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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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