Neural Network Substrate Detection in Semiconductor Handling
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Solution Overview
Problem
Current methods in the semiconductor industry for determining the presence, position, and orientation of substrates, such as wafers and masks, are inefficient and prone to errors, particularly during the teach-in process, leading to potential damage, contamination, and reduced yield due to the reliance on manual, rule-based image processing which is inflexible and prone to errors from varying environmental conditions.
Innovation Solution
The use of an artificial neural network to analyze images of substrates and their locations within the semiconductor industry, generating information data records for controlling machine operations, allowing for flexible and accurate determination of substrate presence, position, and orientation, reducing the need for manual rule definition and improving handling system efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual rule-based image processing is used to determine substrate presence, position, and orientation, then the system can operate with simple hardware, but the reliability and accuracy deteriorate due to errors from varying environmental conditions and manual processing limitations
Solution Approach 1:
The patent replaces manual rule-based image processing with an artificial neural network system that automatically learns and adapts to various environmental conditions. The neural network processes images of substrates in cassettes, determining presence, position, and orientation without manual intervention, thereby improving reliability while managing complexity through automation.
Solution Approach 2:
The patent changes the processing approach from fixed rule-based parameters to adaptive neural network parameters that can dynamically adjust to varying environmental conditions. The neural network learns optimal parameter settings during training and applies them during operation, maintaining high reliability across different lighting, camera angles, and substrate configurations.
2Adaptability or versatility
If rule-based image processing is used, then the system structure remains simple, but the adaptability deteriorates due to inability to handle varying environmental conditions and complex substrate configurations
Solution Approach 1:
The patent replaces rigid rule-based processing with a flexible neural network system that adapts to varying environmental conditions including different lighting, camera positions, and substrate types. The neural network learns from training data and automatically adjusts its processing parameters, providing high adaptability without requiring complex manual reconfiguration.
3Productivity
If manual teach-in processes are used for handling systems, then the system can be implemented with basic components, but the productivity deteriorates due to time-consuming procedures and potential errors
Solution Approach 1:
The patent implements self-service through automated neural network-based substrate detection and handling system control. The system automatically determines substrate presence, position, and orientation, and uses this information to control pick-and-place operations without manual teach-in procedures, significantly reducing setup time and improving productivity.
Solution Approach 2:
The patent replaces manual teach-in procedures with automated neural network processing that rapidly analyzes substrate images and generates control commands for handling systems. This substitution eliminates time-consuming manual operations while maintaining or improving accuracy, directly enhancing productivity.
4Loss of information
If sensors are used to detect substrate presence and position, then real-time information can be obtained, but the device complexity increases due to multiple sensors and their setup requirements
Solution Approach 1:
The patent replaces multiple physical sensors with an optical imaging system processed by a neural network. A single camera captures images of substrates in cassettes, and the neural network extracts presence, position, and orientation information from these images, reducing sensor complexity while maintaining or improving information accuracy.
Data Source
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AI summary
The invention relates to a method for processing substrates, in particular wafers, masks, or flat panel displays, using a semiconductor machine, wherein a computer-aided method is employed to determine the presence and/or position and/or orientation of the substrate. The invention further relates to a system configured for carrying out the method. The computer-aided method comprises an artificial neural network.