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.