journal article·2020-06-23

Memristive Device Characteristics Engineering by Controlling the Crystallinity of Switching Layer Materials

Hao Yang, Buyun Chen, Boxiang Song, Deming Meng, Subodh Tiwari, Aravind Krishnamoorthy, Xiaodong Yan, Zerui Liu, Yunxiang Wang, Pan Hu, Tse-Hsien Ou, Paulo Branicio, Rajiv Kalia, Aiichiro Nakano, Priya Vashishta, Fanxin Liu, Han Wang, Wei Wu

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Abstract

Memristive devices (i.e., memristors) can be highly beneficial in many emerging applications that may play important roles in the future generations of electronic systems, such as bio-inspired neuromorphic computing, high density nonvolatile memory, and field programmable gate arrays. Therefore, the memristor characteristics (such as operation voltage, on/off ratio, and the number of conductance states) must be engineered carefully for different applications. Here, we demonstrate a method to modify the memristor characteristics specifically by controlling the crystallinity of the switching layer material. Through setting the temperature of atomic layer deposition, the crystallinity of deposited Al2O3 can be controlled. Using different crystalline Al2O3 as the memristor switching layer, the characteristics of the corresponding Pt/Al2O3/Ta/Pt cross-point memristors can be modified precisely. The high I-V linearity, high on/off ratio (around 108), low pulse operation voltage (2.5 V), and multilevel conductance states (314 states) of the Pt/Al2O3/Ta/Pt cross-point memristor are demonstrated. More importantly, the mechanism behind this phenomenon is studied. This work deepens our understanding of the working mechanism of memristors and paves the way for using memristors in a broad spectrum of applications.
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Research context

OpenAlex topics: Advanced Memory and Neural Computing · Ferroelectric and Negative Capacitance Devices · Neuroscience and Neural Engineering

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

Affiliations

Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States

Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California 90089, United States

Collaborative Innovation Center for Information Technology in Biological and Medical Physics, and College of Science, Zhejiang University of Technology, Hangzhou, Zhejiang 310023, China

Funding

Air Force Research Laboratory · Basic Energy Sciences

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