3D Reconstruction Slice Imaging With Selective Surface Mapping
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Solution Overview
Problem
Conventional charged particle microscopes face significant challenges in efficiently acquiring multiple data types, such as images, compositional, and crystalline data, for three-dimensional reconstruction, leading to prolonged processing times.
Innovation Solution
The method involves exposing subsequent surfaces of a sample, acquiring images, and comparing them to reference images using neural networks. Based on the comparison, compositional or crystalline maps are acquired for surfaces showing significant changes, while insignificant changes result in exposing new surfaces. Neural networks are also used to propagate maps to unmapped surfaces, ensuring comprehensive data coverage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If compositional or crystalline maps are acquired at every surface, then complete data coverage is achieved, but processing time increases significantly
Solution Approach 1:
The system uses neural networks to automatically analyze surface images and determine whether compositional or crystalline maps are needed, eliminating the need for manual inspection and decision-making. The neural network autonomously identifies surfaces requiring detailed mapping based on image comparison, thereby reducing processing time while maintaining complete data coverage.
Solution Approach 2:
The patent changes the parameter of map acquisition frequency from fixed (every surface) to variable (based on neural network analysis of surface changes). This allows the system to adapt the data collection strategy dynamically, acquiring maps only when necessary, thus reducing overall processing time while ensuring complete data coverage for all relevant surfaces.
2Loss of time
If compositional or crystalline maps are acquired at every n surfaces, then processing time is reduced, but data coverage becomes limited
Solution Approach 1:
The neural network autonomously determines the optimal surfaces for map acquisition by analyzing image changes, replacing the fixed periodic sampling approach. This self-service mechanism ensures that maps are acquired at surfaces where they are most needed, achieving both reduced processing time and complete data coverage.
Solution Approach 2:
The system transitions from a static, periodic map acquisition strategy to a dynamic, adaptive strategy where the decision to acquire maps is based on real-time neural network analysis of surface changes. This dynamic approach optimizes both processing time and data coverage by adjusting map acquisition frequency based on actual sample characteristics.
3Manufacturing precision
If full data sets of images and compositional/crystalline information are acquired, then reconstruction quality improves, but processing time increases into days or weeks
Solution Approach 1:
The neural network automatically identifies which surfaces require compositional or crystalline maps by comparing surface images, eliminating manual inspection and enabling rapid decision-making. This self-service approach ensures that complete data sets are acquired for all surfaces that need them, maintaining high reconstruction quality while dramatically reducing processing time from weeks to hours.
Solution Approach 2:
The patent changes the acquisition strategy from universal (all surfaces) to selective (neurally-determined surfaces), optimizing the balance between data completeness and processing time. This parameter change enables the system to acquire full data sets where needed while skipping redundant acquisitions, thereby maintaining reconstruction quality while reducing processing time from days or weeks to hours.
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 the throughput of the slice-and-view process, allowing for the generation of full, robust data sets that include multiple data types, thereby reducing processing time and improving the quality of three-dimensional reconstructions.
Implementation Method 1
expose, by the focused ion beam, a subsequent surface of a sample
Implementation Method 2
acquire an image of the subsequent surface... arranged to at least detect secondary electrons
Implementation Method 3
acquire a compositional or crystalline map... arranged to at least detect secondary electrons, x-rays, and backscattered electrons
Implementation Method 4
arranged to at least detect secondary electrons, x-rays, and backscattered electrons
Data Source
AI summary
Apparatuses and processes for generating data for three-dimensional reconstruction are disclosed herein. An example method at least includes exposing a subsequent surface of a sample, acquiring an image of the subsequent surface, comparing the image of the subsequent surface to an image of a reference surface, based on the comparison exceeding a threshold, acquiring a compositional or crystalline map of the subsequent surface, and based on the comparison not exceeding the threshold, exposing a next surface.