Two-Stage Defect Classification for Microelectronics
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Solution Overview
Problem
Current defect classification processes in microelectronics manufacturing are slow and costly due to insufficient data for clear separation between defects, often requiring additional data collection that consumes time and resources.
Innovation Solution
A method involving a two-stage automatic defect classification system, where a first camera acquires and processes images to detect suspected defects, and a second camera provides additional data only when necessary for final classification, optimizing resource use and processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single camera is used for defect detection, then the system is simple and fast, but the classification accuracy is insufficient due to lack of detailed information
Solution Approach 1:
The defect classification process is segmented into two stages: initial classification using the first camera for most defects, and secondary classification using the second camera only for ambiguous cases. This segmentation allows the system to achieve high classification accuracy while avoiding the complexity of using both cameras simultaneously for all defects.
Solution Approach 2:
Instead of using the second camera for all defects (excessive action), the system applies it only partially to defects that require additional information for classification. This partial application of the second camera provides sufficient detail for accurate classification while minimizing system complexity and resource usage.
2Measurement precision
If additional data collection is performed for all suspected defects, then classification accuracy improves, but processing time and resource consumption increase
Solution Approach 1:
The system performs additional data collection (using the second camera) only partially - specifically for defects that cannot be confidently classified by the first camera. This partial action maintains high classification accuracy while avoiding the time penalty of processing all defects with the second camera.
Solution Approach 2:
The system uses the initial classification results from the first camera to self-determine which defects require additional analysis. By automatically identifying ambiguous cases and directing them to the second camera, the system eliminates the need for universal re-examination, thereby reducing processing time while maintaining accuracy.
3Measurement precision
If a two-camera system is used for all defects, then classification accuracy is high, but resource consumption and processing time increase
Solution Approach 1:
The defect population is segmented into two groups: confidently classified defects handled by the first camera and ambiguous defects requiring the second camera. This segmentation enables the system to maintain high throughput by processing most defects quickly with the first camera while dedicating resources to the smaller subset of ambiguous cases.
Solution Approach 2:
The second camera is applied only partially to the subset of defects that require additional detail. This partial application preserves processing throughput by avoiding the resource consumption and time delay that would result from processing all defects with the second camera, while still achieving high classification accuracy for the ambiguous cases.
Data Source
AI summary
A method for automatic defect classification, the method may include (i) acquiring, by a first camera, at least one first image of at least one area of an object; (ii) processing the at least one first image to detect a group of suspected defects within the at least one area; (iii) performing a first classification process for initially classifying the group of suspected defects; (iii) determining whether a first subgroup of the suspected defects requires additional information from a second camera for a completion of a classification; (iv) when determining that the first subgroup of the suspected defects requires additional information from the second camera then: (a) acquiring second images, by the second camera, of the first subgroup of the suspected defects; and (b) performing a second classification process for classifying the first subgroup of suspected defects.