Distributed Defect Identification for Semiconductor Image Inspection
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Solution Overview
Problem
Current product defect analysis in manufacturing processes, particularly in semiconductor production, relies heavily on manual detection, leading to inefficiencies and inaccuracies due to the complexity of defects and the need for professional training, resulting in low efficiency and accuracy.
Innovation Solution
A distributed computing system for product defect analysis is introduced, comprising a computing cluster for processing manufacturing messages, a computing cluster for identifying defects, a product image database, and a client device, which uses a defect identification model to analyze product images and output defect identification results, including defect type, position, and size, thereby automating the defect identification process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual detection is used for product defect analysis, then inspectors can identify defects with professional training, but the inspection process requires long time and dedicated attention, resulting in low efficiency
Solution Approach 1:
The patent replaces the manual mechanical inspection system with an automated defect identification model that uses machine learning algorithms to analyze product images. The system automatically detects and classifies defects without human intervention, substituting the mechanical process of manual inspection with an automated computational system that achieves both high accuracy and efficiency
Solution Approach 2:
The defect identification model enables the system to perform self-inspection by automatically analyzing product images, identifying defects, and generating inspection reports without requiring external human inspectors. The system serves itself by incorporating the defect detection capability directly into the manufacturing process
2Reliability
If manual detection is used for complex product defects, then inspectors can make relevant judgments, but professional training is required and the process is time-consuming
Solution Approach 1:
The system performs preliminary defect identification automatically during the manufacturing process by analyzing product images in real-time. The defect identification model is pre-trained with extensive defect data to reliably detect and classify various defect types before products move to subsequent manufacturing stages, eliminating the need for time-consuming manual verification
Solution Approach 2:
The patent replaces the manual judgment process with an automated machine learning system that uses pre-trained models to reliably identify and classify defects. The system substitutes human inspectors with computational algorithms that provide consistent, reliable defect judgment without requiring professional training or time investment
3Productivity
If automated defect identification is implemented, then inspection efficiency is improved, but a distributed computing system with multiple clusters is required
Solution Approach 1:
The patent divides the automated defect identification system into distinct functional modules: a computing cluster for processing manufacturing messages, a computing cluster for executing defect identification tasks, and a product image database. This segmentation allows each component to specialize in specific functions, improving overall efficiency while organizing complexity into manageable segments
Solution Approach 2:
The computing clusters are designed with multi-functionality to handle various defect identification tasks across different product types and defect categories. The system uses universal machine learning models that can be applied to multiple inspection scenarios, reducing the need for separate specialized systems for each defect type
Data Source
AI summary
A distributed computing system for product defect analysis is disclosed. The distributed computing system for product defect analysis includes a computing cluster for processing product manufacturing messages, a computing cluster for identifying product defect, a product image database, and a client device.