Plasmon-Enhanced FerroElectric Discovery
Plasmon-Enhanced FerroElectric Discovery
批准号:
EP/X034593/1
负责人:
Giuliana Di Martino
金额:
$211.03万
依托单位:
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
项目状态:
未结题
起止时间:
2024 至 --
中文摘要
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英文摘要
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.
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