• Journal of Semiconductors
  • Vol. 42, Issue 6, 064101 (2021)
Yujia Li1、2, Jianshi Tang2、3, Bin Gao2、3, Xinyi Li2, Yue Xi2, Wanrong Zhang1, He Qian2、3, and Huaqiang Wu2、3
Author Affiliations
  • 1Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
  • 2Institute of Microelectronics, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
  • 3Beijing Innovation Center for Future Chips (ICFC), Tsinghua University, Beijing 100084, China
  • show less
    DOI: 10.1088/1674-4926/42/6/064101 Cite this Article
    Yujia Li, Jianshi Tang, Bin Gao, Xinyi Li, Yue Xi, Wanrong Zhang, He Qian, Huaqiang Wu. Oscillation neuron based on a low-variability threshold switching device for high-performance neuromorphic computing[J]. Journal of Semiconductors, 2021, 42(6): 064101 Copy Citation Text show less
    (Color online) (a) Schematic diagram of a typical artificial neural network. (b) Circuit implementation of the oscillation neuron with a TS device.
    Fig. 1. (Color online) (a) Schematic diagram of a typical artificial neural network. (b) Circuit implementation of the oscillation neuron with a TS device.
    (Color online) (a) TEM image of the Ag NDs TS device. (b) Schematic illustration of the threshold switching process in the device. (c) Typical current–voltage (I–V) curves for the Ag NDs TS device. (d) Cumulative probability of Vth and Vhold distributions for the Ag NDs TS device. (e) Endurance test of the Ag NDs TS device with over 108 cycles. (f) Measured oscillation waveform of the oscillation neuron.
    Fig. 2. (Color online) (a) TEM image of the Ag NDs TS device. (b) Schematic illustration of the threshold switching process in the device. (c) Typical current–voltage (I–V) curves for the Ag NDs TS device. (d) Cumulative probability of Vth and Vhold distributions for the Ag NDs TS device. (e) Endurance test of the Ag NDs TS device with over 108 cycles. (f) Measured oscillation waveform of the oscillation neuron.
    (Color online) (a) Oscillation waveforms of the oscillation neuron with different Vin when RL = 50 kΩ, CL = 750 pF. (b) The oscillation frequency as a function of Vin. (c) Oscillation waveforms of the oscillation neuron with different RL when Vin = 1.2 V, CL = 750 pF. (d) The oscillation frequency as a function of RL.
    Fig. 3. (Color online) (a) Oscillation waveforms of the oscillation neuron with different Vin when RL = 50 kΩ, CL = 750 pF. (b) The oscillation frequency as a function of Vin. (c) Oscillation waveforms of the oscillation neuron with different RL when Vin = 1.2 V, CL = 750 pF. (d) The oscillation frequency as a function of RL.
    (Color online) The oscillation frequency as a function of the RRAM crossbar array size under different on/off ratios of the TS device.
    Fig. 4. (Color online) The oscillation frequency as a function of the RRAM crossbar array size under different on/off ratios of the TS device.
    (Color online) (a) The oscillation frequency distribution under different CV. (b) The oscillation frequency distribution of different RL when CV = 7% (top panel) and CV = 30% (bottom panel).
    Fig. 5. (Color online) (a) The oscillation frequency distribution under different CV. (b) The oscillation frequency distribution of different RL when CV = 7% (top panel) and CV = 30% (bottom panel).
    (Color online) (a) The structure of MLP neural network. (b) Simulation results of the MNIST recognition accuracy loss as a function of the variability of the TS device.
    Fig. 6. (Color online) (a) The structure of MLP neural network. (b) Simulation results of the MNIST recognition accuracy loss as a function of the variability of the TS device.
    Yujia Li, Jianshi Tang, Bin Gao, Xinyi Li, Yue Xi, Wanrong Zhang, He Qian, Huaqiang Wu. Oscillation neuron based on a low-variability threshold switching device for high-performance neuromorphic computing[J]. Journal of Semiconductors, 2021, 42(6): 064101
    Download Citation