Manufacturing Message Queue for AI Defect Identification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for defect identification in product manufacturing, particularly in semiconductor production, suffer from low efficiency and accuracy due to reliance on manual detection, which is time-consuming and requires professional training, and the processing of product manufacturing messages is not well coordinated with the manufacturing process, leading to inefficiencies.
Innovation Solution
A method involving monitoring product manufacturing messages, establishing a product defect analysis task queue, and distributing tasks to assisting devices using AI defect identification models to identify defect types, locations, and sizes, with efficient task scheduling and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual detection methods are used for defect identification, then inspectors can identify defects with professional training, but the processing efficiency and accuracy are low due to time-consuming manual analysis
Solution Approach 1:
The patent replaces the manual mechanical inspection system with an automated image processing system that uses computer algorithms to analyze product images, extract defect features, and identify defect types automatically, thereby eliminating the time-consuming manual analysis while maintaining or improving identification accuracy
Solution Approach 2:
The patent creates a virtual copy of the inspection process through software algorithms that simulate and automate the defect identification task, allowing multiple products to be analyzed simultaneously without requiring additional physical inspectors, thus improving processing efficiency
2Adaptability or versatility
If multiple product models and complex defect types are handled manually, then comprehensive defect identification can be achieved, but the time and attention required from inspectors increase significantly
Solution Approach 1:
The patent develops a universal defect identification system that can handle multiple product models and various defect types through a single automated platform, using configurable parameters and algorithms that adapt to different inspection requirements without requiring separate manual inspection processes for each product type
Solution Approach 2:
The patent performs preliminary processing of product images including preprocessing, feature extraction, and defect detection automatically before final analysis, preparing the data in advance for rapid classification and identification, thereby reducing the overall inspection time for complex multi-model scenarios
3Reliability
If traditional defect identification methods are used, then professional training is required for inspectors, but the processing efficiency remains low
Solution Approach 1:
The patent replaces the human inspector's trained expertise with automated algorithms that encode defect identification knowledge, allowing the system to reliably identify defects without requiring human training while processing messages at much higher speeds
Solution Approach 2:
The system performs self-learning and automatic adaptation through machine learning algorithms that continuously improve defect identification accuracy without requiring external retraining of human inspectors, maintaining reliability while enabling high-throughput processing
4Ease of operation
If product manufacturing messages are processed without coordination with the manufacturing procedure, then message processing can be performed independently, but the overall manufacturing efficiency is reduced
Solution Approach 1:
The patent implements a feedback mechanism where defect identification results are immediately fed back to the manufacturing process, enabling real-time adjustments and corrections that improve overall manufacturing efficiency while maintaining independent message processing capability
Solution Approach 2:
The system dynamically adjusts its processing priority and resource allocation based on the manufacturing procedure status, allowing flexible coordination between independent message processing and overall production requirements to maximize manufacturing efficiency
Data Source
AI summary
A method and a device for processing product manufacturing messages, and an electronic device are disclosed. The method for processing product manufacturing messages includes: monitoring a plurality of product manufacturing messages; establishing a product defect analysis task queue based on the plurality of product manufacturing messages; distributing product defect analysis tasks to product manufacturing assisting devices based on the product defect analysis task queue, wherein the product defect analysis tasks include a task of identifying product defect content based on a defect identification model; wherein the product defect content includes any one or more of: product defect type, product defect location, and product defect size.