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

当今全球计算系统面临的两个基本挑战是巨大的能源消耗和电子废物。同时解决这两个问题的一个潜在的解决方案是通过“类脑”和“绿色”神经形态计算，具有节能操作和可生物降解的功能。神经形态计算系统需要能够模仿人类突触的硬件组件-生物神经网络的基本构建块，而来自活的或曾经活的生物体（例如植物、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、生物相容的、可生物降解的，并且在自然界中丰富。拟议的研究将通过纳米纤维和机器学习推进基于天然生物有机材料的电阻式随机存取存储器的纳米级、超高密度和晶片级制造的发展，以及基于生物有机材料的电阻式存储器在神经网络中的实现，其具有高精度和高效率，适用于“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。使用基于生物有机材料的电阻存储器的神经形态系统对于个人健康和生物医学应用中的可拉伸、柔性和可穿戴电子器件是期望的，并且解决了过度开发用于电子器件的不可再生资源和电子器件的处置所带来的可持续性和环境问题。该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，为研究和教育的整合提供了一个完美的场所。少数民族，女性和高中学生将被指导进行纳米技术和机器学习的研究。一个基于虚拟现实的交互式系统将被开发，以提供在虚拟洁净室环境中的电阻式存储器和突触器件制造的培训。将举办讲习班，以扩大传播和社区外联。该研究旨在解决阻碍基于生物有机材料的电阻存储器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电阻存储器和突触器件的制造，以及以高精度和效率将这些器件并入神经网络中。本计画将发展先进的奈米科技与奈米纤维技术，以制作奈米尺寸的交叉电极，应用于奈米尺寸与高密度的生物有机材料为基础的电阻记忆体。机器学习算法将被用于研究生物材料膜的工艺和性质、器件开关特性和突触行为之间的相关性。将开发基于纳米级生物有机材料的电阻存储器的突触架构，以模拟突触可塑性和突触功效。基于生物有机材料的电阻存储器和神经网络中的突触器件的实现以及学习能力的评估将通过利用连贯的硬件和软件协同设计来进行。该项目具有潜在的变革性，将在实现基于天然生物有机材料的电阻开关存储器和人工突触器件的纳米级、超高密度和晶圆级制造方面取得突破。该研究成果将通过精确的工艺优化加快器件开发，并建立对天然生物有机材料为基础的电阻开关存储器和突触器件在用于“绿色”神经形态计算系统的神经网络时的基本理解。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。

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.