We report the initial results of GaAs and GaInP solar cells grown by all solid-state molecular-beam-epitaxy (MBE) technique. For GaAs single-junction solar cell, with the application of AlInP as the window layer and GaInP as the back surface field layer, the photovoltaic conversion efficiency of 26% at one sun concentration and air mass 1.5 global (AM1.5G) is realized. The efficiency of 16.4% is also reached for GaInP solar cell. Our results demonstrate that the MBE-grown phosphide-contained III-V compound semiconductor solar cell can be quite comparable to the metal-organic-chemical-vapor-deposition-grown high-efficiency solar cell.

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.

An InGaP/GaAs tandem cell on a GaAs substrate and an InGaAsP/InGaAs tandem cell on an InP substrate were grown separately by all-solid-state molecular beam epitaxy. A room-temperature direct wafer-bonding technique was used to integrate these subcells into an InGaP/GaAs//InGaAsP/InGaAs wafer-bonded solar cell, which resulted in an abrupt interface with low resistance and high optical transmission. The current-matching design for the base layer thickness of each cell was investigated. The resulting efficiency of the four-junction solar cell was 42.0% at 230 suns, which demonstrates the great potential of the room-temperature wafer-bonding technique to achieve high conversion efficiency for cells with four or more junctions.

Underwater wireless optical communication (UWOC) is a wireless communication technology using visible light to transmit data in an underwater environment, which has wide applications. Based on lift-off (In,Ga)N nanowires, this work has proposed and successfully demonstrated a self-powered photoelectrochemical (PEC) photodetector (PD) with excellent transmissivity. The transparent functionality of the PD is critical for 360° omnidirectional underwater detection, which was realized by detaching the (In,Ga)N nanowires from the opaque epitaxial substrates to the indium tin oxide (ITO)/glass. It was also found that the insulating SiO₂ layer can enhance the photocurrent by about 12 times. The core–shell structure of the nanowires is beneficial for generating carriers and contributing to the photocurrent. Furthermore, a communication system with ASCII code is set to demonstrate the PD detection in underwater communication. This work paves an effective way to develop 360° omnidirectional PDs for the wide applications in UWOC system and underwater photodetection.