Integration of CeO<sub>2</sub>-Based Memristor with Vertically Aligned Nanocomposite Thin Film: Enabling Selective Conductive Filament Formation for High-Performance Electronic Synapses

The CeO<sub>2</sub>-based memristor has attracted significant attention due to its intrinsic resistive switching (RS) properties, large on/off ratio, and great plasticity, making it a promising candidate for artificial synapses. However, significant challenges such as high power consumption and poor device reliability hinder its broad application in neuromorphic microchips. To tackle these issues, in this work, we design a novel bilayer (BL) memristor by integrating a CeO<sub>2</sub>-based memristor with a Co-CeO<sub>2</sub> vertically aligned nanocomposite (VAN) layer and compare it with the single layer (SL) memristor. Preliminary electrical testing reveals that the BL memristor offers a reduced set/reset voltage (∼67% lower), a higher on/off ratio (∼5 × 10<sup>2</sup>), enhanced device reliability, and improved device-to-device variation compared to the SL memristor. Insight from COMSOL simulation, coupled with microstructural analysis, provides a comprehensive elucidation on how the VAN layer facilitates the selective conductive filament (CF) formation. Subsequently, the plasticity of the BL memristor is evaluated through long-term potentiation/depression (LTP/LTD), paired-pulse facilitation (PPF), and spike-time-dependent plasticity (STDP). The spiking neural network (SNN) built upon the BL memristor achieves remarkable accuracy (∼94%) after only 12 iterations, underscoring its potential for high-performance neural networks.

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Publication Details

Journal
ACS Applied Materials & Interfaces
Published
2024-11-15
DOI
https://doi.org/10.1021/acsami.4c10687
Citations
15
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
3.13

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article

Integration of CeO2-Based Memristor with Vertically Aligned Nanocomposite Thin Film: Enabling Selective Conductive Filament Formation for High-Performance Electronic Synapses

Jijie Huang, Shuyao Han, Eric Hollander, Hongyi Dou et al.
15 citations
ACS Applied Materials & Interfaces
Advanced Memory and Neural Computing
3.13
article

Integration of CeO2-Based Memristor with Vertically Aligned Nanocomposite Thin Film: Enabling Selective Conductive Filament Formation for High-Performance Electronic Synapses

Jijie Huang, Shuyao Han, Eric Hollander, Hongyi Dou, Ye Cao, Yizhi Zhang, Zedong Hu, Zhongxia Shang, Jianan Shen, Haiyan Wang, Yifan Zhang, Juanjuan Lu, Laveeza Ahmad
article en
15 citations

Abstract

The CeO<sub>2</sub>-based memristor has attracted significant attention due to its intrinsic resistive switching (RS) properties, large on/off ratio, and great plasticity, making it a promising candidate for artificial synapses. However, significant challenges such as high power consumption and poor device reliability hinder its broad application in neuromorphic microchips. To tackle these issues, in this work, we design a novel bilayer (BL) memristor by integrating a CeO<sub>2</sub>-based memristor with a Co-CeO<sub>2</sub> vertically aligned nanocomposite (VAN) layer and compare it with the single layer (SL) memristor. Preliminary electrical testing reveals that the BL memristor offers a reduced set/reset voltage (∼67% lower), a higher on/off ratio (∼5 × 10<sup>2</sup>), enhanced device reliability, and improved device-to-device variation compared to the SL memristor. Insight from COMSOL simulation, coupled with microstructural analysis, provides a comprehensive elucidation on how the VAN layer facilitates the selective conductive filament (CF) formation. Subsequently, the plasticity of the BL memristor is evaluated through long-term potentiation/depression (LTP/LTD), paired-pulse facilitation (PPF), and spike-time-dependent plasticity (STDP). The spiking neural network (SNN) built upon the BL memristor achieves remarkable accuracy (∼94%) after only 12 iterations, underscoring its potential for high-performance neural networks.

ACS Applied Materials & InterfacesVol. 16(47)
Sun Yat-sen University (CN), The University of Texas at Arlington (US), Purdue University West Lafayette (US)
National Science Foundation, U.S. Department of Energy, Office of Science, Division of Materials Research, Basic Energy Sciences
Openalex Percentile: Top 8%
Advanced Memory and Neural Computing
3.13
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