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Stress-induced Artificial neuron spiking in Diffusive memristors

Debi Pattnaik, Yash Sharma, Sergey Saveliev, Pavel Borisov, Amir Akther, Alexander Balanov, Pedro Ferreira

Abstract

Diffusive memristors owing to their ability to produce current spiking when a constant or slowly changing voltage is applied are competitive candidates for the development of artificial electronic neurons. These artificial neurons can be integrated into various prospective autonomous and robotic systems as sensors, e.g. ones implementing object grasping and classification. We report here Ag nanoparticle-based diffusive memristor prepared on a flexible polyethylene terephthalate (PET) substrate in which the electric spiking behaviour was induced by the electric voltage under an additional stimulus of external mechanical impact. By changing the magnitude and frequency of the mechanical impact, we are able to manipulate the spiking response of our artificial neuron. This functionality to control the spiking characterstics paves a pathway for the development of touch-perception sensors that can convert local pressure into electrical spikes for further processing in neural networks. We have proposed a mathematical model which captures the operation principle of the fabricated memristive sensors and qualitatively describes the measured spiking behaviour.

Stress-induced Artificial neuron spiking in Diffusive memristors

Abstract

Diffusive memristors owing to their ability to produce current spiking when a constant or slowly changing voltage is applied are competitive candidates for the development of artificial electronic neurons. These artificial neurons can be integrated into various prospective autonomous and robotic systems as sensors, e.g. ones implementing object grasping and classification. We report here Ag nanoparticle-based diffusive memristor prepared on a flexible polyethylene terephthalate (PET) substrate in which the electric spiking behaviour was induced by the electric voltage under an additional stimulus of external mechanical impact. By changing the magnitude and frequency of the mechanical impact, we are able to manipulate the spiking response of our artificial neuron. This functionality to control the spiking characterstics paves a pathway for the development of touch-perception sensors that can convert local pressure into electrical spikes for further processing in neural networks. We have proposed a mathematical model which captures the operation principle of the fabricated memristive sensors and qualitatively describes the measured spiking behaviour.
Paper Structure (11 sections, 3 equations, 7 figures)

This paper contains 11 sections, 3 equations, 7 figures.

Figures (7)

  • Figure 1: Fabricated diffusive memristor and IV characterization.(a) A side view schematic of a diffusive memristor device, PET/Pt(30 nm)/Ag:SiO$_x$(100 nm)/Ag (5 nm),with an attached voltage source. (b) Typical I-V characteristics of a fabricated memristor measured in both positive (blue line) and negative (red line) voltage cycles. The average of the measured I-V is shown in black. The arrows indicate the direction of voltage change.
  • Figure 2: Surface characterization of the diffusive memristor.(a) XPS survey of the fabricated diffusive memristor, (b-d) High resolution XPS spectra showing O 1s, Si 2p, and Ag 3d with the highlighted areas showing the XPS fits.
  • Figure 3: Artificial memristive neuron.(a) Electrical circuit of an artificial neuron with a diffusive memristor; (b) Measured memristor voltage spikes (red line) vs time for an external input voltage of 0.6 V (black line)
  • Figure 4: Stress-induced spiking.(a) Experimental setup showing the pneumatically controlled impact station. The memristor is placed on the platform with a white rubber cover. The stressor is periodically pressed onto our memristor from the top. (b) Circuit diagram for the stress-induced spiking measurement, (c) Measured voltage spikes for impact pressure 0.15 MPa (black) and 0.35 MPa (red) with a 0.2 s time delay between successive impacts. The memrsitor spikes (left black) from HRS to LRS as a response to the impact which eventually stops (right black) as it is stuck in a permanent LRS. The spiking action is retrieved by gradually increasing the impact (left and right red) from 0.15 MPa to 0.35 MPa in steps of 0.05 MPa.(d) I-V characteristics of the device measured post several impacts in both positive (blue line) and negative (red line) voltages showing asymmetry in V$_t$. The curved arrows indicate the direction of the change in device resistance. (e) Measured memristor voltage spikes (red line) vs time for an external input voltage of 1.9 V (black line) for the device after approximately 4200 impacts
  • Figure 5: Spiking rate: Average spiking rate (number of spike per second) vs time interval between the impact, at different impact pressures.
  • ...and 2 more figures