Collaborative Research: RUI: Natural Bio-organic Resistive Random Access Memory Based Synaptic Devices

合作研究：RUI：基于天然生物有机电阻随机存取存储器的突触器件

• 批准号: 2104976
• 负责人: Feng Zhao
• 金额: $ 33.73万
• 依托单位: Washington State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2104976&HistoricalAwards=false
• 关键词: Collaborative; Research; RUI; Natural; Bio

Two essential challenges faced globally by computing systems today are tremendous energy consumption and electronic wastes. One potential solution to simultaneously address these two issues is by “brain-like” and “green” neuromorphic computing with energy-efficient operation and biodegradable disposals. Neuromorphic computing systems require hardware components capable of mimicking human synapse - the basic building block of biological neural networks, while natural bio-organic materials derived from living or once-living organisms such as plants, animals or microbial materials are renewable, sustainable, biocompatible, biodegradable, and abundant in nature. The proposed research will advance the development of nanoscale, ultrahigh-density and wafer-level manufacturing of natural bio-organic materials based resistive random access memory through nanofabrication and machine learning, and implementation of bio-organic materials based resistive memory in neural networks with high accuracy and efficiency for “green” neuromorphic systems. This project has great impacts on US and global societies and provides many societal benefits. The neuromorphic systems using bio-organic materials based resistive memory are desirable for stretchable, flexible and wearable electronics in personal health and biomedical applications, and address the sustainable and environmental issues brought by excessive exploitation of non-renewable resources for electronics and disposal of electronic devices. The interdisciplinary nature of this research project covers the understanding and practice in nanotechnology, non-volatile memory, neuron and synapse, neuromorphic computing systems and machine learning, which provide a perfect venue for integration of research and education. Minority, female and high school students will be mentored to perform research in nanotechnology and machine learning. A virtual reality based interactive system will be developed to provide trainings of resistive memory and synaptic device fabrication in a virtual cleanroom environment. Workshops will be organized for broadening dissemination and community outreach. The research aims to address technological challenges hampering the development of bio-organic materials based resistive memory and artificial synaptic devices. These challenges include the fabrication of nanoscale, high-density and scalable bio-organic materials based resistive memory and synaptic devices and incorporation of these devices in the neural network with high accuracy and efficiency. In this project, advanced nanotechnology and nanofabrication techniques will be developed to fabricate nanometer-sized crossbar electrodes for nanoscale and high-density bio-organic materials based resistive memory. Machine learning algorithms will be employed to study the correlation of biomaterial film process and property, device switching characteristics and synaptic behaviors. Synaptic architectures based on nanoscale bio-organic materials based resistive memory will be developed to emulate synaptic plasticity and synaptic efficacy. Implementation of bio-organic materials based resistive memory and synaptic devices in neural networks and evaluation of the learning capability will be carried out by leveraging a coherent hardware and software co-design. This project is potentially transformative and will achieve a breakthrough in the realization of nanoscale, ultrahigh-density and wafer-level manufacturing of resistive switching memory and artificial synaptic devices based on natural bio-organic materials. The research outcomes will expedite device development by accurate process optimization and establish a fundamental understanding of natural bio-organic materials based resistive switching memory and synaptic devices when used in the neural networks for “green” neuromorphic computing systems.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.

