Collaborative Research: SMURFS: Statistical Modeling, SimUlation and Robust Design Techniques For MemriStors

合作研究：SMURFS：忆存的统计建模、模拟和鲁棒设计技术

• 批准号: 1311747
• 负责人: Hai Li
• 金额: $ 25.01万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-02-01 至 2016-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1311747&HistoricalAwards=false
• 关键词: Collaborative; Research; SMURFS; Statistical; Modeling

SMURFS: Statistical Modeling, SimUlation and Robust Design Techniques For MemriStorsAbstractThe fourth fundamental passive circuit element − memristor, has demonstrated great potentials in massive data storage, neuromorphic computing, signal processing, biomedical lab-on-a-chip, sensing etc. The objective of this research is to investigate the design implications of process variations and environmental fluctuations to memristor-based VLSI systems, to exploit a fast statistical simulation technique, and to explore new circuit techniques to improve the memristive system reliability and robustness.Intellectual Merit: This research includes three integrated components: (1) The statistical device models for representative memristor technologies, i.e., the TiO2 thin-film and spintronic memristors, will be developed to facilitate the process variation aware design space explorations; (2) The fast Monte-Carlo simulation platform will be developed for memristor-based VLSI system designs and simulations; (3) By leveraging the statistical memristor models and simulation platform, robust circuit design techniques that can minimize the fluctuations of the electrical properties caused by process variations will be investigated.Broader Impacts: This research provides a comprehensive design package for efficiently integrating memristor into existing VLSI systems to offer better performance and power consumption. The device engineers and the circuit designers are well bridged and educated by the research innovations. The developed techniques can be directly transferred to industry applications under the close collaborations with leading industry partners, and directly impact the future memristor-based VLSI systems. The activities in the collaboration also include the tutorials in the major conferences on the technical aspects of the projects and new course development.

SMURFS：存储器的统计建模、仿真和鲁棒设计技术&忆阻器在海量数据存储、神经形态计算、信号处理、生物医学芯片实验室、传感器等领域具有巨大的应用潜力。本研究的目的是研究忆阻器超大规模集成电路设计中工艺变化和环境波动的影响，开发一种快速的统计模拟技术，并探索新的电路技术，以提高忆阻系统的可靠性和鲁棒性。智力优势：这项研究包括三个集成组件：（1）代表性忆阻器技术的统计器件模型，即，二氧化钛薄膜和自旋电子忆阻器，以促进工艺变化意识的设计空间探索：（2）快速蒙特-卡罗模拟平台将开发用于忆阻器为基础的超大规模集成电路系统的设计和模拟;（3）通过利用统计忆阻器模型和仿真平台，我们会研究可靠的电路设计技术，以尽量减少因工艺变动而引致的电性波动。这项研究提供了一个全面的设计包，有效地集成忆阻器到现有的超大规模集成电路系统，以提供更好的性能和功耗。器件工程师和电路设计师很好地沟通和教育的研究创新。 在与业界领先合作伙伴的密切合作下，所开发的技术可以直接转移到行业应用中，并直接影响未来基于忆阻器的VLSI系统。合作的活动还包括在主要会议上就项目的技术方面和新课程开发提供辅导。