Perovskite Oxide Nanocrystals — Synthesis, Characterization, Functionalization, and Novel Applications

Perovskite oxide nanocrystals exhibit a wide spectrum of attractive properties such as ferroelectricity, piezoelectricity, dielectricity, ferromagnetism, magnetoresistance, and multiferroics. These properties are indispensable for applications in ferroelectric random access memories, multilayer ceramic capacitors, transducers, sensors and actuators, magnetic random access memories, and the potential new types of multiple-state memories and spintronic devices controlled by electric and magnetic fields. In the past two decades, much effort has been made to synthesize and characterize the perovskite oxide nanocrystals. Various physical and chemical deposition techniques and growth mechanisms are explored and developed to control the morphology, identical shape, uniform size, perfect crystalline structure, defects, and homogenous stoichiometry of the perovskite oxide nanocrystals. This chapter provides a comprehensive review of the state-of-the-art research activities that focus on the rational synthesis, structural characterization, functionalization, and unique applications of perovskite oxide nanocrystals in nanoelectronics. It begins with the rational synthesis of perovskite oxide nanocrystals, and then summarizes their structural characterizations. Fundamental physical properties of perovskite oxide nanocrystals are also highlighted, and a range of novel applications in nanoelectronics, information storages, and spintronics are discussed. Finally, we conclude this review with some perspectives/outlook and future researches in these fields

Learning in AI Processor

AI processor, which can run artificial intelligence algorithms, is a state-of-the-art accelerator,in essence, to perform special algorithm in various applications. In particular,these are four AI applications: VR/AR smartphone games, high-performance computing, Advanced Driver Assistance Systems and IoT. Deep learning using convolutional neural networks (CNNs) involves embedding intelligence into applications to perform tasks and has achieved unprecedented accuracy [1]. Usually, the powerful multi-core processors and the on-chip tensor processing accelerator unit are prominent hardware features of deep learning AI processor. After data is collected by sensