A dual-mode transparent device for 360° quasi-omnidirectional self-driven photodetection and efficient ultralow-power neuromorphic computing.

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 × 10, coupled with an ultra-low dark current of less than 1 pA. Furthermore, this device has a low power consumption of 2.5 × 10 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.