Learned Curve Alignment for Cross-Equipment Analysis Data
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Solution Overview
Problem
The integration of inspection data from different microscopic observation apparatuses is challenging due to differences in specifications, characteristics, and operating environments, requiring manual curve adjustments by operators, which is time-consuming and labor-intensive.
Innovation Solution
A curve alignment method and apparatus that automatically adjusts and aligns curves by learning from previous operator alignments, using a data retrieving apparatus, storage, and processor to record and apply feature data for alignment operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual curve adjustment is performed by operators to align curves from different equipment, then curve alignment accuracy is improved, but processing time and labor intensity increase
Solution Approach 1:
The system performs self-service by automatically aligning curves using learned alignment models without requiring manual intervention for each alignment task. The model learns from previously aligned curve pairs and automatically applies the alignment transformation to new curves, eliminating the need for operators to manually adjust each curve alignment.
Solution Approach 2:
The system creates a copy of the alignment process by learning from historical alignment operations. The alignment model stores and replicates the transformation parameters from previous manual alignments, allowing automatic reproduction of the same alignment results without repeating the manual adjustment process.
2Measurement precision
If manual curve adjustment is performed by operators to align curves from different equipment, then curve alignment accuracy is improved, but labor intensity increases
Solution Approach 1:
The system performs self-service by automatically aligning curves using learned alignment models without requiring manual intervention for each alignment task. The model learns from previously aligned curve pairs and automatically applies the alignment transformation to new curves, eliminating the need for operators to manually adjust each curve alignment.
Solution Approach 2:
The system replaces the mechanical manual adjustment process with an automated computational model. Instead of operators physically adjusting curves through graphical user interfaces, the alignment model computationally applies transformation parameters to achieve the same alignment results, substituting human labor with automated processing.
3Productivity
If automatic curve alignment is implemented by learning from previous alignments, then processing speed is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary action by pre-learning alignment models from historical data before actual alignment tasks are performed. The model is trained in advance on previously aligned curve pairs, storing the transformation relationships. When new curves need alignment, the pre-learned model is simply applied without requiring complex real-time analysis or learning.
Data Source
AI summary
A curve alignment method and apparatus are provided. In the method, data obtained by at least one equipment analyzing a test sample is retrieved to generate test curves. In response to an alignment operation of directing a first point around a first curve to a second point around a second curve among the test curves, a correspondence between features corresponding to the first and second points is recorded, and correspondences of alignment operations are collected as feature data. Data obtained by the equipment analyzing a current sample is retrieved to generate current curves, and a third point matching the first feature on a third curve and a fourth point matching the second feature on a fourth curve are searched according to the correspondences. At least one of the third curve and the fourth curve is adjusted to align the third point with the fourth point.