Weighted Multi-Mark Substrate Measurement for Lithography Alignment
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Solution Overview
Problem
Existing methods for measuring substrates in lithography processes face challenges in maintaining high alignment and overlay accuracy due to changes in substrate characteristics, which can degrade the intensity and quality of detection signals from measurement marks, leading to reduced measurement accuracy.
Innovation Solution
A method involving the capture of images from multiple marks on a substrate, where evaluation values are processed with weights to acquire information about the substrate's state, allowing for improved measurement accuracy by adjusting for changes in mark characteristics and optimizing signal intensity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single measurement mark is used for substrate measurement, then the measurement process is simple, but measurement accuracy degrades when substrate characteristics change
Solution Approach 1:
The measurement mark is divided into multiple segments (first mark and second mark) with different characteristics. Each segment responds differently to substrate characteristic changes, allowing the system to select or weight segments based on current substrate conditions to maintain measurement accuracy.
Solution Approach 2:
The invention changes the parameters of measurement marks by creating multiple marks with different characteristics (e.g., different patterns, sizes, or positions). This allows the measurement system to adapt to substrate characteristic changes by selecting or weighting marks based on their current detection signal quality.
2Measurement precision
If multiple measurement marks with different characteristics are used, then measurement accuracy is maintained under varying substrate conditions, but the complexity of processing measurement data increases
Solution Approach 1:
The invention implements a feedback mechanism where detection signal characteristics (intensity, quality) are continuously monitored for each mark. Based on this feedback, the system automatically weights or selects marks to optimize measurement accuracy, and this weighting information is fed back into the measurement calculation process.
Solution Approach 2:
The measurement system dynamically adjusts the weighting or selection of different marks based on real-time detection signal characteristics. This dynamic adaptation allows the system to maintain high measurement accuracy under varying substrate conditions without requiring complex manual intervention.
3Reliability
If detection range is expanded to capture more mark information, then measurement robustness improves, but signal intensity and quality may decrease
Solution Approach 1:
Instead of uniformly processing all mark information, the invention applies local quality assessment by evaluating detection signal characteristics (intensity, quality) for each individual mark. This allows the system to identify and weight high-quality local measurements while down-weighting or excluding low-quality measurements, thereby maintaining both robustness and precision.
Data Source
AI summary
A measuring method of measuring a substrate includes capturing images of a plurality of marks provided on the substrate, and processing a plurality of evaluation values while giving weights to the plurality of evaluation values respectively obtained from the images of the plurality of marks captured in the capturing, thereby acquiring information indicating a state of the substrate. The plurality of marks include at least two marks simultaneously captured in the capturing.