrix using PLS regression. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
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16. A method creating a predictive model of an endpoint of an etch process using partial least squares discriminant analysis (PLS-DA) comprising:
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collecting data samples from a calibration wafer using optical emission spectroscopy (OES), each OES data sample having intensity values for a plurality of simultaneously sampled discrete wavelengths;
plotting an endpoint signal from the OES data samples using a plotting method for identifying a location of an endpoint transition;
identifying an etch region of stable intensity values and a post-etch region of stable intensity values on the endpoint signal;
creating a predictor matrix by arranging OES data samples associated with the etch region and the post-etch region by one OES sample in each row;
creating a response matrix with a first discriminate variable value for the OES data samples associated with the etch region and a second discriminate variable value for the OES data samples associated with the post-etch region;
finding a predictive model for the endpoint by regressing the response matrix and the predictor matrix using PLS regression; and
filtering real-time data from a production wafer using the predictive model.
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17. A method creating a predictive model of an endpoint of an etch process using partial least squares discriminant analysis (PLS-DA) comprising:
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collecting data samples from a calibration wafer using optical emission spectroscopy (OES), each OES data sample having intensity values for a plurality of simultaneously sampled discrete wavelengths;
finding a time derivative of the optical emission spectroscopy (OES) data samples;
plotting an endpoint signal from the OES data samples using a plotting method for identifying a location of an endpoint transition;
identifying an etch region of stable intensity values, a transition region of evolving intensity values and a post-etch region of stable intensity values on the endpoint signal;
creating a predictor matrix by arranging OES sample derivative values associated with the etch region, the transition region and the post-etch region by one OES sample derivative value in each row;
creating a response matrix with a first discriminate variable value for OES sample derivative values associated with the transition region and a second discriminate variable value for all other OES sample derivative values;
finding a predictive model for the endpoint by regressing the response matrix and the predictor matrix using PLS regression; and
filtering real-time data from a production wafer using the predictive model.
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18. A method creating a predictive model of an endpoint of an etch process using partial least squares discriminant analysis (PLS-DA) comprising:
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collecting data samples from a calibration wafer using optical emission spectroscopy (OES), each OES data sample having intensity values for a plurality of simultaneously sampled discrete wavelengths, wherein the OES data is synchronously sampled with a period of intensity data;
removing ambiguous and misleading OES data samples;
plotting an endpoint signal from the OES data samples using a plotting method for identifying a location of an endpoint transition;
identifying an etch region of stable intensity values and a post-etch region of stable intensity values on the endpoint signal;
creating a predictor matrix by arranging OES data samples associated with the etch region and the post-etch region by one period of OES samples in each row;
creating a response matrix with a first discriminate variable value for the OES data samples associated with the etch region and a second discriminate variable value for the OES data samples associated with the post-etch region;
finding a predictive model for the endpoint by regressing the response matrix and the predictor matrix using PLS regression; and
filtering synchronously sampled real-time periodic data from a production wafer using the predictive model.
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19. A method creating a predictive model of an event in an etch process using partial least squares discriminant analysis (PLS-DA) comprising:
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collecting data samples from a calibration wafer, each data sample having a plurality simultaneously recorded readings, said event occurring in the data samples from a calibration wafer;
previewing an event plot from the data samples using previewing method for identifying a location of an event transition;
identifying the event transition region on the event plot;
creating a predictor matrix by arranging simultaneously recorded readings associated with the event transition region and simultaneously recorded readings not associated with the event transition region, by one simultaneously recorded reading in each row;
creating a response matrix with a first discriminate variable value for the simultaneously recorded readings associated with the event transition region and a second discriminate variable value simultaneously recorded readings not associated with the event transition region;
finding a predictive model for the event by regressing the response matrix and the predictor matrix using PLS regression; and
filtering real-time data from a production wafer using the predictive model.
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20. A method creating a predictive model of an event in a chamber cleaning process using partial least squares discriminant analysis (PLS-DA) comprising:
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collecting calibration data for a chamber cleaning process;
using at least a portion of the calibration data, identifying a feature associated with the event;
creating a predictor matrix, wherein the predictor matrix includes data for the feature associated with the event;
creating a response matrix, wherein the response matrix is comprised of a first discriminate variable value for the feature associated with the event; and
finding a predictive model for the event by regressing the response matrix and the predictor matrix using PLS regression. - View Dependent Claims (21, 22, 23, 24, 25, 26)
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27. A method creating a predictive model of a fault event in a semiconductor fabricating process using partial least squares discriminant analysis (PLS-DA) comprising:
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collecting calibration data for a semiconductor fabricating process;
using at least a portion of the calibration data, identifying a feature associated with the fault event;
creating a predictor matrix, wherein the predictor matrix includes data for the feature associated with the fault event;
creating a response matrix, wherein the response matrix is comprised of a first discriminate variable value for the feature associated with the fault event; and
finding a predictive model for the event by regressing the response matrix and the predictor matrix using PLS regression. - View Dependent Claims (28, 29)
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Specification