EUV Photomask Defect Disposition via Charged Particle Beam Imaging
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Solution Overview
Problem
Current EUV photomask inspection tools operating in the deep ultraviolet wavelength range face challenges in detecting defects at the necessary resolution and sensitivity, leading to high false detection rates and manual review difficulties due to limited resolution and low signal-to-noise ratios, which complicates defect disposition for EUV lithography.
Innovation Solution
A photomask inspection system that uses a combination of deep ultraviolet optical inspection followed by charged particle beam imaging and atomic force microscopy for defect detection and disposition, employing a hot inspection threshold to increase sensitivity and automate the classification and printability analysis of detected defects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If DUV inspection tools are used to inspect EUV photomasks, then inspection can be performed with available tools, but defect detection resolution and sensitivity are insufficient leading to high false detection rates
Solution Approach 1:
The inspection process is segmented into multiple stages: initial DUV inspection to identify potential defects, followed by charged particle beam inspection for high-resolution verification. This segmentation allows each inspection method to operate in its optimal performance range, with DUV providing broad coverage and charged particle beams providing high-fidelity verification to eliminate false detections.
Solution Approach 2:
Charged particle beam inspection serves as an intermediary verification step between DUV inspection and final defect disposition. The charged particle beam acts as a mediator that validates DUV detections with higher resolution, separating true defects from false detections before final classification and repair decisions are made.
2Measurement precision
If manual review of detected defects is performed, then defect classification can be done, but the process becomes onerous and time-consuming especially with hundreds or thousands of detections
Solution Approach 1:
The system implements automated defect classification and disposition determination that reduces reliance on manual operator review. The charged particle beam inspection data and DUV inspection data are processed through automated algorithms that classify defects and determine dispositions, enabling the system to serve itself in the classification task while minimizing human intervention time.
Solution Approach 2:
Manual mechanical review processes are replaced with automated computational systems. The charged particle beam inspection data is processed through automated classification algorithms and machine learning models that substitute human operators in the defect classification task, dramatically reducing review time while maintaining or improving classification accuracy.
3Measurement precision
If hot inspection threshold is used to increase sensitivity, then more defects are detected, but the number of false detections increases requiring more review
Solution Approach 1:
The system extracts and isolates potential false detections by comparing DUV inspection results with charged particle beam inspection results. Defects that appear in DUV inspection but are not confirmed by the higher-resolution charged particle beam are extracted and classified as false detections, removing them from the set of defects requiring manual review.
Solution Approach 2:
Charged particle beam inspection is performed as a preliminary verification step before final defect disposition. By pre-validating DUV detections with charged particle beam imaging, the system prepares a filtered list of confirmed defects, reducing the burden of subsequent manual review and disposition activities.
4Ease of manufacture
If DUV wavelength inspection is used, then inspection can be performed with current tools, but the resolution is insufficient for EUV mask feature sizes
Solution Approach 1:
The inspection system is segmented into two functional components: DUV inspection for broad defect screening and charged particle beam inspection for high-resolution dimensional measurement. Each component operates at its optimal wavelength and resolution, with the charged particle beam providing the necessary precision for EUV mask features while DUV provides cost-effective broad coverage.
Solution Approach 2:
The inspection system achieves multi-functionality by combining DUV and charged particle beam capabilities in a unified workflow. The same system performs both initial defect detection with DUV and subsequent high-resolution verification with charged particle beams, eliminating the need for separate specialized tools while maintaining both accessibility and precision.
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 enhances defect detection sensitivity and accuracy, allowing for efficient separation of real and false defects, and automates the disposition process, improving throughput and reducing manual error in EUV photomask inspection.
Implementation Method 1
a charged particle beam subsystem configured for generating charged particle beam images of the photomask at locations of the detected defects
Implementation Method 2
an inspection subsystem configured for directing energy to a photomask and detecting energy from the photomask
Data Source
AI summary
Methods and systems for photomask defect dispositioning are provided. One method includes directing energy to a photomask and detecting energy from the photomask. The photomask is configured for use at one or more extreme ultraviolet wavelengths of light. The method also includes detecting defects on the photomask based on the detected energy. In addition, the method includes generating charged particle beam images of the photomask at locations of the detected defects. The method further includes dispositioning the detected defects based on the charged particle beam images generated for the detected defects.