Automatic Inspection Region Determination for Substrate Holding Abnormality
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Solution Overview
Problem
In semiconductor manufacturing systems, existing methods for inspecting the holding state of substrates during rotation are labor-intensive and require expert intervention, as they rely on manual setting of inspection regions and thresholds, which can be inaccurate and require frequent adjustments due to changes in camera positioning or system setup.
Innovation Solution
An automatic method for determining inspection regions by analyzing density changes in images before and after rotation, using an imaging unit to capture first images, cutting out second images corresponding to properly held substrates, and determining feature quantities to set inspection regions based on upper end surfaces and density differences, thereby reducing workload and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual setting of inspection regions and thresholds is used, then expert knowledge can be applied to detect abnormalities, but the workload is labor-intensive and requires frequent adjustments
Solution Approach 1:
The system performs self-calibration by automatically determining inspection regions and thresholds through image processing and density analysis, eliminating the need for manual expert configuration. The control unit autonomously adjusts parameters based on analyzed image data, making the system self-sufficient in maintaining optimal detection settings.
Solution Approach 2:
The system performs preliminary image processing and density analysis to pre-determine inspection regions before actual abnormality detection. By预先 analyzing image characteristics and calculating density distributions, the system prepares optimal inspection parameters in advance, reducing the need for frequent manual adjustments during operation.
2Reliability
If manual setting of inspection regions is used, then specific regions can be targeted for inspection, but the settings require frequent adjustments due to camera positioning changes
Solution Approach 1:
The inspection regions are dynamically determined based on actual image density distributions rather than fixed manual settings. The control unit continuously analyzes image data to adaptively adjust inspection region boundaries and thresholds, allowing the system to automatically accommodate camera positioning changes and maintain consistent inspection quality.
Solution Approach 2:
The system changes inspection parameters (region boundaries, thresholds) based on analyzed density distributions from actual images. By using density-based parameter determination, the system automatically adapts to variations in camera positioning and lighting conditions, maintaining reliable inspection without frequent manual intervention.
3Productivity
If automated density analysis is used, then the workload is reduced and consistency is improved, but the system complexity increases
Solution Approach 1:
The control unit performs multiple functions including image processing, density analysis, inspection region determination, and threshold setting within a single integrated system. This multi-functionality achieves high automation while consolidating complexity into one component, avoiding the need for multiple separate systems.
Solution Approach 2:
The system replaces manual mechanical adjustment operations with automated image processing and computational analysis. By substituting human expert manipulation with algorithm-based density analysis and automatic parameter determination, the system achieves high productivity while managing complexity through software-based solutions rather than mechanical adjustments.
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 method automates the setting of inspection regions, reducing the need for expert intervention and improving the sensitivity and accuracy of abnormality detection in substrate holding states, thereby minimizing the risk of substrate damage and system failure.
Implementation Method 1
an imaging unit for acquiring a first image by photographing the substrate being held by the substrate holding unit from a horizontal direction
Data Source
AI summary
With regard to an inspection region for inspecting abnormality of a holding state of the substrate in an image of the substrate holding unit, (1) an upper end surface of the substrate being normally held by the substrate holding unit is confirmed, (2) based on a position of the upper end surface of the substrate that has been confirmed, a position of the inspection region in a vertical direction is determined, and (3) for a candidate of the inspection region of which the position in the vertical direction has been determined, density thereof at a rotation start time of the substrate holding unit is obtained, a horizontal position of the inspection region is determined based on a difference image integrated value, which is an integrated value of a difference absolute value with density of the same region in an initial state of the substrate holding unit.