In this age of interconnection, photodetectors based on two-dimensional materials have been widely used in wearable physiological monitoring systems and wireless sensor networks. Hence, this paper proposes a novel WSe2 flake photodetector with a metal–semiconductor–metal structure. The photodetector achieved a high responsivity (Ri) of 2.46 A/W under the condition of an external bias voltage at 650 nm as well as an ultra-high detectivity (D*) of 0.76 × 1011 Jones. The photodetector overcomes the shortcomings of traditional photoelectric detectors with large dark currents. A small dark current of ∼1 nA was obtained, which also creates an opportunity for future high-performance photoelectric devices. After 500 bending cycles, the current and dark current of the device decreased only by 1.7%. Additionally, a self-built test platform indicated that the test device has a device response time of up to 6.51 ms, which creates an opportunity for the high-speed corresponding photoelectric devices based on WSe2 flakes in the future.

In organic electronics, the use of self-assembled monolayer dielectric functional groups is considered an effective surface modification strategy that can significantly improve device performance. A flexible polymer (PEDOT:PSS) photodetector on the substrate polyimide with broadband response (450 nm ∼ 980 nm) and high response (0.56 A W −1 ) is reported, which is superimposed on the surface by small molecule (C 60 ) thermal evaporation. In addition, the planeness of the organic semiconductor and the overall performance of the organic thin film photodetector can be improved significantly by using a small molecule modified polymer thin film ; the photocurrent, R i and external quantum efficiency were all increased by more than 10%. The response time of the device reached an astonishing 5.87 ms, much faster than that of similar devices. The response of the small molecule modified organic device is improved by an order of magnitude, which provides a guarantee for better application of organic thin films in circuits. Moreover, the device has a strong flexible stability performance, which can meet the requirements of flexible stability for future photoelectric devices.

Bi2O2Se has broad applications in optoelectronics and electronics because of its excellent air stability and electron mobility. Bi2O2Se is a promising candidate for high-performance infrared (IR) applications. However, very few...

An effective infrared image segmentation algorithm is essential for non-contact fault diagnosis of circuit boards. However, the uneven grayscale of the infrared images, multiple target regions, and large radiation noise pose challenges to achieving accurate segmentation and efficient data extraction for the interested regions. In this paper, we propose a segmentation algorithm based on the Deeplabv3+ network, using the lightweight MobileNetV2 as a replacement for the original Xception backbone network to improve computational efficiency and reduce overfitting. We also employ a composite loss function and cosine annealing learning rate to balance foreground-background segmentation and avoid local optima. Furthermore, we integrate the Convolutional Block Attention Module (CBAM) to extract and combine important spatial and channel features, allowing the algorithm to focus on identifying elements of the circuit board instead of background pixels, thereby improving segmentation accuracy. Experimental results demonstrate that our proposed algorithm achieves state-of-the-art performance in terms of both segmentation accuracy and computational efficiency on our self-built infrared circuit board dataset, with a MIoU of 90.34%, MPA of 95.26%, and processing speed of 25.19 fps. Overall, our proposed segmentation algorithm can effectively identify the key regions of interest in infrared images of circuit boards, providing technical support for non-contact diagnosis.

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