2D Semiconductor Memristors towards Neuromorphic Hardware Applications

面向神经形态硬件应用的 2D 半导体忆阻器

• 批准号: 2331169
• 负责人: Xiaogan Liang
• 金额: $ 36万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 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: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2331169&HistoricalAwards=false
• 关键词: 2D; Semiconductor; Memristors; towards; Neuromorphic

This grant supports research that advances key knowledge and techniques for creating electronic devices for fabrication of future artificial intelligence systems to enhance U.S. technological competitiveness and national prosperity. The current artificial intelligence systems, such as artificial neural networks are still based on conventional computing principles, which do not match biological neuronal processes and result in a formidable computing complexity and unacceptable power consumption for scale-up implementation. To address this challenge, this proposal supports fundamental research to explore critical device physics knowledge for the realization of new memristive switching devices (or memristors) based on 2D nanomaterials which have a high biological similarity, potentially enabling emulation of biological neuronal functions. The hardware-based artificial neural network systems constructed from such devices are anticipated to be capable of executing emerging brain-like neuromorphic computing algorithms and enable superior inference capability as well as power efficiency comparable to those of biological counterparts. Such neural network systems, if successfully developed could be implemented to a broad range of applications, such as controlling of unmanned vehicles, processing of complicated computer vision data, and rapid diagnosis of illness based on machine learning, thereby greatly improving the data processing capability of the systems. In addition, the scientific and technical results from this work will also promote capability in developing advanced computing and robotic systems. This research also enhances participation of students and educators from underrepresented groups in the education activities related to electronics, integrated circuit chips, advanced controlling and computing techniques.The newly proposed 2D semiconductor memristors are anticipated to exhibit several advantageous properties in comparison with state-of-the-art memristors based on bulk materials, including dangling-bond-free surfaces that potentially enable cost-efficient production of device structures with the higher device integration density, the lower threshold voltages and energies for switching states, the higher level of interconnectivity among devices, and the larger number of available device states. These desirable properties could be further leveraged for addressing the aforementioned challenge related to hardware-based neural networks. In spite of such anticipated advantages, the ultimate realization of the neural network systems based on 2D semiconductor memristors demands the research efforts to address several important device-oriented challenges. Specifically, the synaptic weight update characteristics of 2D semiconductor memristors need to be improved to be linear and symmetric in response to pulse-like encoding signals, and new device doping/integration techniques are needed to form different synaptic regions for emulating bio-realistic functions. In addition, more experimental attempts for constructing small-scale networks consisting of 2D semiconductor memristors need to be performed, seeking to exploring the neuromorphic computing algorithms that can fully harvest the aforementioned advantages of 2D semiconductor based memristive devices in processing dynamic spatiotemporal signals. To address these challenges, the PI will perform a series of research tasks to produce reliable 2D semiconductor memristors suitable for practical network implementation and also preliminarily demonstrate small-scale networks for neuromorphic control applications. The specific sub-aims include: (1) Obtain an in-depth understanding of the memristive switching schemes of 2D semiconductor memristors at the microscopic level and produce memristors with improved synaptic weight update characteristics; (2) Realize scalable integration of 2D memristors with deterministic and uniform synaptic properties; (3) Preliminarily demonstrate a small-scale network consisting of 2D semiconductor memristors for temporal data analysis.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.

这项赠款支持研究的研究，以提高关键知识和技术，以创建电子设备来制造未来人工智能系统，以增强美国的技术竞争力和国家繁荣。当前的人工智能系统（例如人造神经网络）仍基于常规计算原理，这些计算原理与生物学神经元过程不符，并导致可强大的计算复杂性和不可接受的功耗，以进行扩展实施。为了应对这一挑战，该提案支持基本研究，以探索基于2D纳米材料的新的元素开关设备（或回忆录）的关键设备物理知识，这些纳米材料具有很高的生物学相似性，并有可能使生物神经元功能效仿。预计由此类设备构建的基于硬件的人工神经网络系统能够执行新兴的脑样神经形态计算算法，并能够与生物学对应物相当。这样的神经网络系统，如果成功开发的话，可以针对广泛的应用程序实施，例如控制无人车辆，处理复杂的计算机视觉数据以及基于机器学习的快速诊断疾病，从而大大提高了系统的数据处理能力。此外，这项工作的科学和技术结果还将促进开发高级计算和机器人系统的能力。 This research also enhances participation of students and educators from underrepresented groups in the education activities related to electronics, integrated circuit chips, advanced controlling and computing techniques.The newly proposed 2D semiconductor memristors are anticipated to exhibit several advantageous properties in comparison with state-of-the-art memristors based on bulk materials, including dangling-bond-free surfaces that potentially enable cost-efficient production of device structures with the较高的设备集成密度，较低的阈值电压和转换状态的能量，设备之间的互连级别较高以及可用数量的可用设备状态。这些理想的属性可以进一步利用，以应对与基于硬件的神经网络有关的上述挑战。尽管具有这种预期的优势，但基于2D半导体备忘录的神经网络系统的最终实现仍需要研究工作，以应对几个重要的设备挑战。具体而言，需要改进2D半导体备忘录的突触重量更新特性，以响应类似脉冲的编码信号而进行线性和对称，并且需要新的设备掺杂/集成技术来形成不同的突触区域以模仿生物真实性功能。此外，还需要进行更多的实验尝试，用于构建由2D半导体候选人组成的小规模网络，需要进行探索，以探索可以完全收集基于2D半导体的上述优势的神经形态计算算法，这些算法是基于2D半导体的上述优势。为了应对这些挑战，PI将执行一系列的研究任务，以生成可靠的2D半导体回忆录，适合于实用网络实施，并且也初步展示了用于神经形态控制应用的小规模网络。特定的子AIM包括：（1）在微观级别上获得对2D半导体备忘录的回忆开关方案的深入了解，并产生具有改善突触重量更新特征的备忘录； （2）实现具有确定性和均匀突触特性的2D备再次介绍者的可扩展整合； （3）初步证明了一个小型网络，该网络由2D半导体候选人组成，用于时间数据分析。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估来评估值得支持的。