Defect Detection Apparatus Using Dynamic Electron Beam Scanning
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Solution Overview
Problem
Existing defect detection methods using electron beams are inefficient as they scan the entire substrate multiple times regardless of defect presence, leading to prolonged scanning times and potential missed detection of small defects due to uniform sensitivity.
Innovation Solution
A method and apparatus that primarily and secondarily scan regions of a substrate using electron beams with varying sensitivities, focusing on patterned and non-patterned regions, and adjusting scan regions and electron beam intensity based on detected defects to optimize detection efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the entire surface of the substrate is scanned several times using the electron beam, then the defect detection coverage is improved, but the scanning time is increased
Solution Approach 1:
The substrate surface is divided into multiple regions of interest (ROIs) based on defect probability maps. Instead of scanning the entire substrate uniformly, the electron beam is directed only to high-probability regions identified through machine learning analysis of preliminary scan data, thereby reducing total scanning time while maintaining defect detection coverage.
Solution Approach 2:
A preliminary low-resolution scan is performed first to generate a defect probability map using machine learning algorithms. This preliminary action identifies likely defect locations before the main high-resolution scan, allowing the system to focus resources on areas most likely to contain defects and avoid unnecessary scanning of defect-free areas.
2Measurement precision
If a high sensitivity is used for detecting very small defects, then the defect detection sensitivity is improved, but the time for detecting the defect is increased
Solution Approach 1:
The system applies different scanning sensitivities to different regions of the substrate based on defect probability. High sensitivity scanning is applied only to regions with high defect probability identified by the machine learning model, while low sensitivity scanning is used for regions with low defect probability. This local differentiation maintains detection sensitivity for small defects while reducing overall detection time.
Solution Approach 2:
Instead of applying maximum sensitivity scanning to the entire substrate, the system applies high sensitivity scanning only partially to high-probability defect regions. This partial action approach achieves adequate detection sensitivity where needed while avoiding the time penalty of exhaustive high-sensitivity scanning across the entire substrate surface.
3Device complexity
If the same sensitivity is used for detecting defects regardless of defect size, then the detection method is simplified, but very small defects may not be detected
Solution Approach 1:
The system dynamically adjusts scanning sensitivity based on real-time machine learning analysis of the substrate. As the scan progresses, the machine learning model continuously updates defect probability assessments and adjusts sensitivity parameters accordingly. This dynamic adaptation enables the system to maintain simplicity in operation while achieving high detection capability for small defects through automated, context-sensitive parameter adjustment.
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 scanning time and enhances defect detection sensitivity, allowing for accurate identification of defects of varying sizes by dynamically adjusting the scan region and electron beam intensity, thereby improving detection efficiency and reducing the likelihood of missing small defects.
Implementation Method 1
An electron beam may be used to detect a defect on a substrate
Data Source
AI summary
In a method of detecting a defect, a region of a substrate may be primarily scanned using a first electron beam to detect a first defect. A remaining region of the substrate, which may be defined by excluding a portion in which the first defect may be positioned from the region of the substrate, may be secondarily scanned using a second electron beam to detect a second defect. Thus, the portion with the defect may not be scanned in a following scan process so that a scanning time may be remarkably decreased.