What Controls Kinetics in Organic Mixed Conductors for Neuromorphic Computing and Beyond?

用于神经形态计算及其他领域的有机混合导体的动力学控制是什么？

• 批准号: 2309577
• 负责人: David Ginger
• 金额: $ 54.07万
• 依托单位: University of Washington
• 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: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2309577&HistoricalAwards=false
• 关键词: What; Controls; Kinetics; Organic; Mixed

Non-technical DescriptionPlastics that conduct both electrons and ions are important for many applications, from sensors that measure brain activity to batteries that store energy. One emerging application for these materials is in computers that can mimic the learning ability of the human brain at the hardware level. Such biologically inspired, or neuromorphic, computers could potentially replicate the learning process and perform key tasks faster, and with lower energy consumption, than today’s computers. Just as neurons in the brain can “learn” over time through repeated activation, devices based on these conducting plastics can change how easily they conduct electrons based on an input. One key limitation in this field is understanding how ions and electrons move through such materials together as a function of time. For example, it is not clear why plastics can change very slowly to reach a high-conductance state, yet they exhibit a rapid change when turned off to a low-conductance state. This project investigates these properties using a range of different techniques that measure how the electrical device performance is affected by the solid structure of the materials, the device geometry, and the chemical properties of the system. The scientific knowledge from this project will enable better understanding of how polymers and other materials can be designed for better next-generation computing devices. The project also extends the principal investigator’s role in education by developing new outreach materials and by supporting local organizations that help first-generation college students to achieve scientific careers. Technical DescriptionThe scientific goal of this project is to better understand the structure/function relationships that govern the performance of organic semiconductors in neuromorphic computing devices. Organic mixed ionic-electronic conductors (OMIECs), typically conjugated polymers, are well-suited to these systems because they can efficiently accommodate ions, resulting in tunable changes in conductance state. This property makes them amenable to applications where controlled “learning” via a voltage-induced conductance change is desired, as in hardware-based artificial neural networks. However, it is currently unclear how different chemical and morphological properties of OMIECs control ion transport kinetics, hysteresis, and non-linear response. A successful neuromorphic device should be able to change conductance quickly with long-lived state retention, and either linear or highly non-linear response depending on the application. This project explores the interconnected factors of kinetics, non-linearity, and geometric scaling by 1) investigating kinetics of ion injection and expulsion using different polymer and counterion combinations; 2) probing non-linearity due to active layer and gate electrode composition; and 3) testing how kinetics and non-linear responses in OMIECs translate to transport measurements in transistors to relate geometric scaling in the device architecture with neuromorphic function. To accomplish these goals, this project combines spectroelectrochemistry, electrical scanning probe microscopy, time-resolved optical microscopy, and electrochemical transistor device measurements to provide insight into how characteristic length scales in OMIECs and local structure affect the measure transport properties and neuromorphic device functionality. The research activity here provides important insight into how the various chemical and morphological factors are interrelated, while also providing guidance for the rational design of better conjugated polymers and other materials for neuromorphic applications.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.

进行电子和离子进行电子和离子的非技术描述塑料对许多应用都很重要，从测量大脑活动的传感器到存储能量的电池。这些材料的一种新兴应用是在计算机中可以模仿硬件层面人类大脑的学习能力的计算机。与当今的计算机相比，这种受生物学启发的计​​算机可以潜在地复制学习过程，并以较低的能耗来复制学习过程，并以较低的能量消耗执行关键任务。正如大脑中的神经元可以通过重复激活随着时间的流逝而“学习”一样，基于这些导电塑料的设备可以改变其基于输入的电子的容易导致电子的方式。该领域中的一个关键限制是了解离子和电子如何通过时间的函数一起通过此类材料。例如，尚不清楚为什么塑料能够非常缓慢地变化以达到高传导状态，但是当将其转换为低传导状态时，它们表现出迅速的变化。该项目使用一系列不同的技术研究了这些特性，这些技术衡量了电动器件性能如何受材料的固体结构，设备几何形状和系统的化学特性影响。该项目的科学知识将使人们可以更好地了解聚合物和其他材料如何设计用于更好的下一代计算设备。该项目还通过开发新的外展材料，并支持帮助第一代大学生实现科学职业的地方组织，从而扩展了主要研究者在教育中的作用。技术描述该项目的科学目标是更好地了解控制神经塑形计算设备中有机半导体性能的结构/功能关系。有机混合离子电子导体（OMIEC）通常是共轭聚合物，非常适合这些系统，因为它们可以有效地容纳离子，从而导致电导状态的可调变化。该属性使它们适合通过电压诱导的电导变化受控的“学习”的应用，就像基于硬件的人工神经元网络一样。但是，目前尚不清楚OMIECS的化学和形态特性如何控制离子转运动力学，滞后和非线性反应。成功的神经形态设备应该能够通过长期保留率快速更换电导，并根据应用的不同线性响应或高度非线性响应。该项目探讨了动力学，非线性和几何缩放的互连因子，1）使用不同的聚合物和反子组合研究离子注射和驱动动力学； 2）由于活动层和门电极组成而引起的非线性探测； 3）测试OMIEC中的动力学和非线性响应如何转化为晶体管中的传输测量值，以将设备体系结构中的几何缩放与神经形态功能相关联。为了实现这些目标，该项目结合了光谱电化学，电扫描探针显微镜，时间分辨的光学显微镜以及电化学晶体管设备测量值，以提供有关OMIECS和局部结构中特征长度尺度的洞察力，并影响测量运输属性和神经形态设备的功能。这里的研究活动提供了有关如何相互关联的各种化学和形态因素的重要见解，同时还为更好地集成聚合物的合理设计和其他用于神经形态应用的材料提供了指导。该奖项反映了NSF的法定任务，并通过使用该基金会的智力功能和广泛的影响来评估NSF的法定任务。