当今计算系统在全球面临的两个基本挑战是巨大的能源消耗和电子垃圾。同时解决这两个问题的一个潜在解决方案是通过“类脑”和“绿色”神经形态计算，以及节能操作和可生物降解的处置。神经形态计算系统需要能够模拟人类突触的硬件组件-生物神经网络的基本组成部分，而来自活的或曾经活过的有机体(如植物、动物或微生物材料)的天然生物有机材料是可再生的、可持续的、生物兼容的、可生物降解的，并且在自然界中含量丰富。这项研究将通过纳米制造和机器学习，推动基于天然生物有机材料的阻性随机存储器的纳米级、超高密度和晶片级制造的发展，并在神经网络中实现基于生物有机材料的阻性存储器的高精度和高效率，用于绿色神经形态系统。该项目对美国和全球社会产生了巨大影响，并提供了许多社会效益。基于生物有机材料的阻性记忆的神经形态系统是个人健康和生物医学应用中可伸展、灵活和可穿戴的电子设备所必需的，并解决了过度开发不可再生电子资源和处置电子设备带来的可持续和环境问题。这一研究项目的跨学科性质涵盖了对纳米技术、非易失性存储器、神经元和突触、神经形态计算系统和机器学习的理解和实践，这些领域为研究和教育的整合提供了一个完美的场所。少数民族、女性和高中生将接受指导，进行纳米技术和机器学习方面的研究。将开发一个基于虚拟现实的交互系统，以在虚拟洁净室环境中提供阻性记忆和突触设备制造的培训。将举办讲习班，以扩大传播和社区外联。这项研究旨在解决阻碍基于生物有机材料的阻性记忆和人工突触设备发展的技术挑战。这些挑战包括基于纳米级、高密度和可扩展的生物有机材料的阻性记忆和突触器件的制造，以及将这些器件以高精度和高效率地整合到神经网络中。在本项目中，将发展先进的纳米技术和纳米制造技术，以制备用于纳米级和高密度生物有机材料阻性存储器的纳米级交叉棒电极。机器学习算法将被用来研究生物材料膜过程和特性、设备切换特性和突触行为的相关性。基于纳米生物有机材料阻性记忆的突触结构将被开发出来，以模拟突触的可塑性和突触的功效。基于生物有机材料的阻性记忆和突触装置在神经网络中的实现和学习能力的评估将通过利用连贯的硬件和软件共同设计来进行。该项目具有潜在的变革性，将实现基于天然生物有机材料的阻性开关存储器和人工突触器件的纳米级、超高密度和晶片级制造的突破。研究成果将通过精确的工艺优化来加快设备的开发，并建立对基于天然生物有机材料的阻性开关存储器和突触设备的基本理解，用于“绿色”神经形态计算系统的神经网络。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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

Study of synaptic properties of honey thin film for neuromorphic systems

• DOI: 10.1016/j.matlet.2021.131169
• 发表时间: 2021-11
• 期刊: Materials Letters
• 影响因子: 3
• 作者: Brandon Sueoka;K. Cheong;F. Zhao

Natural Organic Fructose-based Nonvolatile Resistive Switching Memory for Environmental Sustainability in Computing

基于天然有机果糖的非易失性电阻开关存储器，用于计算环境的可持续性

• DOI: 10.1109/drc58590.2023.10186891
• 发表时间: 2023
• 期刊: Device Research Conference
• 作者: Xing, Yuan;Zhao, Feng
• 通讯作者: Zhao, Feng

Natural Organic Carbohydrate Materials Based Resistive Random Access Memory for Sustainable Neuromorphic Computing Systems

用于可持续神经形态计算系统的基于天然有机碳水化合物材料的电阻式随机存取存储器

• 发表时间: 2023
• 期刊: 244th Electrochemical Society (ECS
• 作者: Zhao, Feng

Controlled Formation of Honey Carbon Nanotube Thin Films by Tailoring the Ratio of Admixture Concentration and Annealing Time

通过调整混合物浓度和退火时间的比例来控制蜂蜜碳纳米管薄膜的形成

• DOI: 10.1093/micmic/ozad067.066
• 发表时间: 2023
• 期刊: Microscopy and Microanalysis
• 影响因子: 2.8
• 作者: Hood, Kaleb;Tanim, Md Mehedi;Templin, Zoe;Dao, Annie;Zhao, Feng;Jiao, Jun
• 通讯作者: Jiao, Jun

A Machine Learning Approach to Support Neuromorphic Device Design and Microfabrication

• DOI: 10.1109/icmla58977.2023.00246
• 发表时间: 2023-12
• 期刊: 2023 International Conference on Machine Learning and Applications (ICMLA)
• 作者: A. Vicenciodelmoral;Md. Mehedi Hasan Tanim;Feng Zhao;Xinghui Zhao