CAREER: Entropy Oxide Memristors for Software-equivalent Neuromorphic Computing

职业:用于软件等效神经形态计算的熵氧化物忆阻器

基本信息

  • 批准号:
    2239951
  • 负责人:
  • 金额:
    $ 50.06万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2023
  • 资助国家:
    美国
  • 起止时间:
    2023-02-15 至 2028-01-31
  • 项目状态:
    未结题

项目摘要

Training of artificial intelligence (AI) is foreseen to consume exponential amounts of computing resources in the next decade, at large environmental and financial costs. Memristive technology has shown significant potential to be part of high-density next generation neuromorphic processors to support energy efficient AI algorithms. The physics of memristive switching is promising since in theory, the device state can change through the movement of only few ions under very low voltage. However, device non-idealities have prevented using memristor chips for neural network training due to performance sub-par to software solutions. Memristors and other emerging devices promise major societal impact via key applications e.g. medical implants, robotics, Internet of Things, etc. that need compact and efficient computing. However, their commercial adoption requires academic leaps in performance, yield, reliability and workforce training. This project will develop the next generation of memristors, based on entropy-stabilized oxides with engineered switching dynamics. This work will enable the discovery of new materials for memristor devices and support the development of efficient computing technologies, in line with the national focus on semiconductor competitiveness and next generation microelectronics. Moreover, this project seamlessly integrates the research and education objectives to support the necessary workforce development in this field. The efforts include overhaul of a nanoelectronics course to make it more accessible, interdisciplinary research training for diverse students, the development of a comprehensive virtual reality memristor simulator, providing github documentation to the community and outreach to local high schools. The developed models and experimental platforms will give students a hands-on education on these devices, their manufacturing, and their use in neuromorphic hardware for novel applications.This CAREER project aims to create ultra-low variability analog memristors by exploiting the underlying material properties and physical phenomena in entropy-stabilized oxides. By comparison with existing approaches attempting structural filament confinement at mesoscopic (micron) scales using phase separation, this project proposes filament confinement at nanometer scales for better switching uniformity. Entropy-stabilized oxides will be explored because they provide a broad compositional space and potential for high-quality films deposited at CMOS-suitable temperatures where the requirements for desired switching dynamics can be met. These complex oxides will be used to investigate the nanoscale control of the filament dynamics by allowing the oxygen vacancies to preferentially move along energetically-favorable trajectories, while the lattice is entropy-stabilized. To this effect, the work will innovate across four intertwined research and educational objectives. Objective #1 will focus on the identification of suitable entropy-stabilized films and their use in engineering memristor devices with ultra-low variability. Objective #2 will integrate and characterize these entropy-stabilized memristors on transistor chips using suitable buffer layers. Objective #3 will use the experimental dataset to develop multi-dimensional device models useful in neural network simulations and use the fabricated chips to prototype perceptrons and transformer neural networks with software-equivalent accuracy. Objective #4 will target the development and incorporation of experiential learning activities to support the training of diverse students in the field. This project will make critical advances in the understanding of the underlying material and physical requirements for ultra-low variability memristors and investigate their potential performance for next generation artificial intelligence hardware.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.
人工智能(AI)的训练预计将在未来十年消耗指数级的计算资源,并带来巨大的环境和财务成本。忆阻技术已经显示出巨大的潜力,可以成为高密度的下一代神经形态处理器的一部分,以支持节能的人工智能算法。记忆开关的物理学是很有前途的,因为从理论上讲,在极低的电压下,仅通过少数离子的运动就可以改变器件的状态。然而,由于软件解决方案的性能低于标准,器件的非理想性阻碍了使用忆阻芯片进行神经网络训练。忆阻器和其他新兴设备通过关键应用(如医疗植入物、机器人、物联网等)承诺对社会产生重大影响,这些应用需要紧凑高效的计算。然而,它们的商业应用需要在性能、产量、可靠性和劳动力培训方面取得学术上的飞跃。该项目将开发下一代忆阻器,基于具有工程开关动力学的熵稳定氧化物。这项工作将有助于发现忆阻器器件的新材料,并支持高效计算技术的发展,符合国家对半导体竞争力和下一代微电子技术的关注。此外,该项目无缝地整合了研究和教育目标,以支持该领域必要的劳动力发展。这些努力包括彻底改革纳米电子学课程,使其更容易获得,为不同的学生提供跨学科的研究培训,开发一个全面的虚拟现实忆阻器模拟器,向社区提供github文档,并向当地高中推广。开发的模型和实验平台将为学生提供有关这些设备的动手教育,它们的制造,以及它们在神经形态硬件中的新应用。这个CAREER项目旨在通过利用潜在的材料特性和熵稳定氧化物中的物理现象来制造超低可变性模拟忆阻器。通过比较现有的在介观(微米)尺度上使用相分离的结构灯丝约束方法,本项目提出了在纳米尺度上的灯丝约束,以获得更好的开关均匀性。熵稳定氧化物将被探索,因为它们提供了广阔的成分空间和潜力,可以在cmos合适的温度下沉积高质量的薄膜,从而满足所需的开关动力学要求。这些复杂的氧化物将用于研究纳米级控制的细丝动力学,通过允许氧空位优先沿着有利于能量的轨迹移动,而晶格是熵稳定的。为此,这项工作将在四个相互交织的研究和教育目标上进行创新。目标1将着重于确定合适的熵稳定薄膜及其在超低变异性工程记忆电阻器件中的应用。目标#2将使用合适的缓冲层在晶体管芯片上集成和表征这些熵稳定的忆阻器。目标#3将使用实验数据集开发在神经网络模拟中有用的多维设备模型,并使用制造的芯片以软件等效的精度原型感知器和变压器神经网络。目标4的目标是开发和整合体验式学习活动,以支持该领域不同学生的培训。该项目将在理解超低可变性忆阻器的基本材料和物理要求方面取得关键进展,并研究其在下一代人工智能硬件中的潜在性能。该奖项反映了美国国家科学基金会的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。

项目成果

期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Using Virtual Reality Cleanroom Simulation in a Mixed Nanoelectronics Classroom
在混合纳米电子学教室中使用虚拟现实洁净室模拟
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Gina Adam其他文献

Microarray standards at last
微阵列标准终于出台
  • DOI:
  • 发表时间:
    2002
  • 期刊:
  • 影响因子:
    64.8
  • 作者:
    Osama Yousuf;Imtiaz Hossen;Andreu Glasmann;Sina Najmaei;Gina Adam
  • 通讯作者:
    Gina Adam
Neural Network Modeling Bias for Hafnia-based FeFETs
Hafnia 基 FeFET 的神经网络建模偏差

Gina Adam的其他文献

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{{ truncateString('Gina Adam', 18)}}的其他基金

CRII: FET: Embedded neuromorphic circuits for real-time closed-loop biosensor data processing
CRII:FET:用于实时闭环生物传感器数据处理的嵌入式神经形态电路
  • 批准号:
    1948127
  • 财政年份:
    2020
  • 资助金额:
    $ 50.06万
  • 项目类别:
    Standard Grant

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