Picosecond ultrasonics is a powerful tool for nanoscale metrology, giving access to dimensions and mechanical, thermal, and optical properties of nanomaterials. By monitoring the temporal evolution of the interaction of light with coherent acoustic phonons, also known as Brillouin oscillations, phonon lifetime and optical absorption can be measured. However, the extraction of these quantities can be inaccurate due to the common assumption of the infinite coherence length of probe pulses. Here, we demonstrate the effect of probe pulse duration on picosecond ultrasonic measurements numerically and experimentally. We establish a model that shows how the probe coherence length affects the measured signal loss and how we can overcome this limitation and measure an upper limit of the acoustic attenuation factor. The model is verified experimentally on a GaAs bulk substrate by varying the probe pulse duration, showing a strong effect for sub-100 fs pulses. Finally, we applied to CH3NH3PbBr3, where we reveal a high acoustic attenuation factor, which is in line with recent claims of strong anharmonicity in halide perovskites.

In recent years, the recommendation system and robot learning are undoubtedly the two most popular application fields, and the core algorithms supporting these two fields are deep learning based on perception and reinforcement learning based on exploration learning, respectively. How to combine these two fields to better improve the development of the whole machine learning field is the dream of numerous researchers. The Deep Reinforcement Network (DRN) model successfully embedded reinforcement learning into the recommendation system, which provided a good idea for subsequent researchers. However, the disadvantage is also obvious, that is, the DRN model is built for news recommendations, meaning that the DRN model is not transferable, which is also the defect of many current recommendation system models. Meanwhile, the agent learning method adopted by the DRN model is primitive and inefficient. Among many models and algorithms that have emerged in recent years, we use the newly proposed deployment efficiency to measure their comprehensive quality and found that few models focus on both efficiency and performance improvement. To fill the gap of model deployment efficiency neglected by many researchers and to create a model of reinforcement learning agents with stronger performance, we have been exploring and trying to complete research on the Gate Attentional Factorization Machines (GAFM) model. Finally, we successfully integrated the GAFM model and reinforcement learning. The Deep Reinforcement Factorization Machines (DRFM) model proposed in this paper is based on the combination of deep learning with high perception ability and reinforcement learning with high exploration ability, centered on improving the deployment efficiency and learning performance of the model. The GAFM model is modified and upgraded using multidisciplinary techniques, and a new model-based random exploration strategy is proposed to update and optimize the recommendation list efficiently. Through parallel contrast experiments on various datasets, it is proved that the DRFM model surpasses the traditional recommendation system model in all aspects. The DRFM model is far superior to other models in terms of performance and robustness, and also significantly improved in terms of deployment efficiency. At the same time, we conduct a comparative analysis with the latest deep reinforcement learning algorithm and prove the unique advantages of the DRFM model.

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We report gallium nitride (GaN) vertical trench junction barrier Schottky (TJBS) diodes and systematically analyzed the effects of the key design parameters on the reverse and forward characteristics of the devices. By taking advantage of the shielding effects from both the trenches and pn junctions in the TJBS structure, the high electric field at the Schottky contact region can be effectively suppressed. We found that the doping concentration, thickness, and spacing of p-GaN, as well as the depth and angle of the trench sidewalls are closely associated with the electric field distribution and the reverse characteristics of the TJBS diodes. With an optimal set of design parameters, the local electric field crowding at either the corner of the trench or the edge of the p-GaN can also be alleviated, resulting in a boosted breakdown voltage of up to 1250 V in the TJBS diodes. In addition, an analytical model was developed to explore the physical mechanism behind the forward conduction behaviors. We believe that the results can provide a systematical design strategy for the development of low-loss, high-voltage, and high-power GaN power diodes towards an efficient power system.