Affiliation:
1. Center for Future Optoelectronic Functional Materials School of Computer and Electronic Information/School of Artificial Intelligence Nanjing Normal University Nanjing 210023 P. R. China
2. National Laboratory of Solid State Microstructures Nanjing University Nanjing 210093 P. R. China
3. State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing 100876 P. R. China
4. Centre for Atomaterials and Nanomanufacturing School of Science RMIT University Melbourne Victoria 3000 Australia
5. College of Materials Science and Engineering Qingdao University of Science and Technology Qingdao 266042 P. R. China
Abstract
AbstractNeuromorphic visual sensors (NVS) based on photonic synapses hold a significant promise to emulate the human visual system. However, current photonic synapses rely on exquisite engineering of the complex heterogeneous interface to realize learning and memory functions, resulting in high fabrication cost, reduced reliability, high energy consumption and uncompact architecture, severely limiting the up‐scaled manufacture, and on‐chip integration. Here a photo‐memory fundamental based on ion‐exciton coupling is innovated to simplify synaptic structure and minimize energy consumption. Due to the intrinsic organic/inorganic interface within the crystal, the photodetector based on monolithic 2D perovskite exhibits a persistent photocurrent lasting about 90 s, enabling versatile synaptic functions. The electrical power consumption per synaptic event is estimated to be≈1.45 × 10−16 J, one order of magnitude lower than that in a natural biological system. Proof‐of‐concept image preprocessing using the neuromorphic vision sensors enabled by photonic synapse demonstrates 4 times enhancement of classification accuracy. Furthermore, getting rid of the artificial neural network, an expectation‐based thresholding model is put forward to mimic the human visual system for facial recognition. This conceptual device unveils a new mechanism to simplify synaptic structure, promising the transformation of the NVS and fostering the emergence of next generation neural networks.
Funder
Natural Science Foundation of Shandong Province
China Postdoctoral Science Foundation
National Natural Science Foundation of China
Subject
Mechanical Engineering,Mechanics of Materials,General Materials Science