Figure 3. Oscillation characteristics based on
VO2 volatile memristor. a) Schematic diagram of the
oscillation neuron. b) Oscillation frequency as a function of load
resistance (R L) with different input voltages (4,
5, 6 V). c) Oscillation frequency as a function of input voltage
(V in) with different load resistors (3.0, 3.5,
4.0, 4.5 kΩ). d) Neuronal response of the oscillation neuron under a
constant bias voltage (5 V, 20 μs) and the influence of varying
resistance (R L). The output frequencies are 0.9,
0.7, 0.55, and 0.35 MHz at R L of 3.0, 3.6, 4.2,
4.8 kΩ, respectively. e) Neuronal response of the oscillation neuron in
series with a fixed resistance (R L=4 kΩ) and the
influence of varying input voltage (V in). The
output frequencies are 0.45, 0.65, 0.8, and 0.9 MHz atV in of 4.2, 4.8, 5.4, and 6.0 V, respectively.
2.2. Artificial haptic perception
neuron based on VO2volatile memristor
In a biological sensory nervous system, the mechanoreceptors are
responsible for sensory information transduction. When the external
stimuli exceed the mechanical threshold of the
mechanoreceptors, sensory information
is being coded through an action potential at a certain
frequency.[38, 39] To mimic the biological
activity from mechanoreceptors, the spiking response due to a haptic
event, we integrated the piezoresistive sensor with a
VO2 memristor to emulate the haptic perception as an
artificia mechanoreceptor. Given the dependence of the output frequency
of VO2 oscillation neuron on the load resistor (Figure
2b,d), the haptic perception function can be achieved by replacing the
fixed R L with a piezoresistive sensor. Figure 4a
shows a schematic diagram of the artificial haptic perception neuron
using VO2 volatile memristor and a commercially
available piezoresistive sensor
(the
entire experimental setup is shown in Figure S7, Supporting
information). Figure 4b shows the I–V curves of the
piezoresistive sensor under different pressures, showing different
resistance state in response to pressure/weight inputs (from 20 to 700
g). Moreover, Figure 4c summarizes the resistance response of the
piezoresistive sensor under different pressures/weights. The stability
and thermal characteristics of the piezoresistive sensor are further
shown in Figures S8 and S9, Supporting Information. It can be clearly
seen that the resistance gradually decreases as the pressure increases.
This characteristic can be generalized by a power function:
(3)
where α = 95570, β = -0.64 are extracted by fitting the curve presented
in Figure 4c. The range of resistance change under pressure is between
~1 kΩ and ~20 kΩ, which meets the series
resistance required by the VO2 oscillation neuron. This
piezoresistive sensor can thus be combined with the VO2oscillation neuron to emulate artificial haptic perception, where the
sensor is used as a receiver of pressure signals and the resultant
sensory signal is converted into spike trains by the VO2neuron.
Indeed, when different pressures/weights (100, 150, 200, 250 g) are
applied to the sensor with a constant bias voltage (5 V, 20 μs), the
oscillation neuron exhibits different output spike frequencies (0.45,
0.55, 0.75, 0.95 MHz), as shown in Figure 4d. The converted spike
frequency increases as the pressure increases. In this way, the
artificial haptic sensory neuron can directly respond to pressure
signals and encode them into spikes, and the output spikes can then
transmit information to spiking neural networks for further processing.
Haptic perception allows human to recognize objects, discriminate
texture, and react appropriately in a social
exchange.[2, 40] This essentially requires
integration of multiple spatial correlated sensory stimuli. In order to
achieve this, we combine two sensors in parallel as a proof of concept,
and they are further connected in series with a VO2memristor (the circuit structure is shown in Figure S10, Supporting
Information). This is utilized to
recognize Braille characters in
the present study. As shown in Figure 4e, the black circles represent
convex patterns in the
Braille
characters, while the white circles indicate no convex, which correspond
to the cases of sensing pressure and no pressure when being touched,
respectively. These scenarios were emulated in experiment by applying
100 g for convex patterns and 0 g otherwise. The results in Figure 4e
show that when only one of the two sensors are triggered, the output
oscillation frequency of VO2 neuron is
~0.4 MHz. However, when the two sensors are triggered at
the same time, the output oscillation frequency will be higher
(~1.1 MHz). The output frequency is zero when neither of
the sensors is triggered (detailed information is shown in Supplementary
Video 1-3). Therefore, the Braille characters can be read out from the
different patterns of output frequencies produced by the
VO2 neurons. It should be pointed out that some Braille
characters may not be fully distinguished based on the horizontal
inputs, and in this case an additional process can be introduced to
apply vertical inputs onto the device so as to further distinguish them.