Automated Slice Milling for Feature Tracking
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Solution Overview
Problem
The existing slice-and-view technique in electron microscopy is inefficient due to the need to process large sections of samples to locate features of interest, especially for features with complex shapes like blood vessels or nerves, leading to significant time investment and extensive image data generation.
Innovation Solution
A method and apparatus that utilize machine vision to automatically determine the location and size of features, adjusting the FIB and SEM beams to make minimal cuts and image only the necessary areas, allowing for precise tracking and branching of features, and removing obstructions to improve access for imaging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large section of the sample is processed to locate the feature, then the feature location is guaranteed to be found, but the processing time and image data generation become excessive
Solution Approach 1:
The system performs preliminary actions by using the SEM to image the sample surface and identify the feature location before initiating FIB milling. This preliminary imaging step provides accurate feature location information that guides subsequent minimal milling operations, avoiding the need to process large sections of the sample.
Solution Approach 2:
The system implements feedback by continuously monitoring SEM images during the slice-and-view process to track the feature's position and adjust the FIB milling path accordingly. This real-time feedback mechanism ensures the feature is located and followed through the sample with minimal material removal, resolving the contradiction between reliable feature location and processing time.
2Reliability
If a large section of the sample is processed to locate the feature, then the feature location is guaranteed to be found, but the amount of image data generated becomes excessive
Solution Approach 1:
The system performs preliminary SEM imaging to locate the feature before FIB milling begins. This preliminary action provides the feature's precise location, allowing the system to limit subsequent imaging to only the minimal region required to follow the feature through the sample, thereby minimizing the total volume of image data generated.
Solution Approach 2:
The system extracts only the essential information needed for feature location from the sample using SEM imaging, then uses this extracted information to guide focused FIB milling and limited imaging. This extraction approach avoids generating excessive image data by processing only the relevant portion of the sample.
3Loss of information
If sequential slice milling and imaging is performed to reconstruct 3D features, then complete 3D information is obtained, but the processing time increases significantly
Solution Approach 1:
The system segments the sample processing into distinct phases: initial SEM imaging to locate the feature, followed by sequential FIB slicing with intermittent SEM imaging to track the feature's path. This segmentation allows the system to obtain complete 3D information by following only the feature's trajectory through the sample, rather than processing the entire sample volume, thereby reducing processing time.
Solution Approach 2:
The system applies partial action by performing imaging and milling operations only on the portions of the sample that contain or are adjacent to the feature of interest. Rather than processing the entire sample, the system follows the feature's path through selective slicing and imaging, obtaining complete 3D information about the feature while minimizing the total processing time.
4Reliability
If the FIB and SEM beams are directed to a large area, then the feature is guaranteed to be captured, but the area that can be imaged with the SEM is limited by its field of view
Solution Approach 1:
The system transitions from a 2D field of view limitation to a 3D solution by using FIB milling to create sequential cross-sections at different depths. The SEM images each 2D slice, and the FIB progressively removes material to reveal deeper sections. This dimensional approach allows the system to capture the entire feature path through the sample volume using the SEM's limited field of view, maintaining reliability while working within the imaging constraints.
Solution Approach 2:
The system segments the feature's 3D path into multiple 2D cross-sectional slices that can be imaged by the SEM within its field of view limitations. By milling the sample in sequential steps and imaging each slice, the system captures the complete feature trajectory through depth, overcoming the SEM's limited lateral field of view while ensuring the feature is fully captured.
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 reduces the time and material required for processing samples, enabling faster and more efficient reconstruction of 3D features by focusing on smaller, relevant areas and improving image resolution by minimizing redeposition and obstruction issues.
Implementation Method 1
the FIB cuts and slices a sample with high precision to reveal its 3D internal structures or features
Implementation Method 2
After obtaining an image of the face by the SEM
Data Source
AI summary
A method and apparatus for performing a slice and view technique with a dual beam system. The feature of interest in an image of a sample is located by machine vision, and the area to be milled and imaged in a subsequent slice and view iteration is determined through analysis of data gathered by the machine vision at least in part. A determined milling area may be represented as a bounding box around a feature, which dimensions can be changed in accordance with the analysis step. The FIB is then adjusted accordingly to slice and mill a new face in the subsequent slice and view iteration, and the SEM images the new face. Because the present invention accurately locates the feature and determines an appropriate size of area to mill and image, efficiency is increased by preventing the unnecessary milling of substrate that does not contain the feature of interest.