Summary of Differentiable Edge-based Opc, by Guojin Chen et al.
Differentiable Edge-based OPC
by Guojin Chen, Haoyu Yang, Haoxing Ren, Bei Yu, David Z. Pan
First submitted to arxiv on: 16 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Optics (physics.optics)
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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 proposed DiffOPC framework is a differentiable optical proximity correction (OPC) technique that combines the benefits of edge-based OPC and inverse lithography technology (ILT). This framework uses a mask rule-aware gradient-based optimization approach to efficiently guide mask edge segment movement during mask optimization, minimizing wafer error by propagating true gradients from the cost function back to the mask edges. The results show that DiffOPC achieves lower edge placement error while reducing manufacturing cost by half compared to state-of-the-art OPC techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to correct errors in semiconductor manufacturing called DiffOPC. This method is better than other ways because it’s more accurate and cheaper. It uses a special kind of optimization that helps the machine making the mask get closer to the perfect design. This makes it possible to make smaller and faster chips that are important for many industries. |
Keywords
» Artificial intelligence » Mask » Optimization
