SBIR Phase I: An Artificial Intelligence System to Accelerate Semiconductor Production using Physics-embedded Lithographic Foundation Model

SBIR 第一阶段：使用物理嵌入式光刻基础模型加速半导体生产的人工智能系统

• 批准号: 2336079
• 负责人: Paul Lou
• 金额: $ 27.5万
• 依托单位: EXIGENT SOLUTIONS, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-02-15 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2336079&HistoricalAwards=false
• 关键词: SBIR; Phase; Artificial; Intelligence; System

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to expedite the wide adoption of next-generation semiconductor chips, which is a major factor in driving technological innovation across industries and societies. As technologies rapidly evolve, shifting to extreme ultraviolet lithography (EUV) systems in semiconductor manufacturing has significantly increased design and manufacturing complexities, leading to prohibitively high costs and stifling innovation. This project aims to alleviate the design and manufacturing bottlenecks by integrating leading-edge artificial intelligence into these complex processes. This innovation aims to significantly boost efficiency, reduce costs, and accelerate time-to-market for new chip designs, overcoming current limitations in next-generation process nodes. Importantly, this proposal is poised to strengthen domestic semiconductor capabilities, a crucial element for maintaining U.S. national security, global competitiveness, and technological leadership.This Small Business Innovation Research Phase I project is focused on advancing state-of-the-art artificial intelligence for simulating photolithography in rapidly emerging semiconductor technologies. As technology evolves and process precisions improve, minor design and manufacturing deviations, such as the 3D mask effect and stochastic variations, can no longer be neglected. Addressing this arising technical challenge requires a swift and precise simulation tool, essential for optimizing yield, throughput, and time-to-market, to maintain competitiveness in this market. The proposed work will create the Lithography Foundation Model (LFM), a system with physics integrated deeply into its framework that understands the intricate dynamics of extreme ultraviolet lithography processes. The technical approach of embedding physical modeling into LFM enables rigorous accuracy across any permutations of process conditions. Coupled with leading-edge hardware-software optimization, LFM promises real-time simulations with exceptional precision. The versatility and modularity of LFM enables applications for various processes, including process simulation, layout correction, and manufacturability optimization.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这项小型企业创新研究（SBIR）I阶段项目的更广泛的影响/商业潜力是加快对下一代半导体芯片的广泛采用，这是推动整个行业和社会技术创新的主要因素。随着技术的迅速发展，半导体制造中的极端紫外线光刻（EUV）系统已大大提高了设计和制造的复杂性，从而导致了高昂的高成本和扼杀创新。该项目旨在通过将领先的人工智能整合到这些复杂的过程中来减轻设计和制造瓶颈。这项创新旨在显着提高效率，降低成本，并加速新的芯片设计的上市时间，克服下一代流程节点中的当前限制。重要的是，该提案有望增强国内半导体能力，这是维持美国国家安全，全球竞争力和技术领导力的关键因素。该小型企业创新研究阶段I阶段I项目旨在推进最先进的人工智能，以模拟迅速的半导体技术中的模拟光刻摄影。随着技术的发展和流程精确度的改善，不再忽略较小的设计和制造偏差，例如3D掩码效应和随机变化。应对这一出现的技术挑战需要快速而精确的仿真工具，这对于优化产量，吞吐量和上市时间至关重要，以保持该市场的竞争力。拟议的工作将创建光刻基础模型（LFM），该系统将物理学深入整合到其框架中，以了解极端紫外线光刻过程的复杂动力学。将物理建模嵌入LFM中的技术方法可以在过程条件的所有排列中进行严格的精度。加上领先的硬件软件优化，LFM承诺具有出色精度的实时模拟。 LFM的多功能性和模块化为各种流程提供了应用程序，包括过程模拟，布局校正和制造性优化。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点和更广泛的影响来通过评估来支持的。