Back to results
US 11,537,837 B2Granted
Automated accuracy-oriented model optimization system for critical dimension metrology
AssigneeKLA-Tencor Corporation
InventorsYuerui Chen, Xin Li
Life of this patent
Estimating the term…
Abstract
Techniques and systems for critical dimension metrology are disclosed. Critical parameters can be constrained with at least one floating parameter and one or more weight coefficients. A neural network is trained to use a model that includes a Jacobian matrix. During training, at least one of the weight coefficients is adjusted, a regression is performed on reference spectra, and a root-mean-square error between the critical parameters and the reference spectra is determined. The training may be repeated until the root-mean-square error is less than a convergence threshold.
Claims19
A method comprising:
- initializing a model that includes a Jacobian matrix using a processor, wherein the initializing includes spectra fitting;
- constraining critical parameters, using the processor, with at least one floating parameter and one or more weight coefficients;
- and
- training, using the processor, a neural network to use the model, wherein the training includes:
- adjusting at least one of the one or more weight coefficients based on accuracy of the critical parameters;
- performing a regression on data for a reference spectra thereby filtering a signal in the reference spectra;
- determining a root-mean-square error between the critical parameters and the data for the reference spectra after performing the regression;
- and
- repeating the adjusting, the performing, and the determining until the root-mean-square error is less than a convergence threshold.
6 more elements
A system comprising:
- a processor in electronic communication with an electronic data storage unit and a wafer metrology tool, wherein the processor is configured to:
- initialize a model in a manner that includes spectra fitting, wherein the model includes a Jacobian matrix;
- constrain critical parameters with at least one floating parameter and one or more weight coefficients;
- and
- train a neural network to use the model, wherein the training includes:
- adjusting at least one of the one or more weight coefficients based on accuracy of the critical parameters;
- performing a regression on data for a reference spectra thereby filtering a signal in the reference spectra;
- determining a root-mean-square error between the critical parameters and the data for the reference spectra after performing the regression;
- and
- repeating the adjusting, the performing, and the determining until the root-mean-square error is less than a convergence threshold.
7 more elements
Dependencies are inferred from the claim text.