Method of feature exaction from time-series of spectra to control endpoint of process
Methods and systems for using a time-series of spectra to identify endpoint of an etch process. One method includes accessing a virtual carpet that is generated from a time-series of spectra for an etch process. A polynomial with coefficients represents the virtual carpet. The method includes processing a fabrication etch process on a fabrication wafer and generating a carpet defined from a time-series of spectra while processing the fabrication etch process. While the processing the fabrication etch process and generating the carpet, comparing portions of the carpet and the virtual carpet to identify an endpoint metric of the fabrication etch process.
1. A method, comprising,
accessing a virtual carpet that is generated from a time-series of spectra for an etch process collected during a training, the training producing a polynomial with coefficients that represent the virtual carpet;
running a fabrication etch process on a fabrication wafer, such that while the fabrication etch process is performed portions of a carpet defined from a time-series of spectra is generated for the fabrication etch process;
comparing the portions of the carpet of the fabrication etch process to the virtual carpet; and
triggering an endpoint of the fabrication etch process when said comparing indicates that a desired etch metric has been reached;
wherein the portions of the carpet of the fabrication etch process are fitted to the virtual carpet to identify a virtual frame number in the virtual carpet that is correlated to a predicted value for said etch metric.
2. The method of claim 1 , wherein said portions of the carpet and the virtual carpet include frames associated with specific times.
3. The method of claim 1 , wherein the etch metric is associated with at least one of a predefined etch depth, a predefined critical dimension value, a predefined wafer bow value, a predetermined line width value, a predefined feature pitch value, a predefined features spacing value, or predetermined measurable value or a combination of two or more thereof.
4. The method of claim 1 , wherein each portion of said carpet includes at least two frames and each portion is a patch of the carpet.
5. The method of claim 1 , wherein the coefficients of the polynomial are processed by a dimensionality reduction algorithm to produce the virtual carpet.
6. The method of claim 5 , wherein the polynomial of the virtual carpet is a standardized polynomial with respective coefficients that are a superset of coefficients obtained from carpets produced during said training.
7. The method of claim 1 , wherein at least some of the coefficients of the virtual carpet are floating parameters and others are fixed or coupled to the floating parameters.
8. The method of claim 7 , wherein said floating parameters in addition to the virtual frame number are used to map to the predicted value for the etch metric.
9. The method of claim 1 , wherein the time-series of spectra is intensity spectral data associated with broadband in-situ reflectometry, or is intensity spectral data associated with Optical Emission Spectroscopy (OES), or is ellipsometric spectral data associated with broadband in-situ ellipsometry, wherein said spectral data is collected from a chamber used for etching while a feature is being etched on a wafer.
10. A method, comprising,
accessing a virtual carpet that is generated from a time-series of spectra for an etch process, a polynomial with coefficients representing the virtual carpet;
processing a fabrication etch process on a fabrication wafer; and
generating a carpet defined from a time-series of spectra while processing the fabrication etch process, and while the processing the fabrication etch process and generating the carpet, comparing portions of the carpet and the virtual carpet to identify an endpoint metric of the fabrication etch process,
wherein the portions of the carpet of the fabrication etch process are fitted to the virtual carpet to identify a virtual frame number in the virtual carpet that is correlated to a predicted value for said endpoint metric.
11. The method of claim 10 , wherein said portions of the carpet and the virtual carpet include frames associated with specific times.
12. The method of claim 10 , wherein the endpoint metric is associated with at least one of a predefined etch depth, a predefined critical dimension value, a predefined wafer bow value, a predetermined line width value, a predefined feature pitch value, a predefined features spacing value, or predetermined measurable value or a combination of two or more thereof.
13. The method of claim 10 , wherein the coefficients of the polynomial are processed by a dimensionality reduction algorithm to produce the virtual carpet.
14. The method of claim 13 , wherein the polynomial of the virtual carpet is a standardized polynomial with respective coefficients that are a superset of coefficients obtained from carpets produced during training.
15. The method of claim 10 , wherein the time-series of spectra is intensity spectral data associated with broadband in-situ reflectometry, or is intensity spectral data associated with Optical Emission Spectroscopy (OES), or is ellipsometric spectral data associated with broadband in-situ ellipsometry, wherein said spectral data is collected from a chamber used for etching while a feature is being etched on a wafer.