Scatterometry Weighting Function for Undesired Diffraction Noise
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Solution Overview
Problem
Scatterometry metrology systems face challenges in accurately measuring sample attributes due to noise introduced by undesired diffraction orders, which can contaminate data and lead to inaccuracies in feature dimension measurements, especially when using large numerical apertures that capture unwanted diffraction orders.
Innovation Solution
A metrology system and method that generate a weighting function to de-emphasize spectroscopic scatterometry data associated with undesired diffraction orders, allowing for accurate attribute measurement by balancing contamination impact across a wide spectral range, using techniques like ray-tracing and rigorous coupled-wave analysis to estimate contamination intensity and generate weights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large numerical aperture is used to collect light for metrology measurements, then the measurement precision is improved, but noise from undesired diffraction orders increases
Solution Approach 1:
The patent segments the spectral data by applying wavelength-dependent weighting functions that separate desired diffraction orders from undesired ones. The weighting function divides the spectral range into regions where different diffraction orders dominate, allowing selective emphasis on clean spectral regions while suppressing contaminated regions.
Solution Approach 2:
The patent changes the parameter of spectral weighting across different wavelengths. By varying the weight assigned to each wavelength based on the predicted contamination level, the system optimizes the contribution of each spectral region to the regression analysis, thereby improving measurement precision while accounting for diffraction order contamination.
2Measurement precision
If spectroscopic scatterometry data across a wide spectral range is used, then the measurement accuracy is improved, but the impact of contamination from undesired diffraction orders increases
Solution Approach 1:
The patent implements dynamic weighting of spectral data points based on their susceptibility to contamination. The weighting function is calculated to reflect the varying reliability of different wavelength regions, allowing the regression analysis to dynamically adjust the influence of each spectral point based on predicted contamination levels from undesired diffraction orders.
Solution Approach 2:
The weighting function serves as an intermediary between the raw spectroscopic scatterometry data and the regression analysis. It mediates the contribution of each wavelength by applying appropriate weights that account for contamination, thereby protecting the measurement accuracy from the harmful effects of undesired diffraction orders while still utilizing the full spectral range.
3Measurement precision
If all captured light is used for regression analysis, then the signal-to-noise ratio is improved, but noise from undesired diffraction orders contaminates the data fitting
Solution Approach 1:
The patent applies partial action by selectively weighting only the portions of the spectral data that are least contaminated by undesired diffraction orders. Rather than using all captured light equally, the method applies differential weighting to optimize the signal-to-noise ratio while preventing contamination from degrading the feature dimension measurement accuracy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The solution enables highly accurate measurements by effectively reducing the impact of noise from undesired diffraction orders, maintaining data integrity across a wide spectral range, and improving the fit of scatterometry data to the model, thus enhancing the precision of critical dimension and other feature measurements.
Implementation Method 1
Light incident on a sample is reflected, scattered, and/or diffracted at various angles based on the structure and composition of features on a sample
Implementation Method 2
Light incident on a sample is reflected, scattered, and/or diffracted at various angles based on the structure and composition of features on a sample
Implementation Method 3
Light incident on a sample is reflected, scattered, and/or diffracted at various angles based on the structure and composition of features on a sample
Implementation Method 4
generates a weighting function for the model to de-emphasize portions of the spectroscopic scatterometry data associated with one or more wavelengths within the selected range of wavelengths at which light captured by the scatterometry tool when measuring the target is predicted to include one or more undesired diffraction orders
Data Source
Figure 1A
Figure 1B
Figure 1C
AI summary
A metrology system may receive a model for measuring one or more selected attributes of a target including features distributed in a selected pattern based on regression of spectroscopic scatterometry data from a scatterometry tool for a range of wavelengths. The metrology system may further generate a weighting function for the model to de-emphasize portions of the spectroscopic scatterometry data associated with wavelengths at which light captured by the scatterometry tool when measuring the target is predicted to include undesired diffraction orders. The metrology system may further direct the spectroscopic scatterometry tool to generate scatterometry data of one or more measurement targets including fabricated features distributed in the selected pattern. The metrology system may further measure the selected attributes for the one or more measurement targets based on regression of the scatterometry data of the one or more measurement targets to the model weighted by the weighting function.