Air Pocket Detection in Single Crystal Materials
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Solution Overview
Problem
Current methods for detecting air pockets in single crystal materials, such as silicon ingots, are inadequate for ensuring structural integrity and product quality, as they fail to accurately identify voids before further processing or shipment, potentially leading to manufacturing failures.
Innovation Solution
A computer-implemented method and system that uses near-infrared light to capture image data, processes it to determine differences in a matrix of data units, calculates an index value based on these differences, and identifies air pockets by comparing the value to a predetermined threshold, distinguishing between circular and non-circular anomalies based on symmetry.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current detection methods are used, then detection capability is limited, but manufacturing failures occur due to missed air pocket detection
Solution Approach 1:
The image data is divided into multiple blocks, with each block processed independently to calculate local symmetry metrics. This segmentation allows the system to detect air pockets at different locations simultaneously while maintaining high measurement precision through localized analysis of each block's symmetry properties
Solution Approach 2:
The system performs multiple operations on the same image data including blocking, symmetry calculation, and index computation beyond what traditional single-pass methods do. This excessive processing action ensures reliable detection by analyzing the data from multiple angles and computing various symmetry metrics to confirm air pocket presence
2Measurement precision
If image data is processed to determine symmetry, then air pocket detection accuracy improves, but processing complexity increases
Solution Approach 1:
The invention exploits the asymmetry introduced by air pockets in otherwise symmetric crystal structures. By calculating symmetry metrics and comparing them against threshold values, the system achieves high detection accuracy while maintaining manageable processing complexity through efficient symmetry algorithms
Solution Approach 2:
The system transforms the two-dimensional image data into a symmetry metric space, adding a new dimensional perspective for analysis. By computing symmetry indices and comparing them in this transformed space, the system achieves accurate air pocket detection without requiring complex three-dimensional reconstruction or additional processing dimensions
3Measurement precision
If symmetry analysis is performed on image data, then detection precision improves, but processing time increases
Solution Approach 1:
By dividing the image into blocks and processing each block independently for symmetry analysis, the system achieves detection precision through localized symmetry metrics while reducing overall processing time through parallel processing of multiple blocks simultaneously
Solution Approach 2:
The system performs multiple symmetry calculations and index computations on the same data blocks to ensure detection precision, but this excessive action is applied selectively only to blocks containing regions of interest rather than the entire image, thereby managing processing time effectively
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
Effectively detects air pockets within single crystal materials by analyzing image data symmetry, preventing further processing of defective ingots and ensuring product reliability by identifying anomalies before they cause manufacturing issues.
Implementation Method 1
a light source configured to emit near-infrared (NIR) light toward a material, a detection device positioned adjacent to the material to capture image data based on light passing through the material
Data Source
AI summary
Methods and systems for use in detecting an air pocket in a single crystal material are described. One example method includes providing a matrix including a plurality of data units, the plurality of data units including image data related to a region of interest of the single crystal material; determining, by a processor, a difference between data units of the matrix and a corresponding data unit of the matrix, wherein the corresponding data unit is defined by a first operation of the matrix; calculating, by the processor, a first index value based on the differences of the corresponding data units; and identifying an air pocket within the single crystal material based on the first index value and a predetermined threshold.