Tomographic Imaging via Spectral Detection in Charged-Particle Microscopy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current tomographic transmission microscopy requires exceptionally thin samples, which are difficult to prepare and handle, especially for thin structures like semiconductor materials, and involves time-consuming mathematical processing to deconvolve images from different tilts, making the process inefficient and inaccurate.
Innovation Solution
A method that uses a second series of sample tilts with concurrent spectral detection to acquire spectral maps, which provide compositional data to enhance the mathematical processing of images, allowing for the construction of a composite image with reduced number of tilt values and faster data acquisition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional tomographic transmission microscopy is used, then tomographic images can be obtained, but exceptionally thin samples are required which are difficult to prepare and handle
Solution Approach 1:
The patent changes the detection parameter from conventional intensity-only detection to spectral detection, measuring the energy distribution of transmitted electrons. This spectral information provides additional constraints for image reconstruction, enabling tomographic imaging of thicker samples that would otherwise be impossible with conventional methods.
Solution Approach 2:
The patent introduces spectral detection as an intermediary measurement process between the electron beam and final image formation. By detecting the energy spectrum of transmitted electrons at different tilts, it creates additional information channels that facilitate reconstruction of thicker samples without requiring them to be exceptionally thin.
2Measurement precision
If conventional tomographic microscopy with multiple tilt angles is used, then composite images can be constructed, but time-consuming mathematical processing is required to deconvolve images
Solution Approach 1:
The patent adds the energy dimension to the traditional spatial imaging by measuring spectral distributions. This transforms the problem from deconvolving purely spatial information to reconstructing both spatial and energy-domain information simultaneously, providing additional constraints that accelerate and improve the reconstruction process.
Solution Approach 2:
The spectral information acts as feedback constraints during the iterative reconstruction process. By comparing measured spectral distributions at different tilts with those predicted from reconstructed images, the algorithm can more efficiently converge to accurate solutions, reducing processing time compared to conventional methods.
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 enables more efficient and accurate transmission charged-particle microscopy without the need for exceptionally thin samples, speeding up the data acquisition and processing by utilizing compositional information from spectral maps to distinguish between different regions in the sample.
Implementation Method 1
Directing the beam through the sample and so as to form an image of the sample at an image detector
Implementation Method 2
At each of said second series of sample tilts, using a spectral detector to accrue a spectral map of said sample
Data Source
AI summary
The invention relates to a method of performing tomographic imaging involving repeatedly directing a charged particle beam through a sample for a series of sample tilts to acquire a corresponding set of images and mathematically combining the images to construct a composite image. The latter of which consists of, at each of a second series of sample tilts, using a spectral detector to accrue a spectral map of said sample, thus acquiring a collection of spectral maps; analyzing said spectral maps to derive compositional data of the sample; and employing said compositional data in constructing said composite image.