Owing to the development of modern display technology, micro-and mini-light-emitting diodes (LEDs) have affected technological advancements in the display industry. The structure of mini-LEDs is completely different than that of traditional LED backlights because the former employs a display technology with an LED array structure, optical resonant cavity, and color conversion layer. Display backlight technology encounters difficulty in achieving a truly effective energy-loss mechanism when designing or measuring mini-LED performance. The present study proposes three major mini-LED energy loss mechanisms: cross-talk, resonant, and quantum conversion losses. Herein, the spectrum was obtained through the integrating sphere system, and the calculation mechanism was formed by the down-conversion theorem as the decoupling principle, which successfully calculated the independent energy-loss efficiency of each mechanism and effectively deduced the brightness and color saturation performance of this design combination.
An efficient and useful method for the incorporation of colloidal quantum dots (QDs) into ionic matrices is demonstrated. We prepared three different synthesis methods, which are traditional saturated-salt water, methanol-assisted, and ethanol-assisted methods. The continuous thermal and photonic stress tests indicate that the high temperature, instead of photonic excitation stress, is more detrimental to the illumination capability of the quantum dots. While the traditional saturated-salt water synthesis and methanol-assisted method are quite effective in low temperature and low photon excitation intensity, the quantum dots sealed by the ethanol-assisted method cannot hold under all conditions. An over-1000-h aging test can provide crucial information for the longevity of these quantum dots, and more than 10,000 h of lifetime can be expected.
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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