journal article·2022-10-05

Surface-Dominated HfO2 Nanorod-Based Memristor Exhibiting Highly Linear and Symmetrical Conductance Modulation for High-Precision Neuromorphic Computing

Jae Uk Kwon, Young Geun Song, Ji Eun Kim, Suk Yeop Chun, Gu Hyun Kim, Gichang Noh, Joon Young Kwak, Sunghoon Hur, Chong-Yun Kang, Doo Seok Jeong, Soong Ju Oh, Jung Ho Yoon

ACS Applied Materials & Interfaces· Volume 14· Issue 39
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Abstract

The switching characteristics and performance of oxide-based memristors are predominately determined by oxygen- or oxygen-vacancy-mediated redox reactions and the consequent formation of conducting filaments (CFs). Devices using oxide thin films as the switching layer usually require an electroforming process for subsequent switching operations, which induces large device-to-device variations. In addition, the hard-to-control redox reaction during repeated switching causes random fluctuations or degradation of each resistance state, hindering reliable switching operations. In this study, an HfO2 nanorod (NR)-based memristor is proposed for simultaneously achieving highly uniform, electroforming-free, fast, and reliable analogue switching properties. The well-controlled redox reaction due to the easy gas exchange with the environment at the surface of the NRs enhances the generation of oxygen or oxygen vacancies during the switching operation, resulting in electroforming-free and reliable switching behavior. In addition, the one-dimensional surface growth of CFs facilitates highly linear conductance modulation with smaller conductance changes compared with the two-dimensional volume growth in thin-film-based memristors, resulting in a high accuracy of >92% in the Modified National Institute of Standards and Technology pattern-recognition test and desirable spike-timing-dependent plasticity.
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Research context

OpenAlex topics: Advanced Memory and Neural Computing · Ferroelectric and Negative Capacitance Devices · Semiconductor materials and devices

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul 02791, Republic of Korea

Department of Materials Science and Engineering, Korea University, Seoul 02841, Republic of Korea

KU-KIST Graduate School of Converging Science and Technology, Korea University, Seoul 02841, Republic of Korea

Division of Materials Science and Engineering, Hanyang University, Seoul 04763, Republic of Korea

Center for Neuromorphic Engineering, Korea Institute of Science and Technology (KIST), Seoul 02791, Republic of Korea

Funding

National Research Foundation of Korea · Korea Institute of Science and Technology

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