The fast development of brain-inspired neuromorphic computing systems has stimulated urgent requirements for artificial synapses with low-power consumption. In this work, a photonic synaptic device based on (Al,Ga)N nanowire/graphene heterojunction has been proposed and demonstrated successfully. In the artificial synaptic device, the incident light, the nanowire/graphene heterojunction, and the light-generated carriers play the roles of action potential, pre-synaptic/post-synaptic membrane, and neurotransmitter in a biological synapse, respectively. As a key synaptic function, the paired pulse facilitation index of the photonic synapse can reach 202%, which can be modulated by the interval time between two adjacent light pulses. It is found that the graphene defects, the surface band bending, and the Al vacancies on the surface of (Al,Ga)N nanowires can be the key reasons contributing to the synaptic characteristics of artificial photonic devices. Hence, the dynamic “learning–forgetting” performance of the artificial synaptic device can resemble the “learning–forgetting” behavior of the human brain. Furthermore, the hand-written digits are set up to mimic a typical characteristic of human perceptual learning. After only three training epochs, the simulated network can achieve a high recognition rate of over 90% based on the experimental conductance for long-term potentiation and long-term depression. In supervised learning processes, such few training times are beneficial to reduce energy consumption significantly. Therefore, in the area of neuromorphic computing technology and artificial intelligence systems requiring low-power consumption, this work paves a potential way to develop the optoelectronic synapse based on semiconductor nanowires.

Due to the extremely high manufacturing standards, the integration of quasi-omnidirectional photodetectors and synaptic devices within a single device remains a long-standing challenge. In this work, we have designed a graphene/(Al,Ga)N nanowire heterojunction, demonstrating the monolithic integration of self-driven 360° photodetectors and artificial synapses in a dual-mode transparent device successfully. By manipulating the carrier transport dynamics through controlling the bias voltage, the degree of oxygen vacancy ionization can be precisely regulated, ultimately realizing the monolithic dual-mode device. At 0 V bias, the device functions as a fast-response self-driven photodetector with stable optical communication capabilities, achieving 360° quasi-omnidirectional photodetection. Upon applying a bias voltage, the operating mode switches to a synaptic device, which successfully simulates brain-like paired-pulse facilitation, short-/long-term plasticity processes, and learning/forgetting behaviors. The device demonstrates an exceptionally high UV/visible rejection ratio of 1.29 × 10⁴, coupled with an ultra-low dark current of less than 1 pA. Furthermore, this device has a low power consumption of 2.5 × 10⁻¹⁴ J per synaptic event, indicating an energy efficiency comparable to synaptic processes in the human brain. Moreover, nonlinear photoconductivity lets the device become a neuromorphic sensor for preprocessing images, enhancing recognition accuracy. Importantly, by leveraging the long-memory characteristic of the devices in open-circuit voltage mode, the devices have been successfully applied to guide humanoid robots in performing direction distinguishing and motion learning. This work provides new insights into the integrated manufacturing of multifunctional monolithic devices and foresees their immense potential in upcoming advanced, low-power neuromorphic computing systems.

In the natural world, the human brain is the most powerful information processor, using a highly parallel, efficient, fault‐tolerant, and reconfigurable neural network. Taking inspiration from this impressive architecture, optoelectronic synaptic devices have gained considerable attention for their ability to process and retain data simultaneously, making them essential components in the upcoming era of neuromorphic computing systems. In recent years, significant progress has been made in the development of optoelectronic neuromorphic synaptic devices using two‐dimensional (2D) material heterostructures. This review focuses on the use of 2D materials in creating optoelectronic synaptic devices. It discusses the recent progress made in utilizing 2D material heterostructures in these devices and examines their potential in different areas such as image recognition, neuromorphic wearable electronics, logical operations, and neuromorphic computing systems. Heterostructures made with 2D materials provide a wide range of possibilities as their electronic band structures can be easily tailored to achieve effective optical and electrical modulation. Optoelectronic synaptic devices based on 2D materials simultaneously exhibit two functionalities: detection and memory. Furthermore, these materials have strong interatomic bonding within layers and possess a thickness of only one atomic layer, giving them exceptional flexibility, optical transparency, and mechanical strength. By utilizing 2D materials for solution processing and their ultra‐thin profile, the manufacturing of three‐terminal synapses becomes cost‐effective, simplifying integration processes.

Integrating neuromorphic computing, photodetection and imaging in single devices remains challenging due to the inherent trade-off between the transient photoresponse speeds of artificial synapses and photodetectors. This study develops a dual-mode monolithic device using a (Al,Ga)N nanowire/graphene heterojunction, operating as a photodetector under negative bias and a neuromorphic sensor under positive bias. Graphene strengthens the built-in electric field, enhancing carrier separation and photocurrent for both functions. The device consumes ultralow energy (3.19 × 10⁻¹¹J) with demonstrated synaptic plasticity features like spike-dependent learning and accelerated memory reinforcement. Leveraging this synaptic plasticity, the device achieves over 90% accuracy in image processing tasks. This work introduces a multifunctional integration strategy that advances neuromorphic computing efficiency and optoelectronic device design, demonstrating the feasibility of simultaneous imaging and brain-inspired computation in compact systems.

Self-powered ultraviolet (UV) photodetectors hold significant commercial and practical value and are expected to be one of the next-generation optoelectronic devices requiring ultralow energy consumption. This work proposes and demonstrates a self-powered deep-UV photodetector based on (In,Ga)O nanowires. By introducing the oxidation method, (In,Ga)O nanowires can be achieved cost-efficiently. Owing to the appropriate energy level alignment, this self-powered photodetector is proved to hold the light-power-density-dependent. Moreover, by the manipulation of a humanoid intelligent robot, the application potential of the self-powered photodetector is demonstrated. Coming from the self-powered photodetector, the control commands can drive the humanoid robot to perform actions. Therefore, the design and demonstration of (In,Ga)O nanowires hold significant reference value for further developing the photodetectors and broadening their applications in ultralow-power detection systems, humanoid intelligent robots, etc.

In the post-Moore era, advancing integrated circuits necessitates the development of artificial synaptic devices capable of replicating the functions of human brain with neuromorphic computing, which consists of numerous biological synapses. The neuromorphic chips based on integrated synaptic devices are the promising candidates due to their unique properties, which enable the simulation of human brain and biological synapse plasticity. In recent years, significant progress has been made in the development of optoelectronic synaptic devices and integrated neuromorphic chips. This review summarizes the recent advancements in the integrated synaptic devices, focusing on the structures, integrated technologies and their applications of neuromorphic chips. It discusses the two-terminal and three-terminal configurations of synaptic devices, emphasizing their advantages in simulating biological synapse functions. These advantages include a simple structure, low power consumption, high stability, a wide switching ratio and fast programming speed. Furthermore, the discussion covers the key applications of synaptic devices and integrated neuromorphic chips, such as image recognition, brain-like computing, tactile and vision sensors. For the synaptic-device-based neural networks in image recognition, they have great potential in tactile technology and visual bionics. Other potential applications have also been reviewed and discussed, including intelligent robots, electric skin, human-computer interaction and brain-computer interface, etc. Hence, neuromorphic chips have great promising application prospects, which can significantly promote the development of artificial intelligence and human civilization.