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Resistive switching materials for information processing

delete2020-01-13
delete798
PRE
AI
Z
Zhongrui Wang
H
Huaqiang Wu
G
Geoffrey W. Burr
C
Cheol Seong Hwang
K
Kang L. Wang
Q
Qiangfei Xia
J
J. Joshua Yang *
DOI:10.1038/s41578-019-0159-3delete
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Abstract

Abstract

En 中文
Resistive switching materials enable novel, in-memory information processing, which may resolve the von Neumann bottleneck. This Review focuses on how the switching mechanisms and the resultant electrical properties lead to various computing applications. The rapid increase in information in the big-data era calls for changes to information-processing paradigms, which, in turn, demand new circuit-building blocks to overcome the decreasing cost-effectiveness of transistor scaling and the intrinsic inefficiency of using transistors in non-von Neumann computing architectures. Accordingly, resistive switching materials (RSMs) based on different physical principles have emerged for memories that could enable energy-efficient and area-efficient in-memory computing. In this Review, we survey the four physical mechanisms that lead to such resistive switching: redox reactions, phase transitions, spin-polarized tunnelling and ferroelectric polarization. We discuss how these mechanisms equip RSMs with desirable properties for representation capability, switching speed and energy, reliability and device density. These properties are the key enablers of processing-in-memory platforms, with applications ranging from neuromorphic computing and general-purpose memcomputing to cybersecurity. Finally, we examine the device requirements for such systems based on RSMs and provide suggestions to address challenges in materials engineering, device optimization, system integration and algorithm design.
Keywords:
PHASE-CHANGE MATERIALS
RANDOM-ACCESS-MEMORY
SPIN-ORBIT TORQUE
REAL-TIME OBSERVATION
DOMAIN-WALLS
ROOM-TEMPERATURE
IN-MEMORY
PERPENDICULAR MAGNETIZATION
LOGIC OPERATIONS
CROSSBAR ARRAY
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Nature Reviews Materials cover
Nature Reviews Materials
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tsinghua university
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university of massachusetts system
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University of Massachusetts Amherst
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international business machines (ibm)
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seoul national university (snu)
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