Design and Synthesis of Atomically Tunable Memristors

原子可调忆阻器的设计与合成

• 批准号: 2314401
• 负责人: Judy Wu
• 金额: $ 35万
• 依托单位: University of Kansas Center for Research Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2314401&HistoricalAwards=false
• 关键词: Design; Synthesis; Atomically; Tunable; Memristors

Neuromorphic computing (NC) is a brain-inspired computing that has recently emerged as a promising resolution to the von Neumann bottleneck of data movement in current computing based on separate processing and memory units. NC enables the information to be processed and stored in the same units and is expected to provide critical computing hardware for the emerging artificial intelligence (AI), machine learning, internet of things, etc., and may converge with quantum computing for quantum neuromorphic computing. Researchers have attempted to mimic biological brain operation by using artificial versions of the elements known as memristors. Memristor, regarded as the fourth fundamental circuit element next to resistor, capacitor, and inductor, consists of a thin dielectric (typically oxide) film of pinched hysteresis of resistance sandwiched with a pair of electrodes to mimic the functionality of biological brain operation. In memristors, the operations of both memory storage and computing are integrated in one device. However, as the field of NC evolves, the ability to control at the atomic scale the memristive resistance, switching speed, cycling endurance, among other performance criteria, becomes increasingly important as NC circuits potentially require devices with different performance capabilities closely integrated together. In particular, memristors with tunable properties are critical to mimic biological brain operation. Unfortunately, an atomic-scale control of the memristor parameters has not been achieved due to lack of control in growth of ultrathin (sub-3 nm) oxides films, resulting in defects that in turn lead to reduced energy efficiency, device non-uniformity and low yield in current memristors. The proposed research aims to address the challenges through a synergetic integration of atomically controlled synthesis of atomically tunable memristors based on ultrathin oxide atomic layer stacks (ALS), atomistic material simulation/modeling that predicts the physical properties of ALS, and advanced characterization both in situ on materials and ex situ on devices. The success of the project can have a broader impact on a large spectrum of commercial applications including NC, AI, quantum information science, etc. Scientifically, a long-standing question in material research is whether a few atomic layers stacked with an atomic precision can provide the functionality and large-area uniformity as required for advanced electronics. This question is driven by the continuous down-sizing of transistors in last five decades to currently sub-5 nm to meet the need in future electronics with functionality tuned with an atomic precision. Using an in vacuo ALD approach developed in PI’s prior NSF support, the proposed research aims to address the challenges through a synergetic integration of atomically controlled synthesis of oxide ALS guided by atomistic material simulation/modeling and advanced characterization both in situ and ex situ on materials and devices. Specifically in the two proposed aims, Aim 1 focuses on design and synthesis of ultrathin oxide ALS and Aim 2 investigates ALS properties at material and device levels. The intellectual merit of the proposed research is through a new fundamental understanding of physical properties of ultrathin oxide ALS that is crucial to achieving atomically tunable memristors for future electronics and computing and a transformative leap in developing novel approaches for design and synthesis of ultrathin ALS to enable new functionalities. The achieved atomically tunable memristors based on the ALS can have a broader impact on a large spectrum of commercial applications including neuromorphic and quantum computing, artificial intelligence, quantum information science, etc. The project emphasizes forefront education and the cutting-edge research capability which will attract high-quality students, especially those from underrepresented groups, to pursue careers in STEM fields and will further amplify the impacts towards producing a unique and diverse future workforce in science and engineering.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.

神经形态计算（NC）是一种脑启发式计算，最近出现了一个有前途的解决方案，以冯诺依曼瓶颈的数据移动在当前的计算基于单独的处理和存储单元。NC使信息能够在相同的单元中处理和存储，并有望为新兴的人工智能（AI），机器学习，物联网等提供关键的计算硬件，并且可以与量子计算收敛以用于量子神经形态计算。研究人员试图通过使用被称为忆阻器的人造元件来模仿生物大脑操作。忆阻器被认为是继电阻器、电容器和电感器之后的第四种基本电路元件，由夹有一对电极的电阻收缩滞后的薄电介质（通常为氧化物）膜组成，以模仿生物大脑操作的功能。在忆阻器中，存储器存储和计算的操作都集成在一个设备中。然而，随着NC领域的发展，在原子尺度上控制忆阻电阻、切换速度、循环耐久性以及其他性能标准的能力变得越来越重要，因为NC电路潜在地需要具有紧密集成在一起的不同性能能力的器件。特别是，具有可调特性的忆阻器对于模拟生物大脑操作至关重要。不幸的是，由于缺乏对纳米（sub-3 nm）氧化物膜的生长的控制，还没有实现忆阻器参数的原子尺度控制，导致缺陷，这些缺陷又导致当前忆阻器中降低的能量效率、器件不均匀性和低产量。拟议的研究旨在通过基于氧化物原子层堆叠（ALS）的原子可调忆阻器的原子控制合成的协同集成，预测ALS物理特性的原子材料模拟/建模，以及材料上原位和设备上的非原位先进表征来解决这些挑战。该项目的成功可以对包括NC，AI，量子信息科学等在内的大量商业应用产生更广泛的影响。 从科学角度来看，材料研究中一个长期存在的问题是，以原子精度堆叠的几个原子层是否可以提供先进电子产品所需的功能和大面积均匀性。这个问题是由晶体管在过去五十年中不断缩小尺寸到目前的5 nm以下所驱动的，以满足未来电子产品的需求，这些电子产品具有以原子精度调谐的功能。使用在PI先前的NSF支持中开发的真空ALD方法，拟议的研究旨在通过原子材料模拟/建模以及材料和设备上原位和非原位的先进表征指导的氧化物ALS的原子控制合成的协同集成来解决挑战。具体而言，在两个拟议的目标，目标1侧重于设计和合成的氧化物ALS和目标2调查ALS性能在材料和器件水平。拟议研究的智力价值是通过对氧化物ALS物理特性的新的基本理解，这对于实现未来电子和计算的原子可调忆阻器至关重要，并且在开发用于设计和合成ALS的新方法以实现新功能方面实现了变革性飞跃。基于ALS实现的原子可调忆阻器可以对神经形态和量子计算、人工智能、量子信息科学等广泛的商业应用产生更广泛的影响。该项目强调前沿教育和前沿研究能力，将吸引高素质的学生，特别是来自代表性不足群体的学生，该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。