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用于神经形态计算的基于氧化铪器件的多级电阻开关

Multi-level resistive switching in hafnium-oxide-based devices for neuromorphic computing.

作者信息

Hellenbrand Markus, MacManus-Driscoll Judith

机构信息

Department of Materials Science & Metallurgy, University of Cambridge, 27 Charles Babbage Rd, Cambridge, CB3 0FS, UK.

出版信息

Nano Converg. 2023 Sep 14;10(1):44. doi: 10.1186/s40580-023-00392-4.

Abstract

In the growing area of neuromorphic and in-memory computing, there are multiple reviews available. Most of them cover a broad range of topics, which naturally comes at the cost of details in specific areas. Here, we address the specific area of multi-level resistive switching in hafnium-oxide-based devices for neuromorphic applications and summarize the progress of the most recent years. While the general approach of resistive switching based on hafnium oxide thin films has been very busy over the last decade or so, the development of hafnium oxide with a continuous range of programmable states per device is still at a very early stage and demonstrations are mostly at the level of individual devices with limited data provided. On the other hand, it is positive that there are a few demonstrations of full network implementations. We summarize the general status of the field, point out open questions, and provide recommendations for future work.

摘要

在神经形态和内存计算这个不断发展的领域,已有多篇综述文章。其中大多数涵盖了广泛的主题,这自然是以牺牲特定领域的细节为代价的。在此,我们聚焦于基于氧化铪的器件用于神经形态应用的多级电阻开关这一特定领域,并总结近年来的进展。虽然基于氧化铪薄膜的电阻开关的一般方法在过去十年左右一直非常活跃,但每个器件具有连续可编程状态范围的氧化铪的开发仍处于非常早期的阶段,并且演示大多处于单个器件层面,所提供的数据有限。另一方面,有一些全网络实现的演示是积极的。我们总结了该领域的总体状况,指出未解决的问题,并为未来工作提供建议。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/816b/10501996/e0f42ee0f6ae/40580_2023_392_Fig1_HTML.jpg

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