Lithographic Overlay Metrology via Illumination Inversion
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Solution Overview
Problem
Current dark-field image-based overlay measurements in lithographic processes are prone to inaccuracies due to feature asymmetry, which is not solely caused by overlay errors, leading to unreliable results.
Innovation Solution
A method involving illumination of target structures with deliberate overlay biases, detecting scattered radiation asymmetry under various conditions, and performing regression analysis to distinguish and isolate overlay errors from feature asymmetry contributions, using a linear regression model that may not pass through the origin.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If dark-field image-based overlay measurements are used, then measurement speed is improved, but measurement precision deteriorates due to feature asymmetry interference
Solution Approach 1:
The measurement process is segmented into multiple distinct steps: (1) obtaining a first dark-field image with first illumination, (2) obtaining a second dark-field image with second illumination, (3) calculating first asymmetry from the first image, (4) calculating second asymmetry from the second image, and (5) determining overlay by comparing asymmetries. This segmentation allows each step to be optimized independently and enables the separation of overlay measurement from feature asymmetry effects through the comparison of measurements taken under different illumination conditions.
Solution Approach 2:
Instead of directly measuring overlay and trying to eliminate feature asymmetry interference, the invention inverts the approach by measuring asymmetry under two different illumination conditions and using the comparison to isolate the overlay component. The feature asymmetry effects manifest differently under the two illumination conditions, allowing their cancellation through the asymmetry comparison, thereby inverting the problem from direct measurement to differential measurement.
2Measurement precision
If multiple measurements under different illumination conditions are performed, then measurement precision is improved by separating overlay errors from feature asymmetry, but measurement time increases
Solution Approach 1:
The invention employs periodic action by performing measurements under two different illumination conditions in sequence. The first dark-field image is obtained with first illumination, then the second dark-field image is obtained with second illumination. This periodic switching between illumination conditions enables the separation of overlay errors from feature asymmetry effects while maintaining a systematic and efficient measurement rhythm that minimizes total measurement time.
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
This approach enhances the robustness of overlay measurements by accurately separating overlay errors from feature asymmetry effects, improving measurement accuracy and reducing inaccuracies caused by non-overlay asymmetries.
Implementation Method 1
detecting radiation scattered by each target structure
Data Source
AI summary
Disclosed is a method of measuring a parameter of a lithographic process, and associated inspection apparatus. The method comprises measuring at least two target structures on a substrate using a plurality of different illumination conditions, the target structures having deliberate overlay biases; to obtain for each target structure an asymmetry measurement representing an overall asymmetry that includes contributions due to (i) the deliberate overlay biases, (ii) an overlay error during forming of the target structure and (iii) any feature asymmetry. A regression analysis is performed on the asymmetry measurement data by fitting a linear regression model to a planar representation of asymmetry measurements for one target structure against asymmetry measurements for another target structure, the linear regression model not necessarily being fitted through an origin of the planar representation. The overlay error can then be determined from a gradient described by the linear regression model.