mlsanityData to 14 Oct 2026

Intelligent OPC Engineer Assistant for Semiconductor Manufacturing

arXiv 2408.12775 ↗First posted 23 Aug 2024cs.AI cs.ARcited by 2

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Abstract (arXiv)

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 textitIntelligent 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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 10 of its 22 references that are in the map. First posted on arXiv 23 Aug 2024.

Builds on

10 of 22 references.

References · 10

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2017Proximal Policy Optimization Algorithmscited by 15,215
  3. 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
  4. 2023ChatGPT Outperforms Crowd-Workers for Text-Annotation Taskscited by 870
  5. 2023ChipNeMo: Domain-Adapted LLMs for Chip Designcited by 174
  6. 2023ChatEDA: A Large Language Model Powered Autonomous Agent for EDAcited by 122
  7. 2023Large Language Models can Learn Rulescited by 69
  8. 2020DAMO: Deep Agile Mask Optimization for Full Chip Scalecited by 24
  9. 2024ILILT: Implicit Learning of Inverse Lithography Technologiescited by 7
  10. 2024CAMO: Correlation-Aware Mask Optimization with Modulated Reinforcement Learningcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026LithoDreamer: A Physics-Informed World Model for Multi-Stage Computational Lithographycited by 0
  2. 2026VPEvolve: A Self-Evolving Virtual Process Engineer for Computational Lithographycited by 0

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