Substrate Mark Alignment Using Parallel Prealignment Range Detection
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Solution Overview
Problem
Conventional substrate processing methods face issues with low prealignment accuracy due to substrate outer shape detection inaccuracies and laminating errors, leading to registration marks not entering the visual field of measuring devices, which decreases throughput.
Innovation Solution
A substrate processing method involving prealignment and fine alignment, where multiple methods are used to calculate positional deviations, and the detection range of marks on the substrate is decided based on these calculations to ensure accurate alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional single-method prealignment is used, then device complexity is reduced, but alignment precision deteriorates due to substrate outer shape detection inaccuracies and laminating errors
Solution Approach 1:
The alignment process is segmented into two distinct stages: prealignment using multiple calculation methods to determine a detection range, and fine alignment within that range. This segmentation allows each stage to use optimized methods appropriate to its requirements, improving overall precision without excessive complexity.
Solution Approach 2:
The prealignment stage performs preliminary positioning and calculates a detection range before the fine alignment stage. This preliminary action narrows down the search area, ensuring that the registration mark enters the visual field of the measuring device and enabling more precise final alignment.
2Productivity
If conventional prealignment is used, then processing speed is maintained, but productivity deteriorates due to repeated search operations when marks do not enter visual field
Solution Approach 1:
The prealignment stage performs preliminary positioning and calculates a detection range before fine alignment. This ensures the registration mark is positioned within the measuring device's visual field, eliminating the need for repeated search operations and reducing mark detection time.
Solution Approach 2:
The system uses feedback from the calculated detection range to adjust the fine alignment process. By incorporating information from multiple calculation methods during prealignment, the system optimizes the detection range to ensure mark visibility, thereby improving throughput.
3Measurement precision
If single calculation method is used, then computational complexity is reduced, but measurement precision deteriorates due to inability to compensate for detection inaccuracies
Solution Approach 1:
Multiple calculation methods are merged and applied in parallel during the prealignment stage. Each method calculates positional deviation independently, and their results are combined to determine a robust detection range, improving measurement precision through redundancy and cross-validation.
Solution Approach 2:
The system creates multiple copies of the positional deviation calculation using different methods. By comparing and combining these copied calculations, the system compensates for individual method inaccuracies and achieves more reliable measurement precision.
Data Source
AI summary
A substrate processing method includes performing prealignment of a substrate, and performing fine alignment of the substrate after the performing the prealignment, wherein the performing the prealignment includes calculating a plurality of amounts of positional deviation of the substrate in parallel by a plurality of methods, and the performing the fine alignment includes deciding a detection range of a mark formed on the substrate based on the plurality of calculated amounts of positional deviation.