An RRAM with a 2D material embedded double switching layer for neuromorphic computing

Po An Chen, Rui Jing Ge, Jia Wei Lee, Chun Hsiang Hsu, Wei-Chou Hsu, Deji Akinwande, Meng-Hsueh Chiang

研究成果: Conference contribution

摘要

Resistive random-access memory (RRAM) has shown great potential for neuromorphic engineering, due to its ability of emulating neural network and simple structure. To mimic the brain-learning behavior, two types of neural actions, short-term plasticity (STP) and long-term potentiation (LTP), should be imitated perfectly. In this work, we propose a unique RRAM cell with a double switching layer, in which a 2D material is embedded as a separation layer. Within a proper voltage range of stress, the mobile oxygen ions are blocked by the single atomic layer, and hence the subsequent relaxation of oxygen ions leads to a volatile switching characteristic. Owing to this volatile characteristic, the proposed device can mimic neural actions, STP and LTP, by a simple pulse train with different repetitions and frequencies without the complicated pulse settings of spike-timing-dependent plasticity (STDP). For various learning algorithms in future brain-inspired applications, different switching materials with different bind energies and relaxation times of oxygen ions can be utilized.

原文English
主出版物標題2018 IEEE 13th Nanotechnology Materials and Devices Conference, NMDC 2018
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781538610169
DOIs
出版狀態Published - 2019 一月 8
事件13th IEEE Nanotechnology Materials and Devices Conference, NMDC 2018 - Portland, United States
持續時間: 2018 十月 142018 十月 17

出版系列

名字2018 IEEE 13th Nanotechnology Materials and Devices Conference, NMDC 2018

Conference

Conference13th IEEE Nanotechnology Materials and Devices Conference, NMDC 2018
國家United States
城市Portland
期間18-10-1418-10-17

指紋

random access memory
oxygen ions
plastic properties
Plasticity
Ions
Oxygen
Data storage equipment
learning
brain
Brain
pulses
spikes
Relaxation time
Learning algorithms
repetition
relaxation time
time measurement
engineering
Neural networks
Electric potential

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials
  • Surfaces, Coatings and Films
  • Instrumentation

引用此文

Chen, P. A., Ge, R. J., Lee, J. W., Hsu, C. H., Hsu, W-C., Akinwande, D., & Chiang, M-H. (2019). An RRAM with a 2D material embedded double switching layer for neuromorphic computing. 於 2018 IEEE 13th Nanotechnology Materials and Devices Conference, NMDC 2018 [8605915] (2018 IEEE 13th Nanotechnology Materials and Devices Conference, NMDC 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NMDC.2018.8605915
Chen, Po An ; Ge, Rui Jing ; Lee, Jia Wei ; Hsu, Chun Hsiang ; Hsu, Wei-Chou ; Akinwande, Deji ; Chiang, Meng-Hsueh. / An RRAM with a 2D material embedded double switching layer for neuromorphic computing. 2018 IEEE 13th Nanotechnology Materials and Devices Conference, NMDC 2018. Institute of Electrical and Electronics Engineers Inc., 2019. (2018 IEEE 13th Nanotechnology Materials and Devices Conference, NMDC 2018).
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abstract = "Resistive random-access memory (RRAM) has shown great potential for neuromorphic engineering, due to its ability of emulating neural network and simple structure. To mimic the brain-learning behavior, two types of neural actions, short-term plasticity (STP) and long-term potentiation (LTP), should be imitated perfectly. In this work, we propose a unique RRAM cell with a double switching layer, in which a 2D material is embedded as a separation layer. Within a proper voltage range of stress, the mobile oxygen ions are blocked by the single atomic layer, and hence the subsequent relaxation of oxygen ions leads to a volatile switching characteristic. Owing to this volatile characteristic, the proposed device can mimic neural actions, STP and LTP, by a simple pulse train with different repetitions and frequencies without the complicated pulse settings of spike-timing-dependent plasticity (STDP). For various learning algorithms in future brain-inspired applications, different switching materials with different bind energies and relaxation times of oxygen ions can be utilized.",
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Chen, PA, Ge, RJ, Lee, JW, Hsu, CH, Hsu, W-C, Akinwande, D & Chiang, M-H 2019, An RRAM with a 2D material embedded double switching layer for neuromorphic computing. 於 2018 IEEE 13th Nanotechnology Materials and Devices Conference, NMDC 2018., 8605915, 2018 IEEE 13th Nanotechnology Materials and Devices Conference, NMDC 2018, Institute of Electrical and Electronics Engineers Inc., 13th IEEE Nanotechnology Materials and Devices Conference, NMDC 2018, Portland, United States, 18-10-14. https://doi.org/10.1109/NMDC.2018.8605915

An RRAM with a 2D material embedded double switching layer for neuromorphic computing. / Chen, Po An; Ge, Rui Jing; Lee, Jia Wei; Hsu, Chun Hsiang; Hsu, Wei-Chou; Akinwande, Deji; Chiang, Meng-Hsueh.

2018 IEEE 13th Nanotechnology Materials and Devices Conference, NMDC 2018. Institute of Electrical and Electronics Engineers Inc., 2019. 8605915 (2018 IEEE 13th Nanotechnology Materials and Devices Conference, NMDC 2018).

研究成果: Conference contribution

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AU - Akinwande, Deji

AU - Chiang, Meng-Hsueh

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AB - Resistive random-access memory (RRAM) has shown great potential for neuromorphic engineering, due to its ability of emulating neural network and simple structure. To mimic the brain-learning behavior, two types of neural actions, short-term plasticity (STP) and long-term potentiation (LTP), should be imitated perfectly. In this work, we propose a unique RRAM cell with a double switching layer, in which a 2D material is embedded as a separation layer. Within a proper voltage range of stress, the mobile oxygen ions are blocked by the single atomic layer, and hence the subsequent relaxation of oxygen ions leads to a volatile switching characteristic. Owing to this volatile characteristic, the proposed device can mimic neural actions, STP and LTP, by a simple pulse train with different repetitions and frequencies without the complicated pulse settings of spike-timing-dependent plasticity (STDP). For various learning algorithms in future brain-inspired applications, different switching materials with different bind energies and relaxation times of oxygen ions can be utilized.

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Chen PA, Ge RJ, Lee JW, Hsu CH, Hsu W-C, Akinwande D 等. An RRAM with a 2D material embedded double switching layer for neuromorphic computing. 於 2018 IEEE 13th Nanotechnology Materials and Devices Conference, NMDC 2018. Institute of Electrical and Electronics Engineers Inc. 2019. 8605915. (2018 IEEE 13th Nanotechnology Materials and Devices Conference, NMDC 2018). https://doi.org/10.1109/NMDC.2018.8605915