journal article·2015-01-01Open access

First principle simulations on the effects of oxygen vacancy in HfO2-based RRAM

Yuehua Dai, Yuanyang Zhao, Jiayu Wang, Jianbin Xu, Fei Yang

AIP Advances· Volume 5· Issue 1

Abstract

HfO2-based resistive random access memory (RRAM) takes advantage of oxygen vacancy (V o) defects in its principle of operation. Since the change in resistivity of the material is controlled by the level of oxygen deficiency in the material, it is significantly important to study the performance of oxygen vacancies in formation of conductive filament. Excluding effects of the applied voltage, the Vienna ab initio simulation package (VASP) is used to investigate the orientation and concentration mechanism of the oxygen vacancies based on the first principle. The optimal value of crystal orientation [010] is identified by means of the calculated isosurface plots of partial charge density, formation energy, highest isosurface value, migration barrier, and energy band of oxygen vacancy in ten established orientation systems. It will effectively influence the SET voltage, forming voltage, and the ON/OFF ratio of the device. Based on the results of orientation dependence, different concentration models are established along crystal orientation [010]. The performance of proposed concentration models is evaluated and analyzed in this paper. The film is weakly conductive for the samples deposited in a mixture with less than 4.167at.% of V o contents, and the resistive switching (RS) phenomenon cannot be observed in this case. The RS behavior improves with an increase in the V o contents from 4.167at.% to 6.25at.%; nonetheless, it is found difficult to switch to a stable state. However, a higher V o concentration shows a more favorable uniformity and stability for HfO2-based RRAM.
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Research context

OpenAlex topics: Advanced Memory and Neural Computing · Ferroelectric and Negative Capacitance Devices · Electronic and Structural Properties of Oxides

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

Affiliations

Institute of Electronic and Information Project, Anhui University , Hefei, Anhui 230601, China

Funding

National Natural Science Foundation of China

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