Summary of Ililt: Implicit Learning Of Inverse Lithography Technologies, by Haoyu Yang et al.
ILILT: Implicit Learning of Inverse Lithography Technologies
by Haoyu Yang, Haoxing Ren
First submitted to arxiv on: 6 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 approach is proposed to tackle the challenges in semiconductor manufacturing by leveraging machine learning (ML) techniques for generating optimized mask initializations. The traditional inverse lithography technology (ILT) relies heavily on good initialization to avoid getting stuck on sub-optimal solutions, which can be time-consuming and inefficient. To address this issue, an implicit learning ILT framework called ILILT is proposed, which leverages the implicit layer learning method and lithography-conditioned inputs to ground the model. Trained to understand the ILT optimization procedure, ILILT outperforms state-of-the-art ML solutions in terms of efficiency and quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers explore how machine learning can help improve semiconductor manufacturing. The goal is to create better mask designs for chip production without needing to use a special computer program called ILT. This helps make the process faster and more efficient. To do this, they created a new way of using machine learning called ILILT. It uses special training data to learn how to create good initial designs for masks. This approach is shown to be better than other ML methods in producing high-quality results. |
Keywords
» Artificial intelligence » Machine learning » Mask » Optimization
