Visual Artificial Intelligence Platform for Manufacturing Defect Analysis

Visual inspection has and will remain to be an integral part of the manufacturing quality control and assessment process, yet, human classification of defects is typically inconsistent and inaccurate due to distractions or fatigue, resulting in many man-hours wasted on manual visual inspections. While Automated Optical Inspection (AOI) systems have mostly addressed the shortcomings in manual quality control, rule-based AOIs tend to over-reject simple flaws as defects, resulting in substantial yield loss.

Visual Artificial Intelligence is able to accurately and consistently identify defects, leveraging the data-rich environment of manufacturing - translating to a smaller margin of error during defect analysis.

This technology is an in-line visual inspection Artificial Intelligence (AI) platform that utilises image-based data obtained from any existing automated image capture systems (for quality control and assurance), to conduct automated inspections at a higher rate than a human being for highly accurate, consistent defect classification and yield improvement.

This technology is designed and built to automate defect analysis for a large number of images from a variety of image-based sources e.g. Automated Visual Inspection (AVI) machine, Energy-Dispersive X-Ray Spectroscopy (EDX), Automated Optical Inspection (AOI) machine, Advanced 3D X-Ray Inspection (AXI) machine, Complementary Metal Oxide Semiconductor (CMOS) cameras and Scanning Electron Microscopes (SEM), at a time.

1. Upload large volumes of image data
2. AI-assisted data labelling (e.g. bent lead, lead deviation burr, scratch, etc)
3. Pre-trained AI models purpose-built for defect identification accelerate AI deployment
4. After testing, the best-performing AI model is automatically deployed

It has the following key features:

• Automates visual defect review and classification process
• Set-up and self-maintain an accurate AI model within hours, not days
• Easily integrates with existing automated image capture tools
• Built-in explainable AI assists with debugging the model performance and provides classification transparency
• Automatically identifies and learns new defects; adapting to line changes
• Drift aware - tracks model performance drift over time and automatically prompts when accuracy degrades
• Visualise heat maps of defect occurrence, yield loss percentage, yield recovery and Return-on-Investment (ROI) metrics

This technology has applications in the following sectors/industries:

• Semiconductor
• Electronics
• Automotive
• Heavy machinery
• Aviation
• Medical device assembly
• Pharmaceuticals
• Food inspection

• Fully automated visual inspection process, resulting in a > 90% reduction in visual inspection headcount
• Reduction in engineering man-hours attributable to insights gained from AI-enabled defect review
• Increase in defect classification accuracy (as compared to human defect classification)
• Reduction in number of escapees, false rejections and over-rejection from rule-based AOIs - increasing yield recovery by 10% (yield-loss minimisation)
• Cost and cycle time reduction through yearly yield recovery and headcount optimisation

The technology owner is keen to collaborate with high-value, complex manufacturing companies through R&D collaboration, new product/service co-development, test-bedding, and/or licensing.
Additionally, the technology owner is also keen to work with technology partners to co-develop enhanced Artificial Intelligence (AI) root-cause analysis and predictive analytics capabilities.