FET: Small: Hybrid Electrical, Ionic, and Biocompatible Artificial Synaptic Transistors

FET：小型：混合电气、离子和生物相容性人工突触晶体管

• 批准号: 2246855
• 负责人: Jean Anne Incorvia
• 金额: $ 60万
• 依托单位: University of Texas at Austin
• 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=2246855&HistoricalAwards=false
• 关键词: FET; Small; Hybrid; Electrical; Ionic

Traditional computing systems have fundamental drawbacks that limit the capability of data storage and processing. Unlike conventional systems, the human brain operates through electrochemical processes, enabling parallel computing and efficient data storage. This project aims to overcome limitations in traditional computing systems by exploring the potential of bioelectronic neuromorphic computing. While progress has been made in developing brain-inspired neuromorphic hardware, there remains a gap between these artificial architectures and biological systems. To address the gap, the project focuses on creating hybrid bioelectronic neuromorphic computing systems that integrate artificial synapses with live neuronal networks. This novel technology holds immense promise across multiple domains. In neuroscience, it offers an artificial platform for studying synapse responses to electrical and ionic signals. In computing, it enables efficient processing of unstructured data, emulating the brain's capabilities. Moreover, in biomedical applications, it facilitates seamless integration between biological systems and computers, opening doors for advanced bioelectronic hybrids. In the long run, these devices could be used as brain implants, allowing the replication of brain behavior and the development of next-generation prosthetic devices for treating neurodegenerative diseases like Parkinson's and Alzheimer's. The impact of this research will be further broadened through graduate student training; year-round involvement of undergraduate students in the research, set up so they can significantly contribute to the project; leadership efforts in developing and leading a program to prepare undergraduates in electrical engineering for graduate school; and dissemination of educational videos to increase awareness and interest in this interdisciplinary area. The investigators propose the design of novel artificial synaptic devices and arrays based on graphene transistors to meet the necessary criteria for seamless integration and response to biological signals. Building upon recent innovations, the team has developed artificial synaptic transistors using fully biocompatible materials, including bilayer graphene and Nafion-based compounds. These transistors also exhibit significantly low switching energy. The project's key objectives are scaling the graphene artificial synaptic transistors to biologically-relevant sizes and investigating individual device response and array behavior. The devices will be engineered to respond to both electrical and ionic signals, specifically potassium (K+) and sodium (Na+) ions. The devices' channel conductance, which corresponds to memory states, will be manipulated by applying electrical pulses to the Nafion gate. This process facilitates the movement of protons through the Nafion body, leading to controlled changes in conductance. A comprehensive approach combining data-driven multidimensional modeling and experimental array testing will be used to validate the functionality and performance of the devices and their arrays as artificial neural networks. Through a combination of modeling and experimental validation, the project aims to develop a cutting-edge neuromorphic system that closely emulates the properties of mammalian neurons.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.

传统的计算系统具有限制数据存储和处理能力的基本缺点。与传统系统不同，人脑通过电化学过程运行，实现并行计算和有效的数据存储。该项目旨在通过探索生物电子神经形态计算的潜力来克服传统计算系统中的局限性。尽管在开发受脑启发的神经形态硬件方面取得了进展，但这些人工体系结构与生物系统之间仍然存在差距。为了解决差距，该项目致力于创建将人工突触与实时神经元网络集成的混合生物电子神经形态计算系统。这项新型技术在多个领域中拥有巨大的希望。在神经科学中，它提供了一个人工平台，用于研究突触对电信和离子信号的反应。在计算中，它可以有效地处理非结构化数据，从而模仿大脑的功能。此外，在生物医学应用中，它有助于生物系统与计算机之间的无缝整合，并为晚期生物电子杂种开门。从长远来看，这些设备可以用作大脑植入物，从而可以复制大脑行为和开发下一代假体设备，用于治疗帕金森氏症和阿尔茨海默氏症等神经退行性疾病。这项研究的影响将通过研究生培训进一步扩大；本科生全年参与研究，以便他们可以为该项目做出重大贡献；领导努力在制定和领导一项计划，为研究生院的电气工程上的本科生做准备；并传播教育视频，以提高对这个跨学科领域的意识和兴趣。研究人员提出了基于石墨烯晶体管的新型人工突触设备和阵列的设计，以满足无缝整合和对生物信号的反应的必要标准。在最近的创新基础上，该团队使用完全生物相容性的材料（包括双层石墨烯和基于Nafion的化合物）开发了人工突触晶体管。这些晶体管还表现出明显低的开关能量。该项目的主要目标是将石墨烯的人工突触晶体管扩展到与生物学相关的大小，并研究单个设备响应和数组行为。这些设备将经过设计以响应电信和离子信号，特别是钾（K+）和钠（Na+）离子。设备的通道电导与内存状态相对应，将通过将电脉冲施加到Nafion Gate来操纵。这个过程促进了质子通过Nafion体的运动，从而导致电导的控制变化。将数据驱动的多维建模和实验阵列测试结合在一起的综合方法将用于验证设备的功能和性能及其作为人工神经网络的功能和性能。通过建模和实验验证的结合，该项目旨在开发一个尖端的神经形态系统，该系统紧密模仿了哺乳动物神经元的特性。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和更广泛影响的审查审查标准来通过评估来通过评估来获得支持的。