This paper studies the optical properties of a GaN Laser Diode (LD). Through simulation, the GaN LD is optimized for the best optical confinement factor. It is found that there are optimal thicknesses of each layer in the diode that yield the highest optical confinement factor. There is a strong relationship between the optical confinement factor and lasing threshold—a higher optical confinement factor results in a lower lasing threshold. Increasing optical confinement improves lasing efficiency. Blue LDs are important to the future of lighting sources as they represent the final color in the RGB spectrum that does not have a high efficiency solution. The modeled GaN LD emits blue light at around ~450nm. Each layer of the GaN LD is drawn in a model simulation program called LaserMOD created by RSOFT Design Group, Inc. By properly modifying the properties of each layer, an accurate model of the GaN LD is created and then simulated. This paper describes the steps taken to properly model and optimize the GaN LD in the 1D and 2D models.

Yes / This work integrates a harmonic tuning mechanism in synergy with the GaN HEMT transistor for 5G mobile transceiver applications. Following a theoretical study on the operational behavior of the Class-F power amplifier (PA), a complete amplifier design procedure is described that includes the proposed Harmonic Control Circuits for the second and third harmonics and optimum loading conditions for phase shifting of the drain current and voltage waveforms. The performance improvement provided by the Class-F configuration is validated by comparing the experimental and simulated results. The designed 10W Class-F PA prototype provides a measured peak drain efficiency of 64.7% at 1dB compression point of the PA at 3.6GHz frequency.

The research of III-nitride wide-bandgap semiconductor devices, such as laser diodes (LDs), ultra-violet (UV) light-emitting diodes (LEDs), and high electron mobility transistors (HEMTs), has recently increased. Numerous opportunities exist for performance improvement in the wide bandgap semiconductor device structure, including material selection, compound compositions, polarization effects, and layer thicknesses. On the other hand, they can make optimization more challenging. It still takes a lot of resources to analyze and test complicated structures in a systematic manner. This dissertation creates a new path for device design by using TCAD and machine learning to deliver quick forecasts of III-nitride semiconductor device performance. The dissertation includes three major components. In Chapter 2, the TCAD-assisted HEMT device design is discussed. We demonstrate the performance improvement of using the new material BAlN as an interlayer in GaN/AlGaN HEMT devices and compare the various interlayer design alternatives for HEMTs. In chapter 3, we propose asymmetrical p-AlGaN/i-InGaN/n-AlGaN tunnel junctions (TJs) by combining machine learning (ML) with TCAD calculations. The resistances for 22254 various TJ structures were predicted by the model, which creates a tool for real-time TJ resistance prediction. Based on our TJ predictions, we proposed asymmetric TJ with higher Al content in the p-layer and lower TJ resistance. In Chapter 4, using the stacked XGBoost/LightGBM algorithm, we thoroughly examined the superlattice (SL) electron blocking layer (EBL) for AlGaN deep ultra-violet (DUV) LEDs. Based on the ML model, we suggest a low Al-content SL-EBL (1 nm/5 nm Al0.7Ga0.3N/Al0.58Ga0.42N) that is simpler, experimentally realizable and can greatly improve carrier transport. Additionally, we examine the prediction data and show how the composition and thickness affect the improvement of the IQE. The work contributes to the advancement of using SL-EBLs for high-efficiency DUV LEDs by providing methodical research on SL-EBLs. This dissertation presents novel approaches to the design of electrical and optical wide bandgap semiconductor devices, which opens up a new avenue for future research. It is possible that it might be used in a broad variety of fields, including illumination, sensing, disinfection, and power devices.

Machine learning models are widely used in various industries. However, the black-box nature of the model limits users’ understanding and trust in its inner workings, and the interpretability of the model becomes critical. For example, when a person’s loan application is rejected, he may want to understand the reason for the rejection and seek to improve his personal information to increase his chances of approval. Counterfactual explanation is a method used to explain the different outcomes of a specific event or situation. It modifies or manipulates the original data to generate counterfactual instances to make the model make other decision results. This paper proposes a counterfactual explanation method based on Generative Adversarial Networks (GAN) and applies it to image recognition. Counterfactual explanation aims to make the model change the predictions by modifying the feature information of the input image. Traditional machine learning methods have apparent shortcomings in computational resources when training and have specific bottlenecks in practical applications. This article builds a counterfactual explanation model based on Deep Convolutional Generative Adversarial Network (DCGAN).The original random noise input of DCGAN is converted into an image, and the perturbation is generated by the generator in the GAN network, which is combined with the original image to generate counterfactual samples. The experimental results show that the counterfactual samples generated based on GAN are better than the traditional machine learning model regarding generation efficiency and accuracy, thus verifying the effectiveness and advancement of the method proposed in this article.

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Modern RF and millimeter-wave communication links call for high-efficiency front end systems with high output power and high linearity to meet minimum transmission requirements. Advanced modulation techniques, such as orthogonal frequency-division multiplexing (OFDM) require a large power amplifier (PA) dynamic range due to the high peak-to-average power ratio (PAPR). This thesis provides the analysis, design, and experimental verification of a high-efficiency, high-linearity S-band Doherty power amplifier (DPA) based on the Class F PA. Traditional Class F PAs use harmonically tuned output matching networks to obtain up to 88.4% power-added efficiency (PAE) theoretically, however the amplifier experiences poor linearity performance due to switched mode operation, typically yielding less than 30dB C/I ratio [1]. The DPA overcomes this linearity limitation by using an auxiliary amplifier to boost output power when the amplifier is subject to a high input power due to its limited conduction cycle. The DPA also provides improved saturated output power back-off performance to maintain high PAE during operation. The DPA presented in this thesis optimizes PAE while maintaining linearity by employing harmonically tuned Class F amplifier topology on a primary and an auxiliary amplifier. A Class F PA is first designed and fabricated to optimize output network linearity – this is followed by a DPA design based on the fabricated Class F PA. A GaN HEMT Class F PA and DPA operating at 2.2GHz are implemented with the PAs measuring 40% and 45% PAE respectively while maintaining a 30dB carrier-to-intermodulation (C/I) ratio on a two-tone test. The PAE is characterized at maximum 21dBm input power per tone and 20MHz tone spacing. When subject to a single 24dBm continuous wave input tone, the Class F PA and DPA output 37dBm and 35.5dBm respectively. The PAs presented in the thesis provide over 30dB C/I ratio up to 21dBm input tones while maintaining over 40% PAE suitable for base station applications.

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