Automated Probe Placement for Image Pattern Modeling
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Solution Overview
Problem
Existing pattern detection methods in image processing struggle with accurately placing probes on patterns with unbalanced orientation profiles, particularly failing to effectively model elongated rectangular shapes due to uniform probe placement strategies that lead to inadequate representation of short edges, resulting in difficulty in alignment and location determination.
Innovation Solution
An automated probe placement module that balances probe distribution by determining a target distribution of probes based on the histogram of perpendicular orientations, adjusting the number of probes on edges to ensure balanced information extraction across all degrees of freedom, regardless of pattern shape, using mechanisms such as balancing perpendicular orientations and sampling from a target distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If uniform probe placement strategy is used, then device complexity is reduced and ease of operation is improved, but measurement precision deteriorates for patterns with unbalanced orientation profiles
Solution Approach 1:
The patent applies local quality by varying probe placement density according to the local orientation characteristics of different pattern regions. The probe distribution is adapted to match the histogram of perpendicular orientations, placing more probes in regions with under-represented orientations and fewer probes in regions with over-represented orientations. This localized adaptation improves measurement precision without requiring complete redesign of the entire probe placement system.
Solution Approach 2:
The patent changes the parameter of probe distribution from uniform to non-uniform based on the orientation histogram. By computing the histogram of perpendicular orientations and using it to determine probe placement density, the system dynamically adjusts probe parameters (position and density) to match the pattern's orientation profile, thereby improving detection accuracy for elongated and irregular shapes.
2Loss of information
If uniform probe distribution is used across all pattern edges, then ease of manufacture is improved, but information completeness deteriorates for elongated rectangular shapes
Solution Approach 1:
The patent applies preliminary action by computing the histogram of perpendicular orientations before actual probe placement. This pre-analysis of the pattern's orientation distribution allows the system to determine the optimal probe distribution strategy in advance, ensuring that all orientation information is captured before the probing process begins, thus preventing information loss.
Solution Approach 2:
The patent uses feedback from the orientation histogram to adjust probe placement density. The histogram serves as feedback information about the pattern's orientation characteristics, which is then used to modify the probe distribution accordingly. This feedback mechanism ensures that probe placement is optimized for the specific pattern being analyzed, improving information completeness.
3Measurement precision
If probes are placed uniformly on all edges, then productivity is improved by simplifying the process, but measurement precision deteriorates for short edges of elongated patterns
Solution Approach 1:
The patent applies local quality by identifying short edges through orientation histogram analysis and concentrating probe placement on these under-represented regions. The system locally adapts probe density to match the specific geometric characteristics of the pattern, ensuring sufficient sampling on short edges while maintaining overall process efficiency through automated determination of probe distribution.
Solution Approach 2:
The patent applies partial action by focusing probe placement efforts on specific critical regions (short edges and under-represented orientations) rather than uniformly distributing probes across all edges. This selective concentration of probes on critical areas improves measurement precision for problematic regions without requiring excessive probes everywhere, thus maintaining productivity.
Data Source
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AI summary
The present application discloses a probe placement module for placing probes on a virtual object depicted in an image. The probe placement module is configured to place probes on interest points of an image so that the probes can accurately represent a pattern depicted in the image. The probe placement module can be configured to place the probes so that the probes can extract balanced information on all degrees of freedom associated with the pattern's movement, which improves the accuracy of the model generated from the probes.