One of the primary limitations of previously reported two-dimensional (2D) photodetectors is a low frequency response (≪ 1 Hz) for sensitive devices with gain. Yet, little efforts have been devoted to improve the temporal response of photodetectors while maintaining high gain and responsivity. Here, we demonstrate a gain of 6.3 × 10 3 electrons per photon and a responsivity of 2.6 × 10 3 A/W while simultaneously exhibiting an ultrafast response time of 40−65 μs in a hybrid photodetector that consists of graphene-WS 2 -graphene junctions covered with indium (In) adatoms atop. The resultant responsivity is 6 orders of magnitude higher than that of conventional photodetectors comprising solely of a Au-WS 2 -Au junction. The photogain is provided mainly by the adsorbed In adatoms, from which photogenerated electrons can be transferred to the WS 2 channel, while holes remain trapped in In adatoms, leading to a photogating effect as electrons are recirculating during the residence of holes in In adatoms. At a gate voltage near the Dirac point of graphene, a detectivity of D* = 2.2 × 10 12 Jones and an ON/OFF ratio of 10 4 are achieved. The enhanced performance of the device can be attributed partly to the transparent graphene/WS 2 contact and partly to the strong capacitive coupling of the In adatoms with the WS 2 channel, which enables ultrafast carrier dynamics.

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In this study, we propose an assistive system for helping visually impaired people walk outdoors. This assistive system contains an embedded system—Jetson AGX Xavier (manufacture by Nvidia in Santa Clara, CA, USA) and a binocular depth camera—ZED 2 (manufacture by Stereolabs in San Francisco, CA, USA). Based on the CNN neural network FAST-SCNN and the depth map obtained by the ZED 2, the image of the environment in front of the visually impaired user is split into seven equal divisions. A walkability confidence value for each division is computed, and a voice prompt is played to guide the user toward the most appropriate direction such that the visually impaired user can navigate a safe path on the sidewalk, avoid any obstacles, or walk on the crosswalk safely. Furthermore, the obstacle in front of the user is identified by the network YOLOv5s proposed by Jocher, G. et al. Finally, we provided the proposed assistive system to a visually impaired person and experimented around an MRT station in Taiwan. The visually impaired person indicated that the proposed system indeed helped him feel safer when walking outdoors. The experiment also verified that the system could effectively guide the visually impaired person walking safely on the sidewalk and crosswalks.

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