Microscopy Interface Positioning for Atomic-Scale Layer Measurement
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Solution Overview
Problem
Existing methods for measuring interfaces in materials observed by microscopy struggle to distinguish between chemical and structural interfaces and lack atomic spatial resolution, failing to accurately measure layer thicknesses, especially in ultrathin layers.
Innovation Solution
A method for processing multidimensional microscopy data involves normalization, segmentation, standard deviation calculation, and thresholding to identify structural interfaces, followed by rectification to achieve precise interface positioning and thickness measurement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional microscopy methods are used to measure interfaces, then the measurement process is simple, but the measurement precision is insufficient and cannot distinguish between chemical and structural interfaces
Solution Approach 1:
The patent segments the interface measurement problem into distinct components: chemical interface detection through compositional analysis and structural interface detection through crystallographic orientation analysis. This segmentation allows each type of interface to be measured with specialized algorithms optimized for its unique characteristics, thereby improving measurement precision while managing complexity through modular processing approaches.
Solution Approach 2:
The patent transitions from conventional 2D image analysis to 4D data processing by incorporating spatial coordinates (x, y) with directional information (azimuthal angle, radial distance) in reciprocal space. This dimensional expansion enables simultaneous extraction of both chemical and structural interface information from the same dataset, achieving high-precision positioning without requiring separate measurement systems.
2Measurement precision
If conventional interface measurement methods are used, then the processing time is short, but the measurement precision for ultrathin layers is insufficient
Solution Approach 1:
The patent implements preliminary action by performing fast Fourier transform and directional filtering operations on the entire dataset before interface extraction. This pre-processing establishes a robust framework that enables rapid identification of ultrathin layers during the measurement phase, reducing processing time while maintaining high precision through the预先 established computational structure.
Solution Approach 2:
The patent dynamically adjusts processing parameters based on the detected interface characteristics and layer thickness. For ultrathin layers, the algorithm automatically optimizes filtering thresholds and sampling rates, allowing high-precision measurement without excessive processing time by adapting the computational effort to the specific measurement requirements.
3Measurement precision
If high-resolution microscopy is used to achieve atomic spatial resolution, then the measurement precision improves, but the computational resources required increase
Solution Approach 1:
The patent extracts only the essential information needed for interface positioning and thickness measurement from the complete high-resolution microscopy dataset. By selectively extracting chemical composition data and crystallographic orientation data through targeted filtering and transformation operations, the method achieves atomic spatial resolution while discarding redundant information, thereby reducing computational resource consumption.
Solution Approach 2:
The patent creates simplified representations of the atomic-scale interface structures through computational models that replicate the essential features without requiring full-resolution processing of the entire dataset. These computational copies enable high-precision measurement of interface positions and thicknesses using reduced computational resources compared to direct analysis of the complete high-resolution data.
Data Source
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AI summary
This device is configured to implement, for the processing of at least one microscopy image of said sample forming an input data block, modules of: -normalization (20) of the input data block to obtain a normalized data block, the normalization including an adjustment of the contrast of the input data block; -segmentation (24) of the normalized data block allowing to determine a first data block representative of the first homogeneous area and a second data block representative of the second homogeneous area, such that the sum point by point of the values of the first data block and the second data block is equal to the same value; -calculation (26) of a standard deviation data block between said first and second data blocks and thresholding of the standard deviation data block, to obtain the structural interface between the first homogeneous area and the second homogeneous area.