2.4. Full-hardware neuromorphic vision system based on
reconfigurable ion-modulated memtransistors
Since the directional movement of ions in the electrolyte needs to
surmount potential energy barrier, and the additional silicon oxide
layer also causes an inevitable voltage drop, the short-term response of
the device has a nonlinear relationship with the amplitude of external
stimuli. To investigate the nonlinear response, we applied a series of
identical pulses to the device, ranging in amplitudes from 0.2 V to 3.6
V. The corresponding increases in drain current were shown in Figure 5a.
The drain current at the end of the last stimuli pulse and the first
pulse stimuli were summarized in Figure 5b and 5c, respectively. To take
the relation between the drain current responses and stimuli amplitude
into systematic computation, a softplus-like function (
y=aln(1+ebx) ) was adopted to fit the experimental
results.
In artificial vision system, images captured by image sensor are often
distorted by various noises, such as electrical noise, mechanical noise,
channel noise and other noises, during generation and transmission. To
suppress noise, improve image quality, and facilitate higher-level
processing, image denoising is performed using the short-term dynamics
of the ion-modulated memtransistors, as illustrated in the inset of
Figure 5b. During inference, the MVM is often performed by applying a
short pulse on the bit line. We use the channel current change
characteristic after the single-pulse stimulus in the operation of
neuron activation in the inference, which is shown in the inset of
Figure 5c. The schematic of the basic neural network architecture for
the neuromorphic vision system is demonstrated in Figure 5d, mainly
including filtering units for denoising, synapses for MVM and hardware
softplus neurons for nonlinear activation. Then the artificial
neuromorphic hardware systems for visual information processing were
proposed based on the ion-modulated memtransistors, as shown in Figure
5e. After stimulating by the encoded electrical pulses in the filtering
units, the drain currents were transferred into voltage pulses in a
linear mapping relation. Then the converted voltage pulses were fed into
the Computing-in-Memory array to perform MVM. The basic cell consists of
one transistor and one ion-modulated memtransistor (1T1M), in which the
transistor is responsible for selective programming and retention
enhancing. Finally, the cumulative current after the MVM is converted to
voltage pulses and then applied on the gate to utilize the softplus-like
response to achieve the nonlinear activation.
As shown in Figure 6 a, we simulated multilayer perception (MLP,
inset in Figure 6a) for the evaluation of the network-level performance
using the ion-modulated memtransistor for softplus neurons. The
simulation details can be found in the Experimental Section, there was
almost no difference in the testing accuracy between the standard
software softplus neuron and the hardware softplus neuron. Unlike weight
updating, there is supposed to be no accumulation in the device state
for the application of neuron function. To avoid the transition between
short-term memory and long-term memory, it is necessary to impose a
constraint on the amplitude of gate pulses. We define the viable upper
limit amplitude of the gate pulses as cutoff voltage, with minimizing
the cutoff voltage, the accumulative effect can be overcome and
programming energy can be saved. As shown in Figure 6b and 6c, there is
no significant difference in the network performance with cutoff voltage
in the interval between 3.0 V and 3.5 V. However, a noticeable
degradation took place when the cutoff voltage reached 2.9 V. Moreover,
after reducing the cutoff voltage below 2.8 V, there is no
classification ability for the neural network, with the accuracy all
about 10%. After investigating the adequate upper limit of gate pulses,
we set the cutoff voltage as 3 V. To characterize the endurance of the
devices, a train of pulses of 3 V was applied on the device gate. Figure
6d shows no sign of ON/OFF ratio degradation up to 2000 cycles, implying
that there is no need to reset the device to the initial state with the
help of peripheral circuits. The spontaneous decaying characteristic can
ensure repeated activations in the inference. As for the noisy nature of
the diffusion of the random ions, we explore the influence on the neural
network performance under the noise of the hardware softplus neuron. It
can be seen in Figure 6e that the neural network can tolerate the
considerable noise level of the hardware softplus neuron, implying the
robustness for the ion-modulated memtransistor configuring as the neuron
function, the hardware softplus functions with Gaussian noise of
different standard deviation were also compared in Figure S7a,
Supporting Information, and the collection of testing accuracies for the
network concerning the neuron function was demonstrated in Figure S7b,
Supporting Information.
Following the discussions about hardware softplus neuron implementation,
we choose the cutoff voltage of 3 V and noise level of 10% for the
subsequent investigation of the filtering function of ion-modulated
memtransistors. Firstly, we compared the same images in three different
states: 1) Original; 2) With 10% Gaussian noise; 3) After softplus-like
function filtering; and the results are shown in Figure 6f. Compared
with the noisy images, after filtering, background noises could be
suppressed and critical image information got enhanced. Although there
was an overall reduction in the specific pixel value, the shape could
still be distinguished by the enhanced contrast with the background
noises. As shown in Figure 6g, after filtering by the device
nonlinearity, the testing accuracy got a significant increase from
11.66% to 77.96%, and the corresponding confusion matrix in Figure 6h
demonstrated improved classification accuracy by filtering the image
noises. One of the determining factors of the specific filtering
function is the mapping gate voltage range. As illustrated in Figure S8,
Supporting Information, there are some differences among the filtering
functions of different starting mapping gate voltages. As shown in
Figure 6i, the suitable range of the starting mapping gate voltages was
located between 1.5 V and 1.8 V. In the following investigations, the
filtering function started mapping from 1.5 V and ended at 3 V.
Moreover, one of the typical testing images with different levels of
Gaussian noises was demonstrated in Figure 6j, and the final testing
accuracies among the original images, noisy images and filtered images
were compared in Figure 6k, implying the neuromorphic vision systems can
process images with considerable noises which is even hard for the human
being to recognize. Finally, we also discussed the impact of different
drain biases on device performances. As shown in Figure S9, Supporting
Information, with the increase of drain biases, the decaying speed also
got boosted, but there were no notable differences in the decaying
characteristics when the biases were beyond 0.3 V. Although increased
decaying speed can help reduce the delay in the inference, as shown in
Figure S10, Supporting Information, the drain current also got increased
with the higher drain bias, which will cause extra energy costs for
computing. Therefore, final drain bias was set at 0.2 V for tackling the
dilemma.