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    China International Forum on Solid State Lighting

    China International Forum on Solid State Lighting

    Conference

    年发文量

    研究主题

    论文(503)

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    1Performance Optimization of Deep Ultraviolet Laser Diodes with Superlattice Hole Blocking Layer
    Liya Jia, Pengfei Zhang,Mengshuang Yin,Fang Wang,Juin J. Liou,Yuhuai Liu

    In this paper, a superlattice hole blocking layer is proposed to reduce the hole leakage problem of AlGaN based deep ultraviolet laser diodes (DUV-LDs). Three kinds of laser diodes with conventional hole blocking layer, double-tapered hole blocking layer and superlattice hole blocking layer are simulated. It is found that the proposed superlattice hole blocking layer can effectively reduce the hole leakage in the n-region, improve the radiative recombination rate, and thus improve the optical and electrical properties of the device.

    2023引用:1
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    2Machine Learning Assisted Early Anomaly Detection of LEDs with Spectral Power Distribution Modeling
    Minne Liu,Mesfin S. Ibrahim,Minzhen Wen, Sheng Li,An Wang,Guoqi Zhang,Jiajie Fan

    Spectral power distribution (SPD) is the radiation power intensity at different wavelengths, containing the most basic photometric and colorimetric performance of the illuminant, which is able to predict the lifetime of LEDs. This paper proposes an SPD model assisted by machine learning algorithms to detect the early failure of white LEDs. The SPD features of 3W high-power white LEDs were firstly extracted by the statistical models of Gaussian, Lorentz, and Asym2sig functions. An unsupervised learning method, principal component analysis (PCA), was then used to reduce the extracted features parameters’ dimensions. Next a K-nearest neighbor (KNN)-based method was used to detect LEDs’ anomalies by dividing the main cluster into groups, and estimating the distance from the center of mass of each cluster to the test point. The results showed the following: (1) for selected white LEDs, the Asym2sig function has a better fitting result than Gaussian and Lorentz functions; (2) machine learning methods can significantly assist in LED anomaly detection and can decrease the amount of anomaly detection time to 789.6 h, compared to the 1311 h when lumen maintenance degradation reaches 70% as required by IES TM21.

    2023
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    3Transfer Printing Quantum Dots for Full-Color Micro-Led Display
    Yanzhe Li,Xinyi Shan,Zhou Wang, Shijie Zhu,Xugao Cui,Gaoshan Huang,Yongfeng Mei,Pengfei Tian

    A distributed Bragg reflector (DBR) was designed and fabricated to reflect the excess blue light to both increase the utilization of the blue light from micro-LED and improve the color purity of the red and green light from QDs. Red and green QDs arrays on glass substrate were presented by transfer printing via polydimethylsiloxane (PDMS) stamp. The red QDs transferred on the DBR as color-converting layer was then attached with the blue micro-LED, and the red QDs down-converted the blue emission from the micro-LED to the red emission. These results provided the promising potential of transfer printing QDs as color-converting layer to realize full-color micro-LED display.

    2023引用:1
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    4High Efficiency Nitride Green LEDs with Built-in PL Color Converter
    C. H. Yan, Y. H. Du, W. Sun, A. L. Yang, D. W. Nie, B. B. Shen, C. Y. Yu

    The well-known "Green gap" issue related to LEDs efficiency significantly limits the full color applications in both display and lighting. In order to solve this problem, in this study, a new green LED structure is proposed, which includes a regular short wavelength MQWs for the electroluminescence (EL) emission, and a green photoluminescence (PL) color conversion region, so called EP-LED. The shorter wavelength EL region is responsible for carrier injection and recombination, which suffers from a much weaker QCSE, and the longer wavelength PL region is responsible for high quantum efficiency color conversion, whose strain field has no effect on carrier injection. With this novel hybrid active-region design, the green EP-LED shows more than 30% improved WPE over the conventional one at current density of 50 A/cm2, and 1/3 suppressed blueshift in wavelength in the current range of 1 mA to 100 mA, as well as the much-reduced efficiency Droop. It is very encouraging that the green or long wavelength EP-LEDs will meet many new needs in future display and lighting applications.

    2023
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    5Research on Monolithic Integrated Driver of Micro LED
    Fang Aoqi,Guo Weiling,Xu Hao,Liu Jixin

    Micro LED (light-emitting diode) is maturely used in the fields of space display, medical detection and visible light communication due to its unique features of miniaturization, low power consumption, fast response speed and high resolution. However, since the driver circuits are usually composed of Si devices, a large number of Micro LED pixels must be transferred from their GaN substrates to bond with Si field-effect transistors (FETs) by mass transfer techniques. Since there are certain technical bottlenecks in the mass transfer technology of Micro LED, relevant scholars at home and abroad have successively put forward the idea of integrating LED and field effect transistor driving circuit on the same substrate, so as to avoid the issues of low yield and poor accuracy. This paper summarizes the research on monolithic integrated active driving of LEDs and Micro LEDs at home and abroad in recent years. These researches include: monolithic integration of high mobility field effect transistors (HEMTs) and LEDs, metal oxide field effect transistors (MOSFET) and LED monolithic integration and thin film field effect transistor (TFT) monolithic integration with Micro LED.

    2023引用:5
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    高被引作者

    作者引用发文
    Kuang Sheng11014
    Junxi Wang8620
    Hongyi Xu828
    Xinke Wu744
    Ricky Lee6612
    Yandan Lin6518
    Jiahui Sun621
    Guoqi Zhang (Kouchi)6118
    Xuejun Fan568
    Brian Shieh517

    高产作者

    作者引用发文
    Junxi Wang8620
    Yandan Lin6518
    Guoqi Zhang (Kouchi)6118
    Jinmin Li4816
    Weiling Guo1816
    Xiangang Xu1115
    Kuang Sheng11014
    Ricky Lee6612
    Luqiao Yin1212
    Jianhua Zhang1111

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