Sample Scheme Optimization for Reduced Lithography Mark Readout
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Solution Overview
Problem
Conventional methods for mark readout in lithographic apparatuses are limited in reducing the number of marks read out without compromising test performance, leading to longer setup, recovery, and drift calibration/verification times.
Innovation Solution
A method involving statistical analysis of measurement data to determine different groups of locations and configuring a sample scheme generation algorithm using a multi-objective genetic algorithm to optimize the number of marks read out, while maintaining key performance indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If the number of marks read out is reduced, then the setup, recovery, and drift calibration/verification times are shortened, but the accuracy of calculated key performance indicators deteriorates
Solution Approach 1:
The patent divides the mark readout process into different groups or categories, allowing selective reading of marks based on their statistical characteristics. This segmentation enables the system to focus on the most informative marks while skipping redundant ones, thereby reducing total readout time while maintaining KPI accuracy.
Solution Approach 2:
The patent implements partial action by reading out only a subset of marks rather than all marks. By using statistical analysis to identify which marks are most critical for accurate KPI calculation, the system performs sufficient (but not excessive) mark reading to achieve the required measurement precision with reduced time loss.
2Loss of time
If statistical analysis and optimization algorithms are implemented, then the number of marks read out is reduced, but the device complexity increases
Solution Approach 1:
The patent performs preliminary statistical analysis of mark characteristics before the actual mark readout process. By pre-grouping marks based on their statistical properties and determining optimal sampling schemes in advance, the system reduces the computational burden during time-critical operations, balancing algorithm complexity with time efficiency.
Solution Approach 2:
The system uses its own measurement data to automatically determine optimal sampling strategies without requiring external intervention. The statistical analysis and optimization algorithms self-adjust based on the specific characteristics of the marks being measured, reducing the need for manual configuration and simplifying operation despite the underlying complexity.
Data Source
AI summary
A method for sample scheme generation includes obtaining measurement data associated with a set of locations; analyzing the measurement data to determine statistically different groups of the locations; and configuring a sample scheme generation algorithm based on the statistically different groups. A method includes obtaining a constraint and/or a plurality of key performance indicators associated with a sample scheme across one or more substrates; and using the constraint and/or plurality of key performance indicators in a sample scheme generation algorithm including a multi-objective genetic algorithm. The locations may define one or more regions spanning a plurality of fields across one or more substrates and the analyzing the measurement data may include stacking across the spanned plurality of fields using different respective sub-sampling.