Automated TEM Sample Preparation Using Machine Vision
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The preparation of TEM samples is a time-consuming and labor-intensive process, particularly for thin lamellae under 100 nm, which are prone to defects like bending and over-milling, making automation challenging due to the precision required in milling, extraction, and deposition.
Innovation Solution
The use of machine vision and fiducials, combined with dual-beam charged particle systems, enables automated sample preparation by accurately determining the positions of the lamella, probe, and TEM grid, guiding the attachment and transfer processes, and employing image recognition software to direct motion and improve precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual preparation techniques are used for thin lamellae under 100 nm, then preparation quality can be maintained through operator skill, but the process becomes extremely time-consuming and labor-intensive
Solution Approach 1:
The automated preparation system performs sample preparation tasks autonomously without continuous human intervention. The system includes automated positioning, milling, and extraction capabilities that operate independently, with the machine itself executing the preparation sequence based on pre-programmed parameters, thereby eliminating the time-consuming manual operations while maintaining quality through consistent automated control
Solution Approach 2:
Manual mechanical operations by operators are replaced with automated mechanical systems. The preparation process uses computer-controlled milling mechanisms, automated sample manipulation arms, and programmable positioning systems to substitute human hands and tools, enabling faster execution while maintaining precision through digital control rather than manual skill
2Productivity
If automated preparation is implemented, then throughput and speed are improved, but precision and reliability become challenging due to the delicate nature of thin samples
Solution Approach 1:
The automated preparation system incorporates real-time feedback mechanisms including sensors that monitor sample position, milling depth, and extraction forces. This feedback loop allows the system to detect and correct deviations immediately, adjusting parameters dynamically to prevent sample damage while maintaining high-speed operation, thereby ensuring both throughput and sample integrity
Solution Approach 2:
The system performs preliminary actions such as pre-positioning the sample, pre-programming milling paths, and pre-setting extraction parameters before the actual preparation begins. This advance preparation ensures that when the automated process runs at high speed, all critical parameters are already optimized, preventing reliability issues that would arise from real-time decision-making under pressure
3Device complexity
If conventional sample preparation methods are used, then existing equipment can be utilized, but the process requires extensive operator expertise and cannot be easily automated
Solution Approach 1:
The automated preparation system is designed as a multi-functional platform that integrates sample positioning, milling, extraction, and loading capabilities into a single unified system. This universal machine can handle various sample types and preparation tasks through programmable operations, eliminating the need for multiple specialized manual tools and making the entire process automatable without requiring extensive operator expertise for each individual task
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 significantly reduces the time and expertise needed for sample preparation, increases throughput, and minimizes defects, allowing for faster analysis and return of workpieces to production lines.
Implementation Method 1
removing material surrounding a thin section using a focused ion beam
Implementation Method 2
attaching the sample probe to the thin section
Data Source
AI summary
Techniques are described that facilitate automated extraction of lamellae and attaching the lamellae to sample grids for viewing on transmission electron microscopes. Some embodiments of the invention involve the use of machine vision to determine the positions of the lamella, the probe, and/or the TEM grid to guide the attachment of the probe to the lamella and the attachment of the lamella to the TEM grid. Techniques that facilitate the use of machine vision include shaping a probe tip so that its position can be readily recognized by image recognition software. Image subtraction techniques can be used to determine the position of the lamellae attached to the probe for moving the lamella to the TEM grid for attachment. In some embodiments, reference structures are milled on the probe or on the lamella to facilitate image recognition.