Line-Based Endpoint Detection for Thin Lamella Preparation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Endpoint detection in lamella preparation for charged particle microscopes is challenging, especially for thin samples less than 10 nm thick, as existing techniques rely on sample characteristics that may be difficult to image or lack defining features, making automation and precise detection difficult.
Innovation Solution
The use of lines formed on the sample surface with relative spatial characteristics, such as equal depth or distance between lines, to determine the endpoint of material removal, which can be monitored manually or using computer algorithms, allowing for agnostic detection across various sample types and microscopes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If electron-based images are used for endpoint detection, then the milling process can be monitored, but the detection precision is insufficient for thin lamellae less than 10 nm thick
Solution Approach 1:
The patent introduces reference lines as an intermediary element that mediates between the ion beam milling process and the electron beam detection system. These lines provide stable, high-contrast features that are easily detectable by electron imaging, serving as a reliable reference framework that overcomes the limitations of direct sample feature detection for thin lamellae.
Solution Approach 2:
The patent creates a copy of the sample surface structure by forming reference lines that replicate the surface topology. These lines serve as a surrogate reference framework that can be reliably detected and measured, providing a copy of the spatial relationships needed for accurate endpoint detection without requiring direct detection of the thin lamella features themselves.
2Adaptability or versatility
If sample characteristics are used for endpoint detection, then detection can be performed, but the method is not versatile for samples with no structures or poor defining features
Solution Approach 1:
The patent performs preliminary action by forming reference lines on the sample surface before the milling process begins. These lines are created in advance as a stable reference framework that will remain throughout the milling process, enabling consistent endpoint detection regardless of the sample's initial structural characteristics or the presence of defining features.
Solution Approach 2:
The reference line framework serves multiple functions: it provides spatial reference for endpoint detection, enables measurement of milling depth and rate, and works universally across different sample types including those without distinct structures. This multi-functional reference system eliminates the need for sample-specific detection strategies.
3Extent of automation
If manual monitoring of line characteristics is used, then endpoint detection is possible, but the automation extent is limited
Solution Approach 1:
The patent implements feedback by continuously monitoring the positions and characteristics of reference lines during the milling process. The system compares the actual line positions against the expected positions based on the reference framework, providing real-time feedback that enables automated endpoint detection and control of the milling process.
Solution Approach 2:
The patent replaces manual mechanical monitoring with automated image processing and analysis systems. Computer algorithms automatically detect the reference lines in electron micrographs, calculate their positions and characteristics, and determine the endpoint based on predefined criteria, substituting manual operation with automated computational 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 method provides a self-calibrating and accurate endpoint detection technique that is insensitive to sample material and beam variations, enabling reliable automation and precise control of the milling process, even for samples with no distinct structures.
Implementation Method 1
Such lamellas are formed by using the ion beam to mill away material from both sides of the sample
Implementation Method 2
imaging, a plurality of times, the sample to at least capture the plurality of lines
Data Source
AI summary
Apparatuses and methods directed toward endpoint detection are disclosed herein. An example method at least includes forming a plurality of lines on a top surface of a sample; removing, a plurality of times, material from a working surface of the sample, the working surface different than the top surface; imaging, a plurality of times, the sample to at least capture the plurality of lines; and determining an endpoint based on a relative spatial characteristic between two or more lines of the plurality of lines.