Charged Particle Beam Scanning for Dwell Time and Image Quality
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Solution Overview
Problem
Existing charged particle beam microscopes face challenges in optimizing scanning parameters to balance image quality and acquisition time, particularly for sensitive samples like proteins, viruses, and biological specimens, leading to potential sample damage and suboptimal data quality due to inappropriate dwell times and integration modes.
Innovation Solution
A method for constructing a Charged Particle Beam microscope operational-settings prediction model that automatically selects optimal parameters by modeling the impact of scanning settings on image quality and time, using machine learning or explicit formulation, to determine the best-fit model for a specific sample class.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fixed dwell times are used for imaging, then sufficient signal can be recorded above background noise, but the sample is unnecessarily exposed to the electron beam for extended periods causing damage
Solution Approach 1:
The patent implements dynamic adjustment of dwell time based on real-time signal quality assessment. Instead of using fixed dwell times, the system continuously monitors signal-to-noise ratio and adapts the dwell time for each pixel accordingly, allowing minimum exposure time while maintaining sufficient signal quality above background noise
Solution Approach 2:
The system incorporates feedback mechanisms where the detected signal quality from preliminary scans is used to determine optimal dwell times for subsequent imaging. The dwell time is adjusted based on feedback from signal-to-noise ratio measurements, ensuring sufficient signal recording while minimizing unnecessary beam exposure and sample damage
2Measurement precision
If longer dwell times are used to improve signal quality, then better image data is obtained, but the total acquisition time increases and sample damage worsens
Solution Approach 1:
The patent applies different dwell times to different regions of the image based on local signal characteristics. Areas with strong signals use shorter dwell times while areas with weak signals use longer dwell times, optimizing the balance between image quality and acquisition time by treating each region according to its specific needs
Solution Approach 2:
The system dynamically determines dwell times based on real-time assessment of signal-to-noise ratio for different image regions. This dynamic adjustment allows the system to achieve sufficient image quality without uniformly applying long dwell times across the entire image, thereby reducing total acquisition time
3Ease of operation
If conventional fixed parameter settings are used, then the imaging process is simple to operate, but the data quality is suboptimal for specific sample types
Solution Approach 1:
The system performs self-optimization by automatically assessing signal-to-noise ratio and determining optimal imaging parameters without requiring manual user intervention. The microscope system itself conducts the optimization process, selecting dwell times and integration modes that maximize data quality for the specific sample being imaged
Solution Approach 2:
The patent implements automated adjustment of imaging parameters including dwell time, integration mode, and number of scans based on detected signal characteristics. The system changes these parameters dynamically to optimize data quality for different sample types and imaging conditions without requiring user expertise in parameter selection
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 allows for high-quality data acquisition in minimal time, reducing sample damage and improving analytical efficiency by dynamically adjusting parameters based on sample characteristics.
Implementation Method 1
Backscattered electrons (BSE) originate from the primary electron beam, which, as the name suggests, are scattered back (e.g., out of the sample) via elastic scattering upon interaction with the sample atoms
Implementation Method 2
the electrons can cause thermal expansions, structural damage, or other sample degradation
Data Source
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AI summary
A method for constructing an operational-settings prediction model, comprises: (I) setting one or more microscope operational parameters to respective initial values; (II) directing a charged particle beam onto a location of a specimen of a known sample class and imaging or analyzing the location using the most recently set microscope operational parameters while detecting emissions from the specimen; (III) repeatedly: (i) changing a value of at least one of the one or more operational parameters; (ii) directing the charged particle beam onto the location and imaging or analyzing the location using the most recently set microscope operational parameters while detecting emissions from the location; and (iii) recording the values of the microscope operational parameters and recording a value that is a measure of the detected emissions from the location; and (IV) constructing and storing a mathematical relationship, pertaining to the known sample class, between at least one variable that represents a microscope operational parameter and a variable that represents the detected emissions.