Phase Analyzer Using Shared Spectra for Consistent Phase Maps
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Solution Overview
Problem
Existing phase analysis methods in scanning electron microscopes face challenges in creating consistent phase maps when combining multiple spectrum imaging data, as identical phases may be incorrectly classified as separate phases due to inconsistencies in individual phase maps.
Innovation Solution
A phase analyzer that performs multivariate analysis on multiple pieces of spectrum imaging data to acquire a representative spectrum group, which is then used for consistent phase analysis across all data sets, ensuring accurate phase classification and map integration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If individual phase maps are created for each piece of spectrum imaging data, then phase analysis can be performed on multiple data sets, but inconsistent phase classification occurs where identical phases are classified as separate phases
Solution Approach 1:
The patent segments the phase analysis process into two distinct stages: first, individual phase maps are created for each spectrum imaging data set; second, a unified phase classification is performed by comparing spectral features across all data sets to identify and merge identical phases. This segmentation allows the system to handle multiple data sets while ensuring consistent phase classification through cross-data-set comparison.
Solution Approach 2:
The patent merges the phase classification results from multiple individual phase maps by introducing a comparison mechanism that identifies identical phases across different data sets. The system combines these results into a unified phase map where phases are consistently classified across all spectrum imaging data, eliminating the inconsistency that would occur if each data set were analyzed independently.
2Reliability
If multivariate analysis is performed on all spectrum imaging data simultaneously, then consistent phase classification can be achieved, but computational complexity increases
Solution Approach 1:
The patent segments the multivariate analysis process into two stages: first, individual phase maps are generated for each spectrum imaging data set using multivariate analysis; second, a unified phase classification is achieved by comparing spectral features across the individual results. This segmentation reduces the computational complexity compared to performing multivariate analysis on all data simultaneously while still achieving consistent phase classification.
Solution Approach 2:
The patent performs preliminary multivariate analysis on each individual spectrum imaging data set to generate initial phase maps. These preliminary results are then used as the basis for the unified phase classification. By performing the computationally intensive multivariate analysis preliminarily on smaller data sets rather than on all data simultaneously, the system reduces overall computational complexity while maintaining classification consistency.
Data Source
AI summary
A phase analyzer includes a data acquisition unit that acquires a plurality of pieces of spectrum imaging data in which positions on a sample are associated with spectra which are based on signals from the sample; a first acquisition unit that acquires a first representative spectrum group for each piece of spectrum imaging data by performing multivariate analysis on each of the plurality of pieces of spectrum imaging data; a second acquisition unit that acquires a second representative spectrum group by performing multivariate analysis on the plurality of first representative spectrum groups acquired by the first acquisition unit; and a phase analysis unit that performs phase analysis on each of the plurality of pieces of spectrum imaging data by using the second representative spectrum group.