High-power light-emitting diode (LED) chip-scale packages (CSPs) prepared by the flip-chip technology have become one of the most promising light sources. The die attach solder layer always plays an important role in heat dissipation, mechanical support, and electronic conductivity. Among different types of solder materials, Sn-3.0Ag-0.5Cu (SAC305) solder alloy shows its great competitiveness on solderability and mechanical properties for the interconnection of high-power LED CSPs. However, reliability problems caused by voids in the SAC305 solder limit its wide application in the high-power LED chip-scale packaging process. Existence of the voids has been considered as one of the major issues causing chip-on-substrate level reliability problems in microelectronic and optoelectronic devices. In this paper, mechanical and thermal properties of SAC305 solder layers with arbitrary voids used in high-power LED CSPs are studied with both finite-element simulations and experiments. The results show that void size and void position within the solder layer are the two most critical issues on the shear strength of interconnection and the chip-on-substrate level thermal distribution in high-power LED CSPs.

Silicon carbide (SiC) devices have shown definite advantages over Si counterparts in high-temperature, high-voltage, and high-frequency applications. To fully exploit the potentiality of SiC devices in high temperatures, die-attach materials that can withstand high temperatures for a long time are required in the power electronics packaging. In this article, the high-temperature die-attach materials, such as high-temperature solders and transient liquid-phase bonding materials, were reviewed first. Then, metallic (mainly Ag and Cu) nanoparticles (NPs) sintering technologies were thoroughly overviewed. The metallic NPs sintering materials, metallic NPs sintering process, and interface and reliability were analyzed, respectively. Finally, the challenges and outlook of promising Cu NPs sintering technology were discussed.

Ultraviolet light-emitting diodes (UV LEDs) play an important role in inactivating novel coronavirus pneumonia, but the lack of rapid lifetime prediction can easily cause untimely failure detection, long product development cycles, and high costs. This study predicts the lifetime of UV LEDs based on the long short-term memory (LSTM) recurrent neural network (RNN). First, the equipment setup was designed to conduct an aging test to obtain a predicted length of life for the UV LED samples using a Weibull distribution. Next, LSTM RNN was employed to predict the lifetime of the UV LEDs based on the radiation power degradation. The results were then compared with those from nonlinear least squares (NLS) regression recommended by the IESNA TM-21 industry standard. Finally, the robustness of the two methods was analyzed by changing the starting times of the predictions. The results showed that the LSTM RNN proposed in this paper reveals not only a 29.7% lower lifetime prediction error compared with the NLS regression, but also a more stable robustness. Thus, the LSTM RNN method is found to be more accurate and more robust in predicting the lifetime of UV LEDs.

This paper presents the research of optimization of road lighting energy consumption by utilizing the fact of human twilight and night vision (mesopic vision) dependency on luminance level and lamp’s light spectral composition. The research was conducted for a suburban street illuminated by smart LED road luminaires with a luminous flux control system with which different luminance levels can be achieved on the road. This road is an access road leading to a town located on the outskirts of Warsaw which is the capital of Poland and a large metropolitan area. Therefore, the traffic here is quite heavy on this road in the morning and in the evening and it is very light at other times of the day. In accordance with EN 13201 standard, lighting control can be applied to illuminate this road. This paper compares energy consumption for different lighting scenarios of the road in question. In the first scenario, the road luminance is compliant with M4, M5, and M6 lighting class requirements depending on the time of the day. In the second scenario, for each M lighting class, the values of luminance levels provided by EN 13201 standard have been reduced to the values resulting from their conversion to the corresponding mesopic luminance values. The conducted research has shown that a 15% saving per year in electricity consumption on the road is possible with such a conversion. Therefore, energy efficiency of a lighting installation can be improved by matching the lighting levels provided by the standard to the mesopic vision.