GaN-based light-emitting diodes (LEDs) with indium tin oxide (ITO)/Ga-doped ZnO (GZO) composite oxide films serving as a transparent contact layer (TCL) were demonstrated. In this study, the wall-plug efficiency of LEDs (LED-III) with textured ITO/GZO composite TCL can be markedly improved by 200% and 45% of magnitude as compared to conventional LEDs with Ni∕Au TCL(LED-II) and planar ITO/GZO TCL(LED-I), respectively. Compared to LED-II, this enhancement is due to the enhanced light extraction efficiency of ITO/GZO composite TCL with high transparency. Compared to LED-I, ZnO-based TCL with a higher refractive index (n∼2.0) allows further enhancement of light extraction through the creation of a textured structure on transparent conductive oxide TCL deposited on the top surface of LEDs. In addition, the ITO/GZO composite TCL with a thickness of 550nm is far larger than that of Ni∕Au TCL with a thickness of approximately 15nm. Therefore, in addition to the effect of high transparency, the thicker ITO/GZO TCL with low lateral resistance would also act as a current-spreading layer leading to an enhancement of light extraction.

InGaN/GaN light-emitting diodes ͑LEDs͒ with graded-thickness multiple quantum wells ͑GQW͒ was designed and grown by metal-organic chemical vapor deposition. The GQW structure, in which the well-thickness increases along ͓0001͔ direction, was found to have superior hole distribution as well as radiative recombination distribution by performing simulation modeling. Accordingly, the experimental investigation of electroluminescence spectrum reveals additional emission from the narrower wells within GQWs. Consequently, the efficiency droop can be alleviated to be about 16% from maximum at current density of 30 to 200 A / cm 2 , which is much smaller than that for conventional LED ͑32%͒. Moreover, the light output power was enhanced from 18.0 to 24.3 mW at 20 A / cm 2 .

We have studied the characteristics of efficiency droop in GaN-based light emitting diodes (LEDs) with different kinds of insertion layers (ILs) between the multiple quantum wells (MQWs) layer and n-GaN layer. By using low-temperature (LT) (780 °C) n-GaN as IL, the efficiency droop behavior can be alleviated from 54% in reference LED to 36% from the maximum value at low injection current to 200 mA, which is much smaller than that of 49% in LED with InGaN/GaN short-period superlattices layer. The polarization field in MQWs is found to be smallest in LED with InGaN/GaN SPS layer. However, the V-shape defect density, about 5.3×108 cm−2, in its MQWs region is much higher than that value of 2.9×108 cm−2 in LED with LT n-GaN layer, which will lead to higher defect-related tunneling leakage of carriers. Therefore, we can mainly assign this alleviation of efficiency droop to the reduction of dislocation density in MQWs region rather than the decrease of polarization field.

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The popularity of the Internet has brought the rapid development of artificial intelligence, affective computing, Internet of things (IoT), and other technologies. Particularly, the development of IoT provides more references for the realization of smart home. However, when people have achieved a certain amount of material satisfaction, they are more likely to want to communicate emotionally. Music contains a lot of emotion information. Music data is an important communication way between people and a better way to convey emotions. Therefore, it has become one of the most convenient and natural interactive ways expected by people in intelligent human-computer interaction. Traditional music emotion recognition methods have some demerits such as low recognition rate and time-consuming. So, we propose a generative adversarial network (GAN) model based on intelligent data analytics for music emotion recognition under IoT. Driven by the double-channel fusion strategy, the GAN can effectively extract the local and global features of the image or voice. Meanwhile, in order to increase the feature difference between the emotional voices, the feature data matrix of the Meyer frequency cepstrum coefficient of the music signals is transformed to improve the expression ability of the GAN. The experiment results show that the proposed model can effectively recognize the music emotion. Compared with other state-of-the-art approaches, the error recognition rate of proposed music music data recognition is greatly reduced. In terms of the accuracy, it exceeds 87% which is higher than that of other methods.