Charged Particle Microscopy Data Classification
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Solution Overview
Problem
Current charged particle microscopy methods face inefficiencies in data acquisition and processing, particularly in accurately determining sample information from detector signals.
Innovation Solution
A method involving a charged particle microscope that scans a sample with a charged particle beam, uses a detector to obtain signals, and processes these signals by comparing them to pre-defined data class elements to determine probabilities and assign sample information values, enabling rapid and confident data representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data processing methods are used in charged particle microscopy, then the analysis can be performed with conventional algorithms, but the data processing speed and accuracy are insufficient
Solution Approach 1:
The patent transforms the data processing approach by changing the parameter representation from continuous spectral analysis to discrete probability-based classification. Each data class element represents a discrete state with associated probability values, allowing rapid comparison and decision-making while maintaining high accuracy through probabilistic confidence measures.
Solution Approach 2:
The patent pre-calculates and stores data class elements with expected detector signals and corresponding sample information values before actual data acquisition. This preliminary preparation creates a reference framework that enables rapid matching during analysis, significantly reducing processing time while maintaining accuracy through pre-established probability relationships.
2Measurement precision
If detailed spectral analysis is performed on detector signals, then accurate material identification can be achieved, but the processing time increases significantly
Solution Approach 1:
The patent segments the continuous spectral data into discrete data class elements, each representing a specific material or compositional state. This segmentation allows the system to compare detector signals against predefined classes rather than performing exhaustive continuous spectral analysis, dramatically reducing processing time while maintaining identification accuracy through the structured classification framework.
Solution Approach 2:
The patent creates simplified copies of spectral data in the form of data class elements that capture the essential characteristics of materials without containing the full spectral complexity. These copied representations enable rapid comparison and matching during analysis, reducing processing time while preserving the critical information needed for accurate material identification.
3Reliability
If probabilistic classification is implemented to provide confidence measures, then output data reliability improves, but the complexity of data processing increases
Solution Approach 1:
The patent introduces probability values as a new parameter dimension to represent confidence in material identification. Each data class element is associated with probability values that quantify the likelihood of matching the detected signal. This parameter addition enhances reliability by providing confidence measures while the discrete, structured nature of the probability framework keeps processing complexity manageable through efficient algorithms.
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
This approach enhances data processing speed and accuracy, providing users with confident output data through improved representation of sample information, including the use of probabilistic classifiers and color encoding for complex signals.
Implementation Method 1
Irradiation of a specimen by a scanning electron beam precipitates emanation of 'auxiliary' radiation from the specimen, in the form of secondary electrons
Implementation Method 2
backscattered electrons are detected by a solid state detector in which each backscattered electron is amplified as it creates many electron-hole pairs in a semiconductor detector
Implementation Method 3
X-rays and cathodoluminescence (infrared, visible and/or ultraviolet photons)
Data Source
AI summary
The invention relates to a method of examining a sample using a charged particle microscope, comprising the steps of providing a charged particle beam, as well as a sample, and scanning said charged particle beam over at least part of said sample. A first detector is used for obtaining measured detector signals corresponding to emissions of a first type from the sample at a plurality of sample positions. According to the method, a set of data class elements is provided, wherein each data class element relates an expected detector signal to a corresponding sample information value. The measured detector signals are processed, and processing comprises comparing said measured detector signals to said set of data class elements; determining at least one probability that said measured detector signals belong to a certain one of said set of data class elements; and assigning at least one sample information value and said at least one probability to each of the plurality of sample positions. Finally, sample information values and corresponding probability can be represented in data.