EAGER: Ferroelectric Memristive Devices Emulating Synapses in Subcortical Information Processors

EAGER：铁电忆阻器件模拟皮层下信息处理器中的突触

• 批准号: 1445386
• 负责人: Santosh Kurinec
• 金额: $ 15.96万
• 依托单位: Rochester Institute of Tech
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-01 至 2017-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1445386&HistoricalAwards=false
• 关键词: EAGER; Ferroelectric; Memristive; Devices; Emulating

Neuromorphic computing is an interdisciplinary field that aspires to create physical architecture and design principles based on biological nervous systems for applications in vision systems, auditory systems and autonomous robots. There is an increasing interest in developing electronic analog circuits to mimic neuro-biological architectures present in the nervous system. In the nervous system of the brain, a synapse is a structure that permits a neuron to pass an electrical or chemical signal to another cell. This exploratory research proposes to create a two-terminal memristive device that can emulate the function of a synapse. The resistance of the device will change depending on the amount, direction, and duration of voltage applied. The device has advantage of maintaining its state until another voltage pulse is applied over conventional computer memory, which requires regular charge to maintain its state. The principle behind the proposed approach is to make dipoles in a film switch up or down depending on voltage polarity in ferroelectric materials. If the thickness of the ferroelectric layer is made small enough, it can allow tunneling of electrons that is a function of the relative density of dipoles in up or down position thus preserving a memory similar to that of the synapse, thereby making electronic analog circuits to mimic brain. The proposed novel synapse circuits will enable integration of complex systems with power-constrained devices. The research on hafnium oxide (HfO2) based FTJ will also open the door for further scaling of FE capacitor based random access memory and enable the fabrication of FE field effect transistor (FE-FET) based memory. The graduate students would have a significant opportunity in advancing their interdisciplinary skills in semiconductors device design and fabrication, integrated circuit design, machine learning, and neuroscience. This proposal explores a two-terminal memristive device based on newly discovered ferroelectricity in CMOS compatible high permittivity dielectric, HfO2 doped with silicon or aluminum. The switching mechanism in FTJ is driven by polarization that is relatively immune from stochastic variations observed in other resistive memory devices. Fabrication of a high permittivity HfO2 based ferroelectric device will allow thinner films which can be scaled. The goal of the proposed research is to explore the design and fabrication of HfO2 based ferroelectric tunnel junction (FTJ) memristive device and its characterization to generate models. In addition, neuron circuits with multiple signaling types for behavioral emulation of biological neurons will be designed, synaptic circuits based on the proposed memristor device models will be trained, and the feasibility of incorporating the neuron and synapse circuits into subcortex-inspired information processing (SIIP) system will be evaluated. The outcome of the proposed exploratory research will result in a novel device that can mimic synaptic behavior and can be integrated with conventional CMOS electronics thereby forming the basis for the next generation of intelligent computing.

神经形态计算是一个跨学科的领域，它致力于创建基于生物神经系统的物理架构和设计原则，用于视觉系统、听觉系统和自主机器人。人们对开发电子模拟电路来模拟神经系统中存在的神经生物结构越来越感兴趣。在大脑的神经系统中，突触是一种允许神经元将电信号或化学信号传递给另一细胞的结构。这项探索性研究建议创造一种两端记忆装置，可以模仿突触的功能。该装置的电阻将根据施加的电压的大小、方向和持续时间而变化。该设备具有保持其状态的优点，直到在传统的计算机存储器上施加另一电压脉冲为止，这需要规则的充电来维持其状态。该方法的原理是使薄膜中的偶极子根据铁电材料中的电压极性进行上下切换。如果铁电层的厚度足够小，它可以允许电子隧穿，这是上下位置偶极子相对密度的函数，从而保留了类似突触的记忆，从而使电子模拟电路模仿大脑。拟议的新型突触电路将使复杂系统与功率受限的设备集成在一起。基于HfO2的FTJ的研究也将为基于FE电容的随机存取存储器的进一步扩展打开大门，并使基于FE-FET的存储器的制造成为可能。这些研究生将有一个重要的机会提高他们在半导体器件设计和制造、集成电路设计、机器学习和神经科学方面的跨学科技能。这一方案探索了一种基于新发现的铁电性的双端记忆器件，这种铁电性是在掺硅或铝的高介电常数介质HfO2中发现的。FTJ中的开关机制是由极化驱动的，极化相对不受其他阻性存储设备中观察到的随机变化的影响。高介电常数HfO2铁电器件的制备将使薄膜变得更薄，而且可以按比例调整。本研究的目标是探索基于HfO2的铁电隧道结(FTJ)记忆器件的设计和制造，并对其进行表征以生成模型。此外，还将设计用于生物神经元行为仿真的具有多种信号类型的神经元电路，将训练基于所提出的忆阻器器件模型的突触电路，并将评估将神经元和突触电路纳入皮层下启发信息处理(SIIP)系统的可行性。拟议中的探索性研究的结果将导致一种新的设备，它可以模拟突触行为，并可以与传统的cmos电子设备集成，从而为下一代智能计算奠定基础。