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.

蓝精灵：MEMRISTORSORTRASTRATS的统计建模，仿真和鲁棒设计技术，第四基本的被动电路元素＆＃8722; Memristor在大量数据存储，神经形态计算，信号处理，生物医学实验室中具有巨大潜力稳健性。智能优点：这项研究包括三个集成的组成部分：（1）将开发代表性Memristor Technologies的统计设备模型，即Tio2薄膜和自旋磁带，以促进工艺变化意识到的空间探索； （2）将为基于Memristor的VLSI系统设计和仿真开发快速蒙特卡洛模拟平台； （3）通过利用统计备忘录模型和仿真平台，将研究可最大程度地减少过程变化引起的电气性能的波动的鲁棒电路设计技术。BROADER的影响：这项研究提供了一项全面的设计包，可有效地将MEMRistor整合到现有的VLSI系统中，以提供更好的VLSI Systems，以提供更好的性能和功能消耗。设备工程师和电路设计师受研究创新的桥接和教育。 在与领先的行业合作伙伴的密切合作下，开发的技术可以直接转移到行业应用程序，并直接影响未来的基于Memristor的VLSI系统。协作中的活动还包括有关项目技术方面和新课程开发的主要会议的教程。