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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.12775 |
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| _version_ | 1866929475195764736 |
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| author | Chen, Guojin Yang, Haoyu Yu, Bei Ren, Haoxing |
| author_facet | Chen, Guojin Yang, Haoyu Yu, Bei Ren, Haoxing |
| contents | Advancements in chip design and manufacturing have enabled the processing of complex tasks such as deep learning and natural language processing, paving the way for the development of artificial general intelligence (AGI). AI, on the other hand, can be leveraged to innovate and streamline semiconductor technology from planning and implementation to manufacturing. In this paper, we present \textit{Intelligent OPC Engineer Assistant}, an AI/LLM-powered methodology designed to solve the core manufacturing-aware optimization problem known as optical proximity correction (OPC). The methodology involves a reinforcement learning-based OPC recipe search and a customized multi-modal agent system for recipe summarization. Experiments demonstrate that our methodology can efficiently build OPC recipes on various chip designs with specially handled design topologies, a task that typically requires the full-time effort of OPC engineers with years of experience. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_12775 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Intelligent OPC Engineer Assistant for Semiconductor Manufacturing Chen, Guojin Yang, Haoyu Yu, Bei Ren, Haoxing Artificial Intelligence Hardware Architecture Advancements in chip design and manufacturing have enabled the processing of complex tasks such as deep learning and natural language processing, paving the way for the development of artificial general intelligence (AGI). AI, on the other hand, can be leveraged to innovate and streamline semiconductor technology from planning and implementation to manufacturing. In this paper, we present \textit{Intelligent OPC Engineer Assistant}, an AI/LLM-powered methodology designed to solve the core manufacturing-aware optimization problem known as optical proximity correction (OPC). The methodology involves a reinforcement learning-based OPC recipe search and a customized multi-modal agent system for recipe summarization. Experiments demonstrate that our methodology can efficiently build OPC recipes on various chip designs with specially handled design topologies, a task that typically requires the full-time effort of OPC engineers with years of experience. |
| title | Intelligent OPC Engineer Assistant for Semiconductor Manufacturing |
| topic | Artificial Intelligence Hardware Architecture |
| url | https://arxiv.org/abs/2408.12775 |