Plasmon-Enhanced FerroElectric Discovery

我们生活在一个信息驱动的社会，我们看到以数据为中心的技术激增，例如自动驾驶汽车、数据中心、物联网和人工智能。数据复杂性呈爆炸性增长，数据中心消耗的能源占全球总能源使用量的比例越来越大。在当前的冯·诺伊曼体系结构中，高达80%的计算能量被消耗在互连上的逻辑和存储器之间的数据传输瓶颈上。为了突破这一限制，需要新类型的设备。神经形态系统模仿大脑神经系统，在认知和数据密集型任务中表现出色，提供高计算效率和低功耗。铁电存储器可以为非易失性存储器和神经形态计算提供所需的技术。等离子体FED通过实现光学可访问的存储器件来研究环境条件下的铁电开关材料，在器件运行期间实时和原位地将低能纳米器件工程和等离子体增强的光-物质相互作用联系起来。我们设计了一种非破坏性技术，能够避免传统电子显微镜技术中存在的开关过程中的电子诱导扰动。工业标准材料HfO2将被探索，但具有铁电和离子导电性的全新多功能。等离子体FED将专注于在手术中获得这种混合开关过程的纳米级，解决目前RRAM中的稳定性问题。等离子体FED还将解决目前铁电随机存取存储器中的可扩展性和可靠性问题，旨在了解氧空位、缺陷和磁畴壁在HfO2中的作用。还将提出光触发铁电开关的概念。等离子体FED将为材料和设备工程师提供关键知识，以指导创造具有无与伦比的性能的设备。潜在的大赢家是基于HfO2的新设备，用于存储和人工智能应用。

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.