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Review on partially coherent vortex beams
Jun ZENG, Rong LIN, Xianlong LIU, Chengliang ZHAO, Yangjian CAI
Front. Optoelectron.. 2019, 12 (3 ): 229-248.
https://doi.org/10.1007/s12200-019-0901-x
Ever since vortex beams were proposed, they are known for owning phase singularity and carrying orbital angular momentum (OAM). In the past decades, coherent optics developed rapidly. Vortex beams have been extended from fully coherent light to partially coherent light, from scalar light to vector light, from integral topological charge (TC) to fractional TC. Partially coherent vortex beams have attracted tremendous interest due to their hidden correlation singularity and unique propagation properties (e.g., beam shaping, beam rotation and self-reconstruction). Based on the sufficient condition for devising a genuine correlation function of partially coherent beam, partially coherent vortex beams with nonconventional correlation functions (i.e., non-Gaussian correlated Schell-model functions) were introduced recently. This timely review summarizes basic concepts, theoretical models, generation and propagation of partially coherent vortex beams.
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Development of optical-thermal coupled model for phosphor-converted LEDs
Xinglu QIAN, Jun ZOU, Mingming SHI, Bobo YANG, Yang LI, Ziming WANG, Yiming LIU, Zizhuan LIU, Fei ZHENG
Front. Optoelectron.. 2019, 12 (3 ): 249-267.
https://doi.org/10.1007/s12200-018-0857-2
In this review, first, we discussed the effect of phosphor features on optical properties by the software simulation in detail. A combination of these parameters: phosphor material, phosphor particle size and particle distribution, phosphor layer concentration, phosphor layer thickness, geometry, and location of the phosphor layer, will result in the final optical performance of the phosphor layer. Secondly, we introduced how to improve light extraction efficiency with various proposed methods. Thirdly, we summarized the thermal models to predict the phosphor temperature and the junction temperature. To stabilize the optical performance of phosphor-converted light emitting diodes (PC-LEDs), much effort has been made to reduce the junction temperature of the LED chips. The phosphor temperature, a critical reliability concern for PC-LEDs, should be attracted academic interest. Finally, we summed up optical-thermal coupled model for phosphors and summarized future optical- thermal issues exploring the light quality for LEDs. We foresee that optical-thermal coupled model for PC-LEDs should be paid more attention in the future.
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Optimization of organic light emitting diode for HAT-CN based nano-structured device by study of injection characteristics at anode/organic interface
Neha JAIN, O. P. SINHA, Sujata PANDEY
Front. Optoelectron.. 2019, 12 (3 ): 268-275.
https://doi.org/10.1007/s12200-019-0848-y
To increase the current density of the hole only device, 1, 4, 5, 8, 9, 11-hexaazatriphenylene-hexacarbonitrile (HAT-CN) material has been inserted in the device at the indium tin oxide (ITO)/organic interface. Since HAT-CN molecule can withdraw electrons, it can alter electronic properties of the electrodes and hence inserted between the organic/metal interfaces. This paper deals with the optimization of the thickness of organic-metal layers to enhance the efficiency. Also, efforts have been made to increase the current density and reduce the operating voltage of the device. The material 2, 7-bis [N , N -bis (4-methoxy-phenyl) amino]-9, 9-spirobifluorene (Meo-Spiro-TPD) is used to simulate the hole only device because it is a thermally stable hole transport material. Simulated results shows that better current density values can be achieved compared to fabricated one by optimizing the organic metal layer thickness. The best optimized layer thickness of 22 nm for Alq3 , 25 nm for CBP* doped with Ir(ppy)3 , 9 nm for Meo-Spiro TPD and 4 nm for HAT-CN which results in current density of 0.12 A/cm2 with a reduction in operating voltage by approximately 2 V.
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High accuracy object detection via bounding box regression network
Lipeng SUN, Shihua ZHAO, Gang LI, Binbing LIU
Front. Optoelectron.. 2019, 12 (3 ): 324-331.
https://doi.org/10.1007/s12200-019-0853-1
As one of the primary computer vision problems, object detection aims to find and locate semantic objects in digital images. Different with object classification, which only recognizes an object to a certain class, object detection also needs to extract accurate locations of objects. In the state-of-the-art object detection algorithms, bounding box regression plays a critical role in order to achieve high localization accuracy. Almost all the popular deep learning based object detection algorithms have utilized bounding box regression for fine tuning of object locations. However, while bounding box regression is widely used, there is few study focused on the underlying rationale, performance dependencies, and performance evaluation. In this paper, we proposed a dedicated deep neural network for bounding box regression, and presented several methods to improve its performance. Some ad hoc experiments are conducted to prove the effectiveness of the network. Also, we apply the network as an auxiliary module to the faster R-CNN algorithm and test them on some real-world images. Experiment results show certain performance improvements on detection accuracy in term of mean IOU.
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