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

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.

Kumar, D., Joharji, L., Li, H., Rezk, A., Nayfeh, A., & El-Atab, N. (2023). Artificial visual perception neural system using a solution-processable MoS2-based in-memory light sensor. Light: Science & Applications, 12(1).

The authors acknowledge financial support from the Semiconductor Initiative, King Abdullah University of Science and Technology, Saudi Arabia (KAUST Research Funding (KRF) under Award No. ORA-2022-5314). The authors also acknowledge the support from the KAUST Core labs, including the Microfluidics lab.

Springer Science and Business Media LLC

Light: Science & Applications


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