SHF: Small: Design/Technology Co-Optimization and Exploration in Emerging Scaling

SHF：小型：新兴扩展中的设计/技术协同优化和探索

• 批准号: 1718570
• 负责人: David Pan
• 金额: $ 45万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1718570&HistoricalAwards=false
• 关键词: SHF; Small; Design; Technology; Co

As the semiconductor industry reaches 10nm/7nm technology nodes and beyond, more innovations will be realized through alternative or emerging scaling such as novel process/material and device engineering to seek equivalent scaling effects. Such scaling demands closer design and technology co-optimization and explorations. This project will seek to develop novel CAD algorithms, tools, and methodologies to bridge the future IC design and technology gaps in the new era of emerging scaling. The proposed research, if successful, will have tremendous impacts on the $350 billion semiconductor industry which is at a critical point, transitioning from conventional scaling to emerging scaling. The PI will work with industry collaborators for technology transfer to benefit the overall industry and society. The highly interdisciplinary nature of this research will be tightly integrated into curriculum development and student training/outreach activities. The technical problems to be addressed include: (1) new sub-resolution directed-self-assembly (DSA) patterning and layout co-optimizations; (2) new mask and layout optimizations for future hybrid lithography technologies considering emerging processes/materials and design requirements; (3) future standard cell and routing architecture exploration; (4) physical design for next-generation nano-devices.

随着半导体行业达到10 nm/7 nm技术节点及以上，更多的创新将通过替代或新兴的缩放实现，如新工艺/材料和器件工程，以寻求等效的缩放效果。这种规模化需要更紧密的设计和技术共同优化和探索。该项目将寻求开发新的CAD算法，工具和方法，以弥合未来集成电路设计和技术差距，在新兴的缩放新时代。拟议的研究如果成功，将对价值3500亿美元的半导体行业产生巨大影响，该行业正处于从传统规模扩展向新兴规模扩展过渡的关键时刻。PI将与行业合作者合作进行技术转让，以造福整个行业和社会。这项研究的高度跨学科性质将紧密结合到课程开发和学生培训/推广活动。要解决的技术问题包括：（1）新的亚分辨率定向自组装（DSA）图案化和布局协同优化;（2）考虑新兴工艺/材料和设计要求的未来混合光刻技术的新掩模和布局优化;（3）未来标准单元和布线架构探索;（4）下一代纳米器件的物理设计。

项目成果

ISOP: Machine Learning-Assisted Inverse Stack-Up Optimization for Advanced Package Design

• DOI: 10.23919/date56975.2023.10137055
• 发表时间: 2023-04
• 期刊: 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE)
• 作者: Hyunsu Chae;B. Mutnury;Keren Zhu;D. Wallace;D. Winterberg;D. D. Araujo-D.;J. Reddy;Adam R. Klivans;D. Pan

MLCAD: A Survey of Research in Machine Learning for CAD Keynote Paper

• DOI: 10.1109/tcad.2021.3124762
• 发表时间: 2022-10
• 期刊: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
• 影响因子: 2.9
• 作者: Martin Rapp;H. Amrouch;Yibo Lin;Bei Yu;D. Pan;M. Wolf;J. Henkel

A Tale of EDA's Long Tail: Long-Tailed Distribution Learning for Electronic Design Automation

EDA 长尾故事：电子设计自动化的长尾分布学习

• DOI: 10.1145/3551901.3556485
• 发表时间: 2022
• 期刊: MLCAD '22: Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD
• 作者: Zixuan Jiang, Mingjie Liu

Semi-Supervised Hotspot Detection with Self-Paced Multi-Task Learning

• DOI: 10.1145/3287624.3287685
• 发表时间: 2019-01
• 期刊: 2019 24th Asia and South Pacific Design Automation Conference (ASP-DAC)
• 作者: Ying Chen;Yibo Lin;Tianyang Gai;Yajuan Su;Yayi Wei;D. Pan

LithoGAN: End-to-End Lithography Modeling with Generative Adversarial Networks

• DOI: 10.1145/3316781.3317852
• 发表时间: 2019-06
• 期刊: 2019 56th ACM/IEEE Design Automation Conference (DAC)
• 作者: Wei Ye;M. Alawieh;Yibo Lin;David Z. Pan