Summary of Patternpaint: Practical Layout Pattern Generation Using Diffusion-based Inpainting, by Guanglei Zhou et al.
PatternPaint: Practical Layout Pattern Generation Using Diffusion-Based Inpainting
by Guanglei Zhou, Bhargav Korrapati, Gaurav Rajavendra Reddy, Chen-Chia Chang, Jingyu Pan, Jiang Hu, Yiran Chen, Dipto G. Thakurta
First submitted to arxiv on: 2 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG)
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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 paper proposes PatternPaint, a diffusion-based framework that generates diverse VLSI (Very Large Scale Integration) layout patterns with limited training samples. Traditional methods rely on large datasets, which is impractical when developing a new technology node. PatternPaint simplifies the generation process into inpainting processes using a template-based denoising scheme. The model achieves high diversity and legality scores in complex 2D metal interconnect design rule settings, outperforming previous works. Few-shot finetuning with only 20 samples boosts the legality rate by 1.87X compared to the original pretrained model. PatternPaint is a production-ready approach for generating layout patterns in new technology nodes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine designing tiny electronic chips that power our computers and smartphones. To do this, we need to create special patterns on these chips, called VLSI layouts. Currently, it’s hard to get the right designs because they require a lot of data and training. The authors of this paper developed a new way to generate these designs using limited data. Their method, called PatternPaint, uses a series of simple steps to create complex designs. It works well even with very little information! This is important because it will help us design better chips faster. |
Keywords
» Artificial intelligence » Diffusion » Few shot
