EUV Mask Contaminant Removal via AI-Based Particle Typing
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Solution Overview
Problem
Debris particles on photolithography masks cause yield loss by shielding mask patterns, and existing cleaning methods are time-consuming and require expertise, extending the repair cycle.
Innovation Solution
An AI-assisted method for identifying and removing contaminant particles on photolithography masks using imaging devices and tailored cleaning processes, including sulfuric acid and hydrogen peroxide mixtures, air-blade cleaning, and gas-etching, based on particle type determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional particle-removing processes are used, then contaminant particles can be removed from the mask, but the cleaning process is time-consuming and requires extensive expertise, extending the repair cycle
Solution Approach 1:
The patent replaces manual expert judgment and traditional mechanical cleaning methods with an automated imaging system that uses optical detection to identify particle types and characteristics. This substitution of mechanical/manual processes with optical and automated systems enables faster, more consistent particle removal decisions without requiring extensive expert experience for each cleaning operation.
Solution Approach 2:
The patent creates a digital copy or image of the mask surface and contaminant particles using imaging devices. By analyzing these images to determine particle characteristics (size, shape, composition), the system can select appropriate cleaning processes without physically examining each particle, thereby reducing the time and expertise required for effective contaminant removal.
2Reliability
If multiple cleaning processes are applied to remove different particle types, then complete particle removal is achieved, but the analysis and determination of particle types extends the repair cycle
Solution Approach 1:
The patent segments the complex task of particle removal into distinct steps: first, use imaging devices to detect and classify particle types based on their characteristics; second, select and apply the specific cleaning process appropriate for that particle type. This segmentation allows systematic handling of different particle types (organic, inorganic, metallic) without requiring complex real-time analysis, as the imaging and classification are performed separately from the cleaning execution.
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
Reduces mask cleaning and system maintenance time by accurately identifying and effectively removing contaminants, enhancing the efficiency and accuracy of the cleaning process.
Implementation Method 1
sulfuric acid and hydrogen peroxide mixtures
Implementation Method 2
sulfuric acid and hydrogen peroxide mixtures
Implementation Method 3
air-blade cleaning
Implementation Method 4
gas-etching
Data Source
AI summary
A method for removing contaminant particles on a surface of a mask includes collecting an image of the mask with the contaminant particles on the surface of the mask, and determining a type of the contaminant particles based on the image of the mask. The method further includes performing a particle-removing process corresponding to the type of the contaminant particles on the surface of the mask, and determining if the contaminant particles are removed from the surface of the mask by the particle-removing process. The method also includes associating the particle-removing process with the type of the contaminant particles when the contaminant particles are removed from the surface of the mask by the particle-removing process.