Artificial visual perception neural system using a solution-processable MoS2-based in-memory light sensor

Abstract

Optoelectronic devices are advantageous in in-memory light sensing for visual information processing, recognition, and storage in an energy-efficient manner. Recently, in-memory light sensors have been proposed to improve the energy, area, and time efficiencies of neuromorphic computing systems. This study is primarily focused on the development of a single sensing-storage-processing node based on a two-terminal solution-processable MoS₂ metal-oxide-semiconductor (MOS) charge-trapping memory structure-the basic structure for charge-coupled devices (CCD)-and showing its suitability for in-memory light sensing and artificial visual perception. The memory window of the device increased from 2.8 V to more than 6 V when the device was irradiated with optical lights of different wavelengths during the program operation. Furthermore, the charge retention capability of the device at a high temperature (100 °C) was enhanced from 36 to 64% when exposed to a light wavelength of 400 nm. The larger shift in the threshold voltage with an increasing operating voltage confirmed that more charges were trapped at the Al₂O₃/MoS₂ interface and in the MoS₂ layer. A small convolutional neural network was proposed to measure the optical sensing and electrical programming abilities of the device. The array simulation received optical images transmitted using a blue light wavelength and performed inference computation to process and recognize the images with 91% accuracy. This study is a significant step toward the development of optoelectronic MOS memory devices for neuromorphic visual perception, adaptive parallel processing networks for in-memory light sensing, and smart CCD cameras with artificial visual perception capabilities.

Fig. 1

Schematic of the human visual-perception process

Fig. 2

Schematic of the fabrication process of the light-sensitive MOS memory devices

Fig. 3. Effect of spin coating vs. drop casting of MoS₂ flakes on the memory electrical performance.

a, b High frequency (80 kHz) C–V curves of the D1 and D2 devices with a memory window of 0.6 V and 2.8 V, respectively. c, d Retention tests of devices D1 and D2

Fig. 4. Effect of spin coating vs. drop casting techniques on MoS₂ flakes surface coverage.

a SEM image of MoS₂ flakes using the spin-coating technique showing a lower density of flakes. b SEM image of MoS₂ flakes using the drop-casting technique showing the large density of flakes

Fig. 5. Drop casted MoS₂ material characterization.

a The SIMS depth profile of the D2 device, b XRD spectra of the drop-cast MoS₂ film, c Mo 3d and S 2s, and d S 2p XPS peaks of the drop-cast MoS₂ film

Fig. 6. MoS₂ structural characterization and the resulting charge trapping memory electrical performance.

a Raman spectrum of the drop-cast MoS₂ layer, b C–V characteristics of the D2 device for 50 repeated programmed and erased cycles with a frequency of 80 kHz, c C–V characteristics of the device with sweeping voltages of +4/−4 to +10/−10 in the programmed and erased conditions, and d memory window of the device with sweeping voltages (inset shows the frequency-dependent C–V curve of the D2 device)

Fig. 7. Reliability characteristics of MoS₂ based memory.

a Cycle-to-cycle uniformity of the D2 device, b device-to-device uniformity of the 10 devices that were chosen randomly, c long-term endurance cycles of the device, and d high-temperature retention test of the device

Fig. 8. Optical charaterization of the MoS₂ based in-memory sensor.

a Schematic of the device with optical light illumination, b C–V curves of the device using different optical light wavelengths from 600 to 400 nm with an interval of 50 nm, c wavelength-dependent threshold voltage of the device, d repeatability of C–V curves for 50 continuous cycles with optical programming (+6/−6) and electrically erasing (−8/+8), e optically programmed and electrically erased endurance of the device with illumination at a wavelength of 400 nm, and f high-temperature retention stability of the device

Fig. 9. Application of the in-memory optical sensor in artificial visual perception.

a C–V curve of the D2 device using an optical light of 50 mW cm⁻² (400 nm) from 1 to 75 µs with an interval of 5 µs; b Memory window of the device, which is optically programmed; c Memory window of the device that is electrically erased; d A small CNN model is used to make a binary classification over the CIFAR-10 dataset; e The kernels (left) are obtained from the ideal software test. The offline mapping kernels (right) are transferred from the corresponding ideal kernel values by illuminating and programming the device. f The confusion matrix of the test results for 764 images in the CIFAR-10 dataset. The yellow-colored diagonal elements in the matrix represent the correctly identified cases