Lithographic Metrology Sampling Scheme Using Noise Propagation
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Solution Overview
Problem
The challenge of accurately measuring and positioning patterns on substrates in lithographic processes is hindered by the time-consuming nature of extensive measurement operations, which limit throughput and increase costs, particularly with advanced alignment models requiring numerous measurements.
Innovation Solution
A method is developed to configure a sampling scheme for metrology operations by obtaining metrology data, fitting a model, evaluating it on an evaluation grid, and calculating a noise propagation coefficient for candidate measurement locations to optimize the sampling scheme, allowing for efficient measurement point selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extensive measurement operations are performed to achieve accurate pattern placement, then measurement precision is improved, but productivity deteriorates due to time-consuming operations
Solution Approach 1:
The patent extracts only the most informative measurement locations from the complete set of available targets on the substrate. By calculating a noise propagation coefficient for each candidate location and selecting only those with coefficients below a threshold, the system extracts the essential measurement information while discarding redundant measurements, thus reducing measurement time while maintaining accuracy
Solution Approach 2:
The patent changes the parameter of measurement selection from a fixed comprehensive set to a dynamically optimized subset based on noise propagation characteristics. By using the noise propagation coefficient as a selection criterion, the system adapts the measurement scheme to the specific distortion characteristics of each wafer, achieving accurate placement with fewer measurements
2Manufacturing precision
If advanced alignment models are used to correct non-linear distortions, then manufacturing precision is improved, but device complexity increases due to the need for numerous measurements
Solution Approach 1:
The patent extracts only the critical measurement locations needed to characterize non-linear distortions for advanced alignment models. By selecting measurement points based on noise propagation coefficients, the system obtains sufficient data for complex distortion correction without requiring comprehensive measurement of all available targets, thus simplifying the measurement scheme while maintaining manufacturing precision
Solution Approach 2:
The patent applies partial action by performing measurements at only a subset of available locations rather than all possible targets. The noise propagation coefficient calculation identifies the minimum necessary measurements to capture non-linear distortion characteristics, avoiding excessive measurements while still providing sufficient data for advanced alignment model correction
3Measurement precision
If the number of measurement locations is increased to improve model accuracy, then measurement precision is improved, but loss of time increases due to extended measurement duration
Solution Approach 1:
The patent extracts the essential measurement information from a larger set of candidate locations by using noise propagation coefficients as a selection criterion. This allows the system to achieve model accuracy comparable to comprehensive measurements while spending time only on the most informative locations, thus reducing total measurement time while maintaining model accuracy
Solution Approach 2:
The patent performs preliminary calculation of noise propagation coefficients for all candidate measurement locations before actual measurements are taken. This preliminary action allows the system to pre-identify the optimal subset of measurement locations, ensuring that time is spent only on measurements that will most contribute to model accuracy, thereby reducing overall measurement time
Data Source
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AI summary
Disclosed is a method of configuring a sampling scheme for a metrology operation comprising: obtaining metrology data relating to measurements on a substrate; fitting a first model to the metrology data to obtain a first fitted model; evaluating the first fitted model on an evaluation grid; calculating a noise propagation coefficient for each candidate measurement location of a plurality of candidate measurement locations based on said evaluation, and configuring the sampling scheme based on the calculation step.