Summary of Deep Learning For Fast Segmentation and Critical Dimension Metrology & Characterization Enabling Ar/vr Design and Fabrication, by Kundan Chaudhary et al.
Deep learning for fast segmentation and critical dimension metrology & characterization enabling AR/VR design and fabrication
by Kundan Chaudhary, Subhei Shaar, Raja Muthinti
First submitted to arxiv on: 20 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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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 A novel deep learning approach is proposed for segmenting regions of interest (ROIs) from microscopy images, crucial in AR/VR module design and fabrication. The study fine-tunes a pre-trained Segment Anything Model (SAM) using a diverse dataset of electron microscopy images, employing low-rank adaptation (LoRA) to reduce training time while enhancing accuracy. This generalizable model supports zero-shot learning and accurate ROI extraction with precision, facilitating the extraction of critical dimensions (CDs). The approach is demonstrated on surface relief gratings (SRGs) and Fresnel lenses in single and multiclass modes, enabling identification of transition points and relevant CD extraction. This combined segmentation and CD extraction model offers significant advantages for various industrial applications by enhancing analytical capabilities, time to data and insights, and optimizing manufacturing processes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a new way to analyze microscope images, which is important for making augmented reality and virtual reality devices. They took an existing AI model and adjusted it using many different types of microscopy images. This improved the model’s ability to recognize specific parts in the images and extract useful information. The approach was tested on two types of images: surface relief gratings and Fresnel lenses. It worked well for both, allowing researchers to identify important features and measure their sizes accurately. This could lead to better manufacturing processes and more accurate analysis of data. |
Keywords
» Artificial intelligence » Deep learning » Lora » Low rank adaptation » Precision » Sam » Zero shot
