MRS Meetings and Events


EL05.04.03 2024 MRS Spring Meeting

Au Nanoparticle Floating-Gate Memristor Array for Low-Power Neuromorphic System

When and Where

Apr 23, 2024
5:00pm - 7:00pm

Flex Hall C, Level 2, Summit



Hongwoon Yun1,Woojong Yu1

Sungkyunkwan University1


Hongwoon Yun1,Woojong Yu1

Sungkyunkwan University1
The von Neumann architecture, which physically separates the CPU and memory devices, has been dominant for a long time. However, it has limitation in computational speed due to bottlenecks and waste a large amount of energy. To address the energy and speed issue, we made a neuromorphic system based on au nanoparticle floating gate memristor (AuNp-FGM).<br/>Our memristor, utilizing graphene as floating gate, MoS<sub>2</sub> as channel, have been recognized for its excellent performance [1,2]. Also, it has been recognized as one of the most promising candidates for neuromorphic system [3]. In this study, by forming au nanoparticles between floating gate and tunneling oxide, a two-terminal memristor which operates in the ±3V region is fabricated. Additionally, we made a neuromorphic array, calculated the energy used for learning simulation.<br/>Using the change of the Fermi energy level (E<sub>f</sub>) of graphene, (AuNp-FGM) exhibits memory characteristic. The device exhibits high on/off ratio over than 10<sup>6</sup>, retention more than 9 hours and robust endurance more than 80,000 times. AuNp-FGM also showed low cycle to cycle variability of C<sub>v</sub> = 3.6% (n = 90, C<sub>v</sub> = , = standard deviation, = mean value). Furthermore, it showed excellent linearity, indicating applicability to neuromorphic systems. For 100 level potentiation (+4V, 0.5s), non-linearity factor ranges from 0.1 to 0.6. For 100 level depression (-3V, 0.2s), it ranges from 2.3 to 4.6 (n = 15). A similar trend was shown even when the number of input pulses was changed (50, 100, 200, 300, 400 inputs).<br/>Based on AuNp-FGM, we fabricated a neuromorphic array consisting of 2 neurons and 32 synapses. Three types of data (horizontal, vertical, diagonal) were used in 40 learning simulation. The total energy consumed was 70uJ, which confirmed to be a 97% energy reduction compared to the previous experiment [3].<br/><br/>References<br/>[1] [1] Vu, Q., Shin, Y., Kim, Y. <i>et al.</i> <i>Nat. Commun.</i> <b>7</b>, 12725 (2016).<br/>[2] Q. A. Vu, H. Kim, V. L. Nguyen, U. Y. Won, S. Adhikari, K. Kim, Y. H. Lee, W. J. Yu, <i>Adv. Mater.</i> 2017, 29, 1703363.<br/>[3] Won, U.Y., An Vu, Q., Park, S.B. <i>et al.</i> <i>Nat. Commun.</i> <b>14</b>, 3070 (2023).


2D materials | graphene | van der Waals

Symposium Organizers

Silvija Gradecak, National University of Singapore
Lain-Jong Li, The University of Hong Kong
Iuliana Radu, TSMC Taiwan
John Sudijono, Applied Materials, Inc.

Symposium Support

Applied Materials

Session Chairs

Lain-Jong Li
John Sudijono

In this Session

Chemical Vapor Deposition of One-Dimensional Van der Waals Material Nb2Se9 assisted by Liquid Precursor

Room-Temperature Direct Growth of Transition Metal Dichalcogenide Films via Remote Plasma-Assisted Chemical Vapor Deposition

Au Nanoparticle Floating-Gate Memristor Array for Low-Power Neuromorphic System

Multi-Neuron Connection Using Multi-Terminal Floating-Gate Memristor for Unsupervised Learning

WITHDRAWN (NO REG) 5/20/2024 EL05.04.05 Self-Powered Asymmetric-Geometry 2D Diodes for Sensing Volatile Organic Compounds

Synthesis of Te and Sb Doped Black Phosphorus Single Crystals, Oxidation-Resistance and Room-Temperature Gas Sensing Applications

WITHDRAWN (NO REG) 5/20/2024 EL05.04.07 Novel Technique to Fabricate Thick CNT based Buckypaper for EMI Shielding Applications

Structural and Physical Properties of Two Distinct Two-Dimensional Lead Halides with Intercalated Cu(II): A Comparative Study

Van der Waals Interface Engineering for Enhancemen of Semiconductor Device Performance

Monolayer MoS2 with Controllable and Localized Micro-Scale Domains of Strain enabled by Spatially Varying Nanotopography

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