Summary of Intelligent Opc Engineer Assistant For Semiconductor Manufacturing, by Guojin Chen et al.
Intelligent OPC Engineer Assistant for Semiconductor Manufacturing
by Guojin Chen, Haoyu Yang, Bei Yu, Haoxing Ren
First submitted to arxiv on: 23 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Hardware Architecture (cs.AR)
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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 reinforcement learning-based AI/LLM-powered methodology, called Intelligent OPC Engineer Assistant, is proposed to optimize optical proximity correction (OPC) in semiconductor manufacturing. The approach involves searching for OPC recipes and summarizing them using a customized multi-modal agent system. This methodology can efficiently build OPC recipes on various chip designs with specially handled design topologies, typically requiring the expertise of experienced OPC engineers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed an AI-powered tool to help optimize semiconductor manufacturing. They created a system that uses artificial intelligence and large language models to find the best way to correct tiny defects in microchips. This process usually requires a lot of experience and expertise, but the new system can do it more efficiently and accurately. |
Keywords
* Artificial intelligence * Multi modal * Reinforcement learning
