Nature Nanotechnology reports significant progress in the research of infrared sensing-memory-computing devices by SITP
The current infrared sensing technology is based on the Von Neumann architecture, using independent sensing, computing, and storage units to process the massive visual data generated by the sensing terminals. The frequent transmission of redundant data within the discrete architecture composed of sensors, processors and memory leads to high latency and high-power consumption. Therefore, there is an urgent need to develop novel optoelectronic devices that can combine sensing, storage, and computing functions into one device. The human visual system possesses a powerful ability to perceive and process visual information, primarily attributed to the pre-processing of visual information by the retina and the highly parallel storage and computing capabilities of the brain's neural network. In recent years, inspired by this, scientists have conducted in-depth research in areas such as sensor-embedded computing technology and the development of integrated sensing, storage, and computing devices.
This study introduces sulfur vacancies in two-terminal back-to-back photovoltaic detectors. By using pulsing voltage to control the spatial distribution of sulfur vacancies, it affects the spatial distribution of sulfur vacancies is modulated using pulse voltage. The results of KPFM and WDS characterizations show that the modulation of the spatial distribution of sulfur vacancies on the Schottky barriers at the metal/semiconductor interface and gets non-volatile reconfigurable 11 positive/negative photoresponse states under zero bias. This study has presented a high-performance sensing-memory-computing device with configurable responsivity, achieving a key technological breakthrough for large-scale and multi-dimensional neural vision hardware. It opens various possibilities for large-scale hardware integration and neural morphological visual applications.
Prof. Weida Hu and Prof. Jinshui Miao are the corresponding authors of this paper, with Tangxin Li being the first author. This research work received supported from the Ministry of Science and Technology of China, the National Natural Science Foundation of China, the Chinese Academy of Sciences, and the Shanghai Municipal Science and Technology Commission.
Figure 1 a. Two-terminal sensing-memory-computing devices b. Sulfur vacancies migrate with applied voltage pulses c. Neural network for object detection with the devices