2.3. Image reconstructions through short-term accumulations of
the ion-modulated memtransistors
For human beings, identifying and reconstructing the original images
from a series of noisy images is simple and fast, as shown in Figure 4a.
It is difficult to identify the original images from each of these
series alone. However, it is easier to identify them once they are
presented subsequently, which gives inspiration that we can take
advantage of the short-term memory features of the devices to extract
the real content behind a series of disturbing images. First, we convert
the pixel values of different time points corresponding to the same
location of the images into electrical pulses, using binary images and
four different time points for the convenience of testing, as shown on
the right of Figure 4b. After that, the converted electrical pulses were
applied to the gate of the devices with 0.2 V drain-source bias for
reading. Pixel values 1 and 0 correspond to pulse amplitude values of 3
V and 0 V respectively while keeping the width at 100 ms and the period
at 150 ms. A combination of spatio-temporal information corresponding to
the final drain-source current was summarized in the left of Figure 4b.
The three patterns corresponding to the letters ‘X’, ‘Y’ and ‘Z’ with
some random noises added manually, which makes it hard to identify
exactly which letter of each image among them. Then these converted
electrical pulses were fed into the ion-modulated-memtransistors-based
array according to the previous spatiotemporal information encoding
scheme, as schematically depicted in Figure 4c. Detailed encodings for
each letter can be found in Figure S3, Supporting information. Finally,
the resulting channel current changes are summarized, as shown in Figure
4d. The specific current variation for each pixel can be found in Figure
S4, S5 and S6, Supporting Information. The reconstructed images can show
the original letter patterns more clearly, implying that the original
feature information with extra noises can be accumulated and filtered at
the same time, the signal-to-noise ratio can be improved and the
original factual information then can be recognized.