Summary of Ai-guided Defect Detection Techniques to Model Single Crystal Diamond Growth, by Rohan Reddy Mekala et al.
AI-Guided Defect Detection Techniques to Model Single Crystal Diamond Growth
by Rohan Reddy Mekala, Elias Garratt, Matthias Muehle, Arjun Srinivasan, Adam Porter, Mikael Lindvall
First submitted to arxiv on: 10 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The researchers tackle the challenge of achieving high-quality diamond growth via chemical vapor deposition by developing a defect segmentation pipeline that identifies defective states during the growth process using in-situ optical images. They employ a semantic segmentation approach to isolate and classify these defects, leveraging human-in-the-loop software architecture with active learning, data augmentations, and model-assisted labeling to reduce annotation time and cost. Their best-performing model, based on YOLOV3 and DeeplabV3plus architectures, achieved excellent accuracy for center, polycrystalline, and edge defects. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scientists have made progress in growing diamonds using a special process called chemical vapor deposition. But to make sure the diamond is perfect, they need to understand how tiny flaws form during growth. This paper shows how to use special computer algorithms to look at pictures taken during growth and find these flaws. The team used a way of labeling images that helps machines learn from people’s actions, which makes it much faster and cheaper to identify defects. |
Keywords
» Artificial intelligence » Active learning » Semantic segmentation
