The Role of Defect Classification in Industrial Automation
Deep-learning AI significantly enhances the speed and precision of automated defect classification (ADC) in industrial manufacturing. By enabling early defect detection and improving classification accuracy and consistency, AI-powered ADC allows factories to quickly identify and rectify defects, maximizing machine capacity and ensuring high product quality. This technology is highly sensitive and applicable to a wide range of industrial applications in both discrete and process manufacturing.
Built to Address Increasing Inspection Complexity
To boost yield, the ability to manage and classify defects efficiently is crucial. However, growing defect complexity and exploding product mix present major inspection challenges. Built to tackle tomorrow’s challenges, Robovision’s AI-powered ADC enables you to:
- Customize inspection based on your specific product mix and processes
- Speed up quality checks for real-time inline and offline inspection
- Shorten Time to Resolution using efficient end-to-end workflows
- Continuously improve accuracy
How it Works
Real-World Applications
Automatic defect classification (ADC) for outgoing quality control
Defect classification is vital to determine if goods pass or fail inspection. Considering the quantity and severity of different defect types helps inform shipping decisions. The more granular the classification enabled by deep learning-based ADCs, the more valuable the insights are to suppliers and buyers.
Defect classification information can help:
- Improve the supplier’s ability to self-identify and correct quality defects before outside inspection
- Ensure more accurate inspection results that match quality tolerances and expectations
- Reduce cases of “pending” results reported by the inspector due to unclear quality tolerances
Spatial Signature Analysis
The spatial distribution of data (e.g., defects on a wafer surface) can be used to determine the potential source of problems in the manufacturing process. A spatial signature is defined as a population of defects originating from a single manufacturing problem.
A significant proportion of systematic defects can manifest as spatial patterns (signatures) of failing chips on the silicon wafers. Combining wafer map pattern classification with a wafer’s equipment genealogy—such as the specific equipment that processes the wafer— assists engineers/technicians in pinpointing a root cause.
Inconsistent Weld Bead Geometry
Inconsistent weld bead geometry means variations in bead width, height, alignment, and toe angle. These variations compromise product quality, increase rework, and lead to fatigue failures. Manual inspection is time-consuming and error-prone, which makes it unsuitable for high-throughput operations. By contrast, deep learning ADCs can automatically classify weld bead geometry defects, including overfill / underfill, misalignments, wrong toe angle, or inconsistent bead shape.
AI ADC for Advanced Process Control
Advanced Process Control (APC) has been widely used in manufacturing to improve process and product performance. Key APC benefits include:
Faster root cause analysis
ADC categorizes defects and correlates them with process parameters. Engineers can pinpoint issues in the manufacturing line faster, perform corrective actions quicker, and ensure process optimization.
Improved process stability
ADC integrates with APC systems to provide real-time defect data. Dynamic adjustments can be made to processes, minimizing variations and boosting overall stability.
Yield improvement
Early detection and classification of defects allow manufacturers to take proactive measures, preventing yield loss and optimizing throughput.
Real-time defect identification
Reduce dependency on manual inspection and increase accuracy
Cost reduction
ADC reduces manual inspection costs and rework expenses, supporting efficient resource utilization.
Better decision-making and analytics
ADC provides trend analysis and predictive insights, allowing manufacturers to anticipate defects before they cause major disruptions.
Smooth Inline and Offline Integration
The Robovision AI-ADC supports both inline (or tool-centric) ADC and offline integration. This flexibility makes it applicable for multiple inspection applications, offering smooth integration with existing Manufacturing Execution Systems (MES).
Leveraging Existing Imaging Tools and Data Sets
Imaging tools designed for other purposes can be used for defect classification. This approach means imaging datasets are already available, which speeds up time to resolution. By repurposing existing images through the Robovision AI-ADC solution, manufacturers can classify additional defects or implement excursion detection without investing in new equipment or major upgrades. This approach enables earlier identification of issues, leading to improved yields and increased ROI from existing equipment.
