Microstructure Imaging Feedback for Device Fluctuation Detection
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Solution Overview
Problem
Capturing a large number of charged particle beam images for microstructure evaluation can take a long time, during which fluctuations in the imaging device due to factors like electron gun deterioration or environmental changes can superimpose unwanted information on the images, leading to incorrect material structure evaluation.
Innovation Solution
A microstructure evaluation system that includes a fluctuation evaluation unit to detect and correct long-term fluctuations caused by the imaging device by re-capturing images and comparing feature data, distinguishing between sample and device-induced fluctuations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of charged particle beam images are captured for statistically reliable microstructure evaluation, then measurement precision is improved, but loss of time increases significantly
Solution Approach 1:
The system continuously monitors feature data from captured images and compares it against reference values to detect fluctuations in real-time. When a fluctuation exceeding a predetermined threshold is detected, the system automatically identifies and excludes affected images from the evaluation dataset, providing feedback control that maintains measurement precision without requiring excessive imaging time.
Solution Approach 2:
The system performs preliminary fluctuation detection and image validation during the imaging process itself, rather than after complete data collection. By pre-identifying images affected by device fluctuations and excluding them beforehand, the system avoids wasting time processing invalid data and can determine when sufficient valid images have been collected for statistically reliable evaluation.
2Measurement precision
If the imaging period is extended to capture more images for statistical reliability, then measurement precision is improved, but reliability deteriorates due to superimposed fluctuation information
Solution Approach 1:
The system extracts and identifies feature data that reflects device fluctuations separately from feature data representing actual microstructure characteristics. By detecting fluctuations through comparison with reference images and extracting only the fluctuation components, the system can exclude their influence from the final microstructure evaluation, thereby maintaining both measurement precision and reliability even during extended imaging periods.
Solution Approach 2:
The system introduces reference images as an intermediary standard against which captured images are compared. These reference images serve as a mediator to detect and quantify device fluctuations, allowing the system to distinguish between fluctuations caused by the imaging device and actual microstructure features, thereby preserving evaluation reliability.
3Loss of time
If the number of captured images is reduced to shorten the imaging period, then loss of time is reduced, but measurement precision deteriorates
Solution Approach 1:
The system implements real-time feedback monitoring that tracks the quality and validity of captured images as they are acquired. By continuously evaluating whether each image is affected by device fluctuations and adjusting the selection of images used for evaluation accordingly, the system can achieve statistically reliable results with fewer images, thereby reducing imaging time while maintaining measurement precision.
Data Source
AI summary
A microstructure evaluation system or detecting a fluctuation caused by an imaging device, including a feature data extraction unit configured to extract first feature data from an image captured by an imaging device while changing an observation field of view on a sample; and a fluctuation evaluation unit configured to calculate a long-term fluctuation of the first feature data, return an observation field of view to a position before occurrence of the long-term fluctuation and re-capture an image when the long-term fluctuation exceeds a predetermined criterion, and calculate a difference between the first feature data of the captured image at the position before the occurrence of the long-term fluctuation and the first feature data of the re-captured image. The fluctuation evaluation unit determines that the long-term fluctuation is a fluctuation caused by the imaging device when the difference is equal to or greater than a predetermined threshold.