Wafer Seal Chuck Imaging for In Situ Defect Detection
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Solution Overview
Problem
Conventional systems for inspecting wafer seal chuck assemblies in electroplating processes are inefficient and costly due to the use of complex laser-based methods, which reduce throughput and require multiple tools, while seals degrade over time from chemical deposits and contamination, leading to potential damage and contamination of subsequently processed wafers.
Innovation Solution
The implementation of imaging devices, such as cameras with wide fields of view, mounted on a tool arm within a maintenance chamber, coupled with digital image processing and machine learning algorithms, to automatically detect and identify defects in wafer seal chuck assemblies, enabling efficient and accurate inspection and cleaning without additional transfer steps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If laser-based inspection methods are used to detect defects in wafer seal chuck assemblies, then measurement precision is improved, but device complexity increases and productivity decreases
Solution Approach 1:
The patent replaces complex laser-based inspection systems with a simplified camera-based imaging system. The camera captures images of the wafer seal chuck assembly, and machine learning algorithms process these images to detect defects. This substitution of mechanical/optical inspection with digital imaging and computational analysis maintains measurement precision while dramatically improving productivity and reducing device complexity.
Solution Approach 2:
The patent creates a digital copy (image) of the wafer seal chuck assembly using a camera, then analyzes this copy using machine learning algorithms. This allows defect detection without physical manipulation or complex measurement tools, enabling rapid inspection that maintains precision while improving throughput.
2Measurement precision
If multiple inspection tools are deployed to detect seal defects, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent employs a single camera-based imaging system that performs multiple inspection functions. The machine learning model is trained to detect various types of defects including seal degradation, contamination, and damage to nearby components. This universal inspection tool replaces multiple specialized tools, reducing device complexity while maintaining comprehensive defect detection capability.
Solution Approach 2:
The patent combines image capture, defect detection, and analysis into a single integrated system. The camera captures images, and machine learning algorithms simultaneously analyze multiple defect types across different regions of the wafer seal chuck assembly. This merging of functions into one system reduces the number of separate tools needed while maintaining high measurement precision.
3Ease of operation
If manual inspection methods are used for wafer seal chuck assemblies, then ease of operation is improved, but productivity decreases and measurement precision worsens
Solution Approach 1:
The patent implements an automated inspection system where the camera and machine learning algorithms perform defect detection without manual intervention. The system autonomously captures images, processes them through trained models, and identifies defects. This self-service approach eliminates the need for manual inspection while dramatically improving productivity and maintaining high measurement precision through consistent algorithmic analysis.
Data Source
AI summary
Exemplary wafer seal chuck assembly maintenance chambers may include a chamber body defining an open interior. The chambers may include a plurality of chuck assembly supports disposed within the open interior. The plurality of chuck assembly supports may be rotatable within the open interior. The chambers may include a tool arm that is positionable above the open interior. The chambers may include an imaging device mounted on a distal end of the tool arm.