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从铁电材料优化到神经形态器件

From Ferroelectric Material Optimization to Neuromorphic Devices.

作者信息

Mikolajick Thomas, Park Min Hyuk, Begon-Lours Laura, Slesazeck Stefan

机构信息

NaMLab gGmbH, Noethnitzer Strasse 64 a, 01187, Dresden, Germany.

Institute of Semiconductors and Microsystems, TU Dresden, 01069, Dresden, Germany.

出版信息

Adv Mater. 2023 Sep;35(37):e2206042. doi: 10.1002/adma.202206042. Epub 2023 Feb 3.

DOI:10.1002/adma.202206042
PMID:36017895
Abstract

Due to the voltage driven switching at low voltages combined with nonvolatility of the achieved polarization state, ferroelectric materials have a unique potential for low power nonvolatile electronic devices. The competitivity of such devices is hindered by compatibility issues of well-known ferroelectrics with established semiconductor technology. The discovery of ferroelectricity in hafnium oxide changed this situation. The natural application of nonvolatile devices is as a memory cell. Nonvolatile memory devices also built the basis for other applications like in-memory or neuromorphic computing. Three different basic ferroelectric devices can be constructed: ferroelectric capacitors, ferroelectric field effect transistors and ferroelectric tunneling junctions. In this article first the material science of the ferroelectricity in hafnium oxide will be summarized with a special focus on tailoring the switching characteristics towards different applications.The current status of nonvolatile ferroelectric memories then lays the ground for looking into applications like in-memory computing. Finally, a special focus will be given to showcase how the basic building blocks of spiking neural networks, the neuron and the synapse, can be realized and how they can be combined to realize neuromorphic computing systems. A summary, comparison with other technologies like resistive switching devices and an outlook completes the paper.

摘要

由于在低电压下由电压驱动的开关特性以及所实现的极化状态的非易失性,铁电材料在低功耗非易失性电子器件方面具有独特的潜力。此类器件的竞争力受到知名铁电体与现有半导体技术兼容性问题的阻碍。氧化铪中铁电性的发现改变了这种局面。非易失性器件的自然应用是作为存储单元。非易失性存储器件也为诸如内存内或神经形态计算等其他应用奠定了基础。可以构建三种不同的基本铁电器件:铁电电容器、铁电场效应晶体管和铁电隧道结。在本文中,首先将总结氧化铪中铁电性的材料科学,特别关注针对不同应用调整开关特性。非易失性铁电存储器的当前状态为研究诸如内存内计算等应用奠定了基础。最后,将特别关注展示尖峰神经网络的基本构建块神经元和突触如何能够实现,以及它们如何能够组合以实现神经形态计算系统。总结、与电阻式开关器件等其他技术的比较以及展望完善了本文。

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