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神经形态电路与系统中忆阻器的进展与挑战

Progress and Challenges for Memtransistors in Neuromorphic Circuits and Systems.

作者信息

Yan Xiaodong, Qian Justin H, Sangwan Vinod K, Hersam Mark C

机构信息

Department of Materials Science and Engineering, Northwestern University, Evanston, IL, 60208, USA.

Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, 60208, USA.

出版信息

Adv Mater. 2022 Dec;34(48):e2108025. doi: 10.1002/adma.202108025. Epub 2022 Feb 19.

Abstract

Due to the increasing importance of artificial intelligence (AI), significant recent effort has been devoted to the development of neuromorphic circuits that seek to emulate the energy-efficient information processing of the brain. While non-volatile memory (NVM) based on resistive switches, phase-change memory, and magnetic tunnel junctions has shown potential for implementing neural networks, additional multi-terminal device concepts are required for more sophisticated bio-realistic functions. Of particular interest are memtransistors based on low-dimensional nanomaterials, which are capable of electrostatically tuning memory and learning behavior at the device level. Herein, a conceptual overview of the memtransistor is provided in the context of neuromorphic circuits. Recent progress is surveyed for memtransistors and related multi-terminal NVM devices including dual-gated floating-gate memories, dual-gated ferroelectric transistors, and dual-gated van der Waals heterojunctions. The different materials systems and device architectures are classified based on the degree of control and relative tunability of synaptic behavior, with an emphasis on device concepts that harness the reduced dimensionality, weak electrostatic screening, and phase-changes properties of nanomaterials. Finally, strategies for achieving wafer-scale integration of memtransistors and multi-terminal NVM devices are delineated, with specific attention given to the materials challenges for practical neuromorphic circuits.

摘要

由于人工智能(AI)的重要性日益增加,最近人们投入了大量精力来开发神经形态电路,旨在模拟大脑高效节能的信息处理方式。虽然基于电阻开关、相变存储器和磁隧道结的非易失性存储器(NVM)已显示出实现神经网络的潜力,但对于更复杂的生物逼真功能,还需要额外的多终端器件概念。基于低维纳米材料的忆晶体管尤其令人关注,它们能够在器件层面静电调节记忆和学习行为。在此,在神经形态电路的背景下提供忆晶体管的概念概述。综述了忆晶体管及相关多终端NVM器件(包括双栅浮栅存储器、双栅铁电晶体管和双栅范德华异质结)的最新进展。根据突触行为的控制程度和相对可调性对不同的材料系统和器件架构进行分类,重点关注利用纳米材料的低维特性、弱静电屏蔽和相变特性的器件概念。最后,阐述了实现忆晶体管和多终端NVM器件晶圆级集成的策略,并特别关注实际神经形态电路面临的材料挑战。

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