Resistive switching materials for information processing
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 c...
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| Published in | Nature reviews. Materials Vol. 5; no. 3; pp. 173 - 195 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
01.03.2020
Nature Publishing Group |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2058-8437 2058-8437 |
| DOI | 10.1038/s41578-019-0159-3 |
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| Abstract | 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.
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. |
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| AbstractList | 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.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. 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. |
| Author | Xia, Qiangfei Hwang, Cheol Seong Burr, Geoffrey W. Wang, Zhongrui Wang, Kang L. Wu, Huaqiang Yang, J. Joshua |
| Author_xml | – sequence: 1 givenname: Zhongrui surname: Wang fullname: Wang, Zhongrui organization: Department of Electrical and Computer Engineering, University of Massachusetts – sequence: 2 givenname: Huaqiang orcidid: 0000-0001-8359-7997 surname: Wu fullname: Wu, Huaqiang organization: Institute of Microelectronics, Tsinghua University – sequence: 3 givenname: Geoffrey W. orcidid: 0000-0001-5717-2549 surname: Burr fullname: Burr, Geoffrey W. organization: IBM Research–Almaden – sequence: 4 givenname: Cheol Seong orcidid: 0000-0002-6254-9758 surname: Hwang fullname: Hwang, Cheol Seong organization: Department of Materials Science and Engineering, and Inter-University Semiconductor Research Center, Seoul National University – sequence: 5 givenname: Kang L. surname: Wang fullname: Wang, Kang L. organization: Department of Electrical and Computer Engineering, University of California – sequence: 6 givenname: Qiangfei orcidid: 0000-0003-1436-8423 surname: Xia fullname: Xia, Qiangfei email: qxia@umass.edu organization: Department of Electrical and Computer Engineering, University of Massachusetts – sequence: 7 givenname: J. Joshua orcidid: 0000-0003-0671-6010 surname: Yang fullname: Yang, J. Joshua email: jjyang@umass.edu organization: Department of Electrical and Computer Engineering, University of Massachusetts |
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