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Multilevel switching memristor by compliance current adjustment for off-chip training of neuromorphic system

Author

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  • Kim, Tae-Hyeon
  • Kim, Sungjoon
  • Hong, Kyungho
  • Park, Jinwoo
  • Hwang, Yeongjin
  • Park, Byung-Gook
  • Kim, Hyungjin

Abstract

Multilevel operation is one of the most essential properties for synaptic devices to realize hardware artificial neural networks. Compliance current (Icc) adjustment is a multilevel programming method that can be utilized for a large-scale one-transistor and one-resistor (1T1R) array. It protects the devices from permanent breakdown by regulating abrupt switching. However, according to the reported literature so far, the number of conductance states in the Icc control method is insufficient to implement off-chip-trained neuromorphic systems. Therefore, we experimentally explore the feasibility of a larger number of conductance states using the Icc control method. We fabricated an Al2O3/TiOx-based resistive switching memory array, observed the conductance change while increasing Icc during set operations, and 64-level conductance states were statistically demonstrated. Furthermore, we verified that the 64-level states showed recognition performance close to that of a software-based neural network through off-chip learning of the convolutional neural network (CNN) structure. The fabricated synaptic device array with the Icc-control programming method is expected to contribute to the development of hardware neural network by reducing the information loss in the transfer process.

Suggested Citation

  • Kim, Tae-Hyeon & Kim, Sungjoon & Hong, Kyungho & Park, Jinwoo & Hwang, Yeongjin & Park, Byung-Gook & Kim, Hyungjin, 2021. "Multilevel switching memristor by compliance current adjustment for off-chip training of neuromorphic system," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    • Handle: RePEc:eee:chsofr:v:153:y:2021:i:p2:s0960077921009413
    DOI: 10.1016/j.chaos.2021.111587
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    1. Lee, Geun Ho & Kim, Tae-Hyeon & Song, Min Suk & Park, Jinwoo & Kim, Sungjoon & Hong, Kyungho & Kim, Yoon & Park, Byung-Gook & Kim, Hyungjin, 2022. "Effect of weight overlap region on neuromorphic system with memristive synaptic devices," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    2. Choi, Woo Sik & Jang, Jun Tae & Kim, Donguk & Yang, Tae Jun & Kim, Changwook & Kim, Hyungjin & Kim, Dae Hwan, 2022. "Influence of Al2O3 layer on InGaZnO memristor crossbar array for neuromorphic applications," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    3. Lee, Geun Ho & Kim, Tae-Hyeon & Youn, Sangwook & Park, Jinwoo & Kim, Sungjoon & Kim, Hyungjin, 2023. "Low-fluctuation nonlinear model using incremental step pulse programming with memristive devices," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).
    4. Choi, Woo Sik & Kim, Donguk & Yang, Tae Jun & Chae, Inseok & Kim, Changwook & Kim, Hyungjin & Kim, Dae Hwan, 2022. "Electrode-dependent electrical switching characteristics of InGaZnO memristor," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    5. Peng, Yuexi & Liu, Jun & He, Shaobo & Sun, Kehui, 2023. "Discrete fracmemristor-based chaotic map by Grunwald–Letnikov difference and its circuit implementation," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).

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