Atomically Thin Oxides for Ultralow Power Non-volatile Memory Technology
Atomically Thin Oxides for Ultralow Power Non-volatile Memory Technology
批准号:
2606804
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2021
资助国家:
英国
项目状态:
未结题
起止时间:
2021 至 --
中文摘要
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英文摘要
New high-performance, ultralow power non-volatile memory (NVM) technology is essential to a wide range of diverse and hugely growing data centric technologies spanning IoT, transport, medicine, security, entertainment, neuromorphic computing, and AI, all which will evolve significantly over the next decade, and in time will radically change the way we live. NVM is also essential for strongly improving the efficiency of energy-hungry data centres where memory accounts for a large fraction of the overall power usage. Memristors can mimic artificial neurons capable of both computing and storing data, and they have the potential to dramatically reduce the energy and time lost in conventional computers. Within the memristor structure itself, certain oxides (inc. TiO2 and HfO2) are excellent active layers, owing in part to labile oxygen vacancies affecting the resistance of the layer, upon an applied voltage. This project shall explore and focus on precisely engineered two-dimensional (2D) oxide materials to develop novel active 'switching' layers in memristors. This offers exciting potential for the following reasons: Firstly, we can downscale the overall design, making it more compact and avoid the 'short channel' effect, by confining electrons within the atomically-thin active region. Secondly, we can test novel van der Waals bound active layers where there could be new phenomena giving rise to enhanced switching. Lastly, the large range of available layered materials provides scope to explore a wealth of potential combinations as a multi-layered active layer. Guided first by a material down-selection process, we shall fabricate devices and characterise them using state-of-the-art facilities, all the while considering future potential use cases, with a special emphasis on neural network/machine learning applications.
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