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.

传导电子和离子的塑料在许多应用中都很重要，从测量大脑活动的传感器到储存能量的电池。这些材料的一个新兴应用是在计算机中，可以在硬件层面模仿人类大脑的学习能力。这种受生物启发的或神经形态的计算机有可能复制学习过程，并以比今天的计算机更快、更低的能耗执行关键任务。就像大脑中的神经元可以通过反复激活来“学习”一样，基于这些导电塑料的设备可以根据输入改变导电电子的容易程度。该领域的一个关键限制是了解离子和电子如何随时间一起穿过这些材料。例如，目前尚不清楚为什么塑料可以非常缓慢地变化以达到高电导状态，但当关闭到低电导状态时，它们表现出快速变化。该项目使用一系列不同的技术来研究这些特性，这些技术测量了材料的固体结构、器件的几何形状和系统的化学特性对电气设备性能的影响。从这个项目中获得的科学知识将使人们更好地理解如何设计聚合物和其他材料来制造更好的下一代计算设备。该项目还通过开发新的推广材料和支持帮助第一代大学生实现科学事业的当地组织，扩大了首席研究员在教育方面的作用。技术描述本项目的科学目标是更好地理解控制神经形态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（OMIECs），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，导致电导状态的可调变化。这种特性使它们适用于通过电压诱导电导变化控制“学习”的应用，如基于硬件的人工神经网络。然而，目前尚不清楚omiec的不同化学和形态特性如何控制离子传输动力学、滞后和非线性响应。一个成功的神经形态器件应该能够快速改变电导，长时间保持状态，并根据应用做出线性或高度非线性的响应。该项目通过研究不同聚合物和反离子组合的离子注入和排出动力学，探索动力学、非线性和几何尺度的相互关联因素；2)由有源层和栅电极组成引起的探测非线性；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，从而将器件结构中的几何缩放与神经形态功能联系起来。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、时间分辨光学显微镜和电化学晶体管器件测量，以深入了解omiec和局部结构中的特征长度尺度如何影响测量传输特性和神经形态器件功能。这里的研究活动为了解各种化学和形态因素如何相互关联提供了重要的见解，同时也为合理设计更好的共轭聚合物和其他神经形态应用材料提供了指导。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。