Plasmon-Enhanced FerroElectric Discovery

等离激元增强铁电的发现

• 批准号: EP/X034593/1
• 负责人: Giuliana Di Martino
• 金额: $ 211.03万
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX034593%2F1
• 关键词: Plasmon; Enhanced; FerroElectric; Discovery

We live in an information driven society, where we see proliferation of data centric technologies, e.g. self- driving vehicles, data centres, IoT and AI. Data complexity is explosively growing and data centers consume an increasing fraction of total world energy use. With the current von Neumann architectures up to ~80% of the computing energy is consumed in the data-transfer bottleneck between logic and memory on interconnects. To progress beyond this limit new types of device are needed. Neuromorphic systems, mimicking the brain nervous system, shine for proficiency in cognitive and data-intensive tasks, providing high computing efficiencies and low power consumption. Ferroelectric memories could offer the required technology for both non-volatile memory and neuromorphic computing.PlasmoFED links low-energy nanoscale device engineering and plasmon-enhanced light-matter interactions by implementing optically accessible memory devices to investigate ferroelectric switching materials in ambient conditions, in real-time and in-situ during device operation. We devise a non-destructive technique able to avoid electron-induced perturbation of the switching process present in traditional electron microscopy techniques. The industry-standard material HfO2 will be explored but with entirely new multifunctionality of ferroelectricity and ionic conductivity. PlasmoFED will focus on nanoscopic in-operando access to this hybrid switching process, tackling the current problems of stability in RRAMs. PlasmoFED will also address the current problems of scalability and reliability in FeRAMs aiming to understand the role of oxygen vacancies, defects and domain wall propagation in HfO2. The concept of light triggered ferroelectric switching will also be developed. PlasmoFED will provide critical knowledge for materials and device engineers to guide the creation of devices of unparalleled performance. The potential big win is new devices based on HfO2 for memory and AI applications.

我们生活在一个信息驱动的社会中，在那里我们看到以数据为中心技术的扩散，例如自动驾驶车辆，数据中心，物联网和AI。数据复杂性正在爆炸性增长，数据中心消耗了全球总能源的越来越多的部分。在当前的von Neumann架构中，在互连上的逻辑和内存之间的数据传输瓶颈中消耗了高达约80％的计算能量。为了超越此限制，需要新的设备。神经形态系统，模仿脑神经系统，阐明认知和数据密集型任务，提供较高的计算效率和低功耗。铁电回忆可以为非挥发性记忆和神经形态计算提供所需的技术。质量链接链接低能纳米级纳米级设备工程和等离子体增强的轻度 - 轻度 - 轻质 - 轻度 - 轻度互动，通过实施可访问的环境环境条件，以实现环境环境，并在设备上实时和设备运行。我们设计了一种无损技术，能够避免传统电子显微镜技术中存在电子诱导的转换过程的扰动。将探索行业标准的材料HFO2，但具有全新的铁电性和离子电导率的多功能性。质子质量将集中于纳米镜室内访问此混合动力切换过程，从而解决了RRAM中稳定性的当前问题。质子质量还将解决旨在了解HFO2中氧空位，缺陷和域壁传播的作用的FERAM的当前问题。光触发铁电开关的概念也将开发。等离子将为材料和设备工程师提供关键知识，以指导创建无与伦比的性能设备。潜在的大胜利是基于HFO2的新设备，用于内存和AI应用程序。