journal article·2024-04Open access

Reliability Improvement and Effective Switching Layer Model of Thin‐Film MoS2 Memristors

Yifu Huang, Yuqian Gu, Sivasakthya Mohan, Andrei Dolocan, Nicholas D. Ignacio, Shanmukh Kutagulla, Kevin Matthews, Alejandra Londoño‐Calderon, Yao‐Feng Chang, Ying‐Chen Chen, Jamie H. Warner, Michael T. Pettes, Jack C. Lee, Deji Akinwande

Advanced Functional Materials· Volume 34· Issue 15

Abstract

Abstract 2D memristors have demonstrated attractive resistive switching characteristics recently but also suffer from the reliability issue, which limits practical applications. Previous efforts on 2D memristors have primarily focused on exploring new material systems, while damage from the metallization step remains a practical concern for the reliability of 2D memristors. Here, the impact of metallization conditions and the thickness of MoS2 films on the reliability and other device metrics of MoS2‐based memristors is carefully studied. The statistical electrical measurements show that the reliability can be improved to 92% for yield and improved by ≈16× for average DC cycling endurance in the devices by reducing the top electrode (TE) deposition rate and increasing the thickness of MoS2 films. Intriguing convergence of switching voltages and resistance ratio is revealed by the statistical analysis of experimental switching cycles. An “effective switching layer” model compatible with both monolayer and few‐layer MoS2, is proposed to understand the reliability improvement related to the optimization of fabrication configuration and the convergence of switching metrics. The Monte Carlo simulations help illustrate the underlying physics of endurance failure associated with cluster formation and provide additional insight into endurance improvement with device fabrication optimization.
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Research context

OpenAlex topics: Advanced Memory and Neural Computing · 2D Materials and Applications · MXene and MAX Phase Materials

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

Affiliations

Microelectronics Research Center The University of Texas at Austin Austin TX 78758 USA

Material Science and Engineering Graduate Program Texas Materials Institute The University of Texas at Austin Austin TX 78712 USA

Center for Integrated Nanotechnologies Materials Physics and Applications Division Los Alamos National Laboratory Los Alamos NM 87545 USA

Intel Corporation Hillsboro Oregon 97124 USA

Department of Electrical and Computer Engineering Northern Arizona University Flagstaff Arizona 86011 USA

Walker Department of Mechanical Engineering The University of Texas at Austin Austin TX 78712 USA

Funding

National Science Foundation · U.S. Department of Energy

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