AI Quality Classification for Semifinished Component Carriers
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Solution Overview
Problem
The increasing miniaturization and complexity of electronic components on component carriers, combined with the need for efficient heat removal and mechanical robustness, pose challenges in quality classification, which is often cumbersome and prone to human error.
Innovation Solution
An automated system using artificial intelligence for optical inspection and quality classification of semifinished component carriers, comparing actual images with reference images, and taking actions based on classification results, including repair or rejection, to improve yield and efficiency without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If human operators perform quality classification, then flexibility and judgment capability are maintained, but productivity is low and errors are frequent
Solution Approach 1:
The patent replaces the mechanical human inspection system with an automated optical inspection system using cameras and image processing. The automatic optical inspection unit captures images of component carriers and compares them against reference images, eliminating manual visual inspection and significantly increasing throughput while maintaining consistent quality judgment.
Solution Approach 2:
The system performs self-service through automated decision-making. The automatic judgment unit independently evaluates inspection results, makes quality classifications, and triggers appropriate actions without requiring human intervention. This self-service capability maintains high productivity while reducing manual errors.
2Measurement precision
If automated optical inspection is implemented, then productivity increases and consistency improves, but measurement precision and defect detection capability are insufficient for complex miniaturized components
Solution Approach 1:
The inspection system is segmented into multiple functional units: automatic optical inspection unit for image capture and initial analysis, automatic judgment unit for decision-making, and action control unit for executing corrections. This segmentation allows each unit to specialize in specific tasks, improving overall precision while maintaining high throughput through parallel processing of multiple component carriers.
Solution Approach 2:
The system implements feedback through the comparison of actual images with reference images. The automatic judgment unit receives inspection results, evaluates them against predefined criteria, and provides feedback for quality classification. This feedback mechanism ensures high measurement precision by continuously comparing actual component characteristics against established standards.
3Loss of time
If manual quality classification is used, then judgment flexibility is maintained, but loss of time and labor effort increase
Solution Approach 1:
The system performs preliminary actions by pre-establishing reference images and inspection criteria before actual quality classification is needed. The automatic judgment unit has pre-loaded reference data that enables immediate comparison and decision-making during production, eliminating the need for time-consuming manual evaluation while simplifying operation through automated protocols.
Solution Approach 2:
The automatic optical inspection system serves multiple functions: image capture, image comparison, quality classification, and action triggering. This multi-functionality consolidates what would otherwise require multiple separate manual operations into a single automated system, reducing time loss while maintaining ease of operation through integrated control.
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
This approach enables high-yield, efficient manufacturing of component carriers by automating quality control, reducing manual errors, and improving throughput, allowing for reliable mechanical and electrical reliability even under harsh conditions.
Implementation Method 1
automatic optical inspection by comparison of a data set indicative of an actual image of a respective semifinished component carrier with a data set indicative of a reference image
Data Source
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AI summary
A method of manufacturing component carriers (100), wherein the method comprises supplying a panel (102), comprising a plurality of semifinished component carriers (100), to an automatic optical inspection unit (104) for automatic optical inspection by comparison of a data set indicative of an actual image of a respective semifinished component carrier (100) with a data set indicative of a reference image, forwarding the inspected panel (102) to an automatic judgment unit (106) for carrying out a quality classification of the semifinished component carriers (100), based on a result of the automatic optical inspection, by applying artificial intelligence, and taking an action based on the quality classification.