Substrate Region Classification Using LEEPP for Defect Screening
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Solution Overview
Problem
Graphene substrates used for low-energy electron holography and similar transmission mode analytic procedures are compromised by surface contaminants and structural defects, leading to reduced imaging performance and yields.
Innovation Solution
Implementing low-energy electron point projection (LEEPP) imaging to identify and classify substrate regions for analyte deposition, using sparse and secondary scans to optimize coverage and deposition patterns, and employing a trained machine learning tool for consistent image classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If a comprehensive scan of the entire substrate is performed to ensure complete coverage, then the coverage of substrate area is improved, but the scan time increases significantly
Solution Approach 1:
The substrate is divided into multiple regions or tiles that are scanned systematically. The scanning process is segmented into passes over different portions of the substrate, allowing efficient coverage without requiring a single continuous scan of the entire area. This segmentation enables the system to achieve complete coverage while managing scan time through structured progression across substrate regions.
2Measurement precision
If the scanning resolution is increased to improve image quality, then the measurement precision is improved, but the scan time increases
Solution Approach 1:
The system performs scanning at variable resolutions depending on the requirements of different substrate regions. Instead of uniformly applying high resolution across the entire substrate, the system applies partial scanning at high resolution only where needed (such as in regions containing analyte particles), while using lower resolution scanning in other areas. This selective approach maintains measurement precision for critical regions while reducing overall scan time.
3Productivity
If substrate regions are classified to identify suitable regions for analyte deposition, then the productivity is improved by reducing scan time, but the manufacturing precision may be compromised
Solution Approach 1:
The substrate regions are classified and pre-sorted before analyte deposition to identify and select only the most suitable regions. This preliminary classification action filters out unsuitable regions (those with defects or contaminants) in advance, allowing the deposition process to focus only on high-quality regions. This approach improves productivity by reducing the search space while maintaining manufacturing precision through systematic evaluation of region suitability criteria.
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
Enhances the yield of analyte characterization data by identifying suitable substrate regions efficiently, reducing scan time, and improving image classification accuracy, thereby maintaining high-quality imaging performance.
Implementation Method 1
low-energy electron point projection (LEEPP) imaging
Data Source
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AI summary
Methods and apparatus are disclosed for classifying regions of a substrate prior to performing an analytic procedure involving analyte particles supported on the substrate. Regions of the substrate are sparsely scanned using low-energy electron point projection (LEEPP) imaging. Regions are classified as suitable for the analytic procedure (or not) based on defects visible in the respective images. Given one suitable region, neighboring regions are scanned to increase the yield of suitable regions. Based on a distribution of suitable regions, a deposition pattern is planned, and analyte is deposited according to the plan. Following deposition, suitable regions are scanned again to identify or count visible analyte molecules visible. Based on numbers of analyte molecules found, regions are earmarked for analyte characterization, e.g. by low-energy electron holography and reconstruction. A trained machine learning classifier provides consistent, accurate image classification across a range of defect types.