Circuit Board Optical Inspection with Adaptive Defect Validation
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Solution Overview
Problem
Existing optical inspection systems for circuit boards cannot dynamically adjust to changing manufacturing conditions, leading to false positives and increased follow-up inspections, and require manual reprogramming due to changes in lighting or board composition.
Innovation Solution
Implementing an adaptive algorithm that learns from diagnostic feedback to improve defect detection, reducing the need for manual reprogramming and enhancing first-pass yield by correcting optical image processing results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual visual check by operator is used for defect diagnosis, then detection accuracy can be maintained, but time consumption and labor requirements increase
Solution Approach 1:
The system enables self-service by allowing the AOI system to automatically learn from operator diagnoses and update its inspection programs without manual reprogramming. The operator's expertise is captured and automated, allowing the system to serve itself in adapting to new defect patterns and manufacturing conditions.
Solution Approach 2:
The system implements feedback by capturing operator diagnoses and using them to automatically update the AOI inspection programs. The feedback loop flows from operator diagnosis -> database storage -> automatic program update -> improved AOI performance, eliminating the need for manual reprogramming while maintaining high detection accuracy.
2Productivity
If optical image processing is used for defect detection, then inspection speed increases, but false positives occur due to inability to adapt to manufacturing changes
Solution Approach 1:
The system transforms the static optical image processing programs into dynamic, self-updating systems. The inspection programs automatically adapt to manufacturing changes by learning from new defect patterns and manufacturing conditions through feedback from operator diagnoses, maintaining high reliability while preserving inspection speed.
Solution Approach 2:
The feedback mechanism captures operator diagnoses and automatically updates the AOI inspection programs, enabling the system to learn from manufacturing changes and reduce false positives while maintaining fast inspection speeds through automated program optimization.
3Measurement precision
If inspection programs are manually updated to adapt to manufacturing changes, then detection accuracy improves, but device complexity and maintenance requirements increase
Solution Approach 1:
The system eliminates manual program maintenance by implementing self-service through automated learning. The AOI system automatically updates its own inspection programs based on feedback from operator diagnoses, transforming a complex manual maintenance process into an automated self-optimizing system.
Solution Approach 2:
The feedback loop automatically captures operator diagnoses and translates them into program updates, eliminating the need for manual program maintenance while maintaining high detection accuracy. The system learns from real-world data and self-adjusts without human intervention in the maintenance process.
Data Source
AI summary
The invention relates to a method for optical quality control (Q) during the manufacture (1) of circuit boards (L), comprising: capturing (S3) an image (P1) of a circuit board (L); determining (S4) a first defect indicator (F1) by means of optical image processing on the basis of the captured image; determining (S5) a second defect indicator (F2) by means of a trained adaptive algorithm (ML) on the basis of the captured image (P1), wherein, if the first defect indicator (F1) determined by means of the optical image processing (OR) indicates that the circuit board (L) is defective (S41), the second defect indicator (F2) is determined by means of the trained adaptive algorithm (ML) (S5); outputting (S6) the second defect indicator (F2) instead of the first defect indicator (F1) as the result of the quality control (Q).