In this study, we have grown 380-nm ultraviolet light-emitting diodes (UV-LEDs) based on InGaN/AlInGaN multiple quantum well (MQW) structures on free-standing GaN (FS-GaN) substrate by atmospheric pressure metal-organic chemical vapor deposition (AP-MOCVD), and investigated the relationship between carrier localization degree and FS-GaN. The micro-Raman shift peak mapping image shows low standard deviation (STD), indicating that the UV-LED epi-wafer of low curvature and MQWs of weak quantum-confined Stark effect (QCSE) were grown. High-resolution X-ray diffraction (HRXRD) analyses demonstrated high-order satellite peaks and clear fringes between them for the UV-LEDs grown on the FS-GaN substrate, from which the interface roughness (IRN) was estimated. The temperature-dependent photoluminescence (PL) measurement confirmed that the UV-LEDs grown on the FS-GaN substrate exhibited better carrier confinement. Besides, the high-resolution transmission electron microscopy (HRTEM) and energy-dispersive spectrometer (EDS) mapping images verified that the UV-LEDs on FS-GaN have fairly uniform distribution of indium and more ordered InGaN/AlInGaN MQW structure. Clearly, the FS-GaN can not only improve the light output power but also reduce the efficiency droop phenomenon at high injection current. Based on the results mentioned above, the FS-GaN can offer UV-LEDs based on InGaN/AlInGaN MQW structures with benefits, such as high crystal quality and small carrier localization degree, compared with the UV-LEDs on sapphire.
This study presents a novel method for estimating the phosphor conversion efficiency of white light-emitting diodes (WLEDs) with different ratios of phosphors. Numerous attempts have been made for predicting the phosphor conversion efficiency of WLEDs using Monte Carlo ray tracing and the Mie scattering theory. However, because efficiency depends on the phosphor concentration, obtaining a tight match between this model and the experimental results remains a major challenge. An accurate prediction depends on various parameters, including particle size, morphology, and packaging process criteria. Therefore, we developed an efficient model that can successfully correlate the total absorption ratio to the phosphor concentration using a simple equation for estimating the spectra and lumen output. The novel and efficient method proposed here can accelerate WLED development by reducing costs and saving fabrication time.
Many automated optical inspection (AOI) companies use supervised object detection networks to inspect items, a technique which expends tremendous time and energy to mark defectives. Therefore, we propose an AOI system which uses an unsupervised learning network as the base algorithm to simultaneously generate anomaly alerts and reduce labeling costs. This AOI system works by deploying the GANomaly neural network and the supervised network to the manufacturing system. To improve the ability to distinguish anomaly items from normal items in industry and enhance the overall performance of the manufacturing process, the system uses the structural similarity index (SSIM) as part of the loss function as well as the scoring parameters. Thus, the proposed system will achieve the requirements of smart factories in the future (Industry 4.0).
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