journal article·2023-08Open access

Hybrid Tuning of Sub‐Filaments to Improve Analog Switching Performance in Memristive Devices

Pan Hu, Sushmit Hossain, Zerui Liu, Deming Meng, Yunxiang Wang, Tse‐Hsien Ou, Hao Yang, Buyun Chen, Zhi Cai, Yudi Shi, Mark Barnell, Qing Wu, Stephen B. Cronin, Wei Wu

Advanced Materials Technologies· Volume 8· Issue 15

Abstract

Abstract Memristive devices are promising candidates for analog computing applications such as neuromorphic computation. Larger dynamic ranges and more sufficient multilevel states can enable the significant development of memristor‐based utilizations. Herein, a method to improve the analog switching performance of memristors through a hybrid tuning (coarse and fine tuning) of two sub‐filaments is demonstrated. The creation of sub‐filaments inside the dielectric switching layer is realized by deploying Pt metal islands in the switching layer. Given the different material stack configurations of the two sub‐filaments, they exhibit different switching properties to play the roles of coarse and fine tuning respectively in the memristor. Based on the above mechanism, a Pt/Ta/Al2O3/Pt island/Al2O3‐x/TiOy/Al2O3‐x/Pt memristor is proposed and fabricated. Through the hybrid tuning of two sub‐filaments, a combined dynamic range of 600 Ω to 50 kΩ is achieved. Compared to the reference Pt/Ta/Al2O3/Pt memristors (dynamic range: 600 Ω to 8 kΩ), both dynamic range and multilevel resistance states are increased significantly. Meanwhile, the energy efficiency is improved because the resistance of tunable states can be set to larger values. Furthermore, this mechanism can be incorporated into various existing memristors to improve their dynamic range and multilevel states, which extensively enriches the applications of memristors.
Source: crossref · Quality: baseline

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 CA 90089 USA

Mork Family Department of Chemical Engineering & Materials Science University of Southern California Los Angeles CA 90089 USA

Air Force Research Laboratory Information Directorate Rome NY 13441 USA

Funding

Intelligence Advanced Research Projects Activity · Rome Laboratory

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