Monolithic 2D Perovskites Enabled Artificial Photonic Synapses for Neuromorphic Vision Sensors

Neuromorphic 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.

On-Chip Photonic Synapses with All-Optical Memory and Neural Network Computation

Inspired by the human brain, neural network computing was expected to break the bottleneck of traditional computing, but the integrated design still faces great challenges. Here, a readily integrated membrane-system photonic synapse was demonstrated. By pre-pulse training at 1064 nm (cutoff wavelength), the photonic synapse can be regulated both excitatory and inhibitory at tunable wavelengths (1200-2000 nm). Furthermore, more weights and memory functions were shown through the photonic synapse integrated network. Additionally, the digital recognition function of the single-layer perceptron neural network constructed by photonic synapses has been successfully demonstrated. Most of the biological synaptic functions were realized by the photonic synaptic network, and it had the advantages of compact structure, scalable, adjustable wavelength, and so on, which opens up a new idea for the study of the neural synaptic network.

All-Oxide-Based Highly Transparent Photonic Synapse for Neuromorphic Computing

The neuromorphic system processes enormous information even with very low energy consumption, which practically can be achieved with photonic artificial synapse. Herein, a photonic artificial synapse is demonstrated based on an all-oxide highly transparent device. The device consists of conformally grown In₂O₃/ZnO thin films on a fluorine-doped tin oxide/glass substrate. The device showed a loop opening in current-voltage characteristics, which was attributed to charge trapping/detrapping. Ultraviolet illumination-induced versatile features such as short-term/long-term plasticity and paired-pulse facilitation were truly confirmed. Further, photonic potentiation and electrical habituation were implemented. This study paves the way to develop a device in which current can be modulated under the action of optical stimuli, serving as a fundamental step toward the realization of low-cost synaptic behavior.

Photonic Synapses Based on Inorganic Perovskite Quantum Dots for Neuromorphic Computing

Inspired by the biological neuromorphic system, which exhibits a high degree of connectivity to process huge amounts of information, photonic memory is expected to pave a way to overcome the von Neumann bottleneck for nonconventional computing. Here, a photonic flash memory based on all-inorganic CsPbBr₃ perovskite quantum dots (QDs) is demonstrated. The heterostructure formed between the CsPbBr₃ QDs and semiconductor layer serves as a basis for optically programmable and electrically erasable characteristics of the memory device. Furthermore, synapse functions including short-term plasticity, long-term plasticity, and spike-rate-dependent plasticity are emulated at the device level. The photonic potentiation and electrical habituation are implemented and the synaptic weight exhibits multiple wavelength response from 365, 450, 520 to 660 nm. These results may locate the stage for further thrilling novel advances in perovskite-based memories.