The rapid development of neuromorphic computing has stimulated extensive research interest in artificial synapses. Optoelectronic artificial synapses using laser beams as stimulus signals have the advantages of broadband, fast response, and low crosstalk. However, the optoelectronic synapses usually exhibit short memory duration due to the low lifetime of the photo-generated carriers. It greatly limits the mimicking of human perceptual learning, which is a common phenomenon in sensory interactions with the environment and practices of specific sensory tasks. Herein, a heterostructure optoelectronic synapse based on graphene nanowalls and CsPbBr₃ quantum dots was fabricated. The graphene/CsPbBr₃ heterojunction and the natural middle energy band in graphene nanowalls extend the carrier lifetime. Therefore, a long half-life period of photocurrent decay - 35.59 s has been achieved. Moreover, the long-term optoelectronic response can be controlled by the adjustment of numbers, powers, wavelengths, and frequencies of the laser pulses. Next, an artificial neural network consisting of a 28 × 28 synaptic array was established. It can be used to mimic a typical characteristic of human perceptual learning that the ability of sensory systems is enhanced through a learning experience. The learning behavior of image recognition can be tuned based on the photocurrent response control. The accuracy of image recognition keeps above 80% even under a low-frequency learning process. We also verify that less time is required to regain the lost sensory ability that has been previously learned. This approach paves the way toward high-performance intelligent devices with controllable learning of visual perception.

Recently optoelectronic synapses generating light-driven electrical memories have played a vital role in the neuromorphic computing of visual perception. However, all the optoelectronic synapses demonstrate photoelectric conversion. Peripheral circuits are used for contact photocurrent measurement, leading to significant energy consumption and impeding the evolution of optical wireless communication. It is crucial to develop noncontact neuromorphic visual perception based on light-driven photonic memories. Herein, we report all-photonic artificial synapses based on photochromic perovskites. Triggered by ultraviolet and visible light pulses, cesium lead iodide bromine induces a structural disorder. Optical transmittance changes induced by the disorder last after the pulses are gone. Next, the photonic memories are propagated in the air and processed by a recurrent neural network. The accuracy of binary image recognition is instantly stabilized at 1.0, and accuracy above 0.8 after 7 epochs is achieved in the recognition of digitals from 0 to 9. The all-photonic synapses realize remote perception with zero in-situ energy consumption and enable artificial sensory systems with low-power computation, remote control, and ultrahigh propagation speed.

Artificial intelligence applications in extreme environments place high demands on hardware robustness, power consumption, and speed. Recently, diffractive neural networks have demonstrated superb advantages in high-throughput light-speed reasoning. However, the robustness and lifetime of existing diffractive neural networks cannot be guaranteed, severely limiting their compactness and long-term inference accuracy. Here, we have developed a millimeter-scale and robust bilayer-integrated diffractive neural network chip with virtually unlimited lifetime for optical inference. The two diffractive layers with binary phase modulation were engraved on both sides of a quartz wafer. Optical inference of handwritten digital recognition was demonstrated. The results showed that the chip achieved 82% recognition accuracy for ten types of digits. Moreover, the chip demonstrated high-performance stability at high temperatures. The room-temperature lifetime was estimated to be 1.84×10²³ trillion years. Our chip satisfies the requirements for diffractive neural network hardware with high robustness, making it suitable for use in extreme environments.

Perovskite solar cells have attracted much attention due to their easy fabrication, low cost, and high photoelectric conversion efficiency. To reduce pollution, the absorption layer thickness of perovskite solar cells should be reduced. Moreover, the ultrathin layer can effectively depress the electron–hole recombination in the process of carrier transfer. However, the light absorption of the ultrathin perovskite solar cell is not satisfactory. The promising pathways to design absorption-enhanced ultrathin perovskite solar cells are plasmonic light trapping and anti-reflection coating. In this paper, we propose a design for the light absorption enhancement of ultrathin solar cells with a 100 nm perovskite layer through the integration of plasmonic structure arrays and moth-eye textured anti-reflection coatings. Due to the plasmonic scattering and the antireflection effect, an optimized light absorption enhancement of 41% has been achieved, compared with a 100 nm blank layer. In this case, a silver cylindrical array with a radius of 100 nm, a height of 120 nm, and a coverage of 12% is embedded into the rear-side hole transport layer. Inverted pyramids of the moth-eye textures with a base length of 180 nm and a depth of 125 nm are located on the front surface of the antireflection coating and further improve the perovskite light absorption. The absorbance of the 100 nm layer is dramatically raised to 72.51%, which is comparable to that of a 300 nm perovskite layer (72.86%). The simulation results pave the way for the realization of environmental-friendly and high-performance perovskite optoelectronic devices.