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.

该补助金支持研究，以促进为制造未来人工智能系统而创建电子设备的关键知识和技术，以提高美国的技术竞争力和国家繁荣。目前的人工智能系统，如人工神经网络，仍然是基于传统的计算原理，这并不匹配生物神经元的过程，并导致一个可怕的计算复杂性和不可接受的功耗的放大实现。为了应对这一挑战，该提案支持基础研究，以探索关键的器件物理知识，以实现基于具有高度生物相似性的2D纳米材料的新型忆阻开关器件（或忆阻器），从而可能实现生物神经元功能的仿真。从这样的设备构建的基于硬件的人工神经网络系统被预期能够执行新兴的类脑神经形态计算算法，并且能够实现上级推理能力以及与生物对应物相当的功率效率。这种神经网络系统如果开发成功，可以应用于广泛的应用，例如无人驾驶车辆的控制，复杂的计算机视觉数据的处理，以及基于机器学习的疾病快速诊断，从而大大提高系统的数据处理能力。此外，这项工作的科学和技术成果还将促进开发先进计算和机器人系统的能力。这项研究还提高了学生和教育工作者对电子、集成电路芯片、先进控制和计算技术相关教育活动的参与。新提出的2D半导体忆阻器与基于体材料的忆阻器相比，预计将表现出几个有利的特性，包括无悬挂键合表面，其潜在地使得能够成本有效地生产具有更高器件集成密度、更低阈值电压和用于切换状态的能量、器件之间更高水平的互连性以及更大数量的可用设备状态。这些理想的属性可以进一步用于解决与基于硬件的神经网络相关的上述挑战。尽管有这些预期的优点，基于2D半导体忆阻器的神经网络系统的最终实现需要研究工作来解决几个重要的面向设备的挑战。具体地，2D半导体忆阻器的突触权重更新特性需要被改进为响应于脉冲状编码信号而线性和对称，并且需要新的器件掺杂/集成技术来形成用于仿真生物现实功能的不同突触区域。此外，需要进行更多的实验尝试来构建由2D半导体忆阻器组成的小规模网络，寻求探索神经形态计算算法，该算法可以充分收获基于2D半导体的忆阻器件在处理动态时空信号中的上述优点。为了应对这些挑战，PI将执行一系列研究任务，以生产适用于实际网络实施的可靠的2D半导体忆阻器，并初步展示用于神经形态控制应用的小规模网络。具体的次级目标包括：（1）深入了解微观层面的2D半导体忆阻器的忆阻开关方案，生产具有改进突触权重更新特性的忆阻器;（2）实现具有确定性和均匀突触特性的2D忆阻器的可扩展集成;（3）在法庭上证明─由2D半导体忆阻器组成的用于时间数据分析的规模网络。该奖项反映了NSF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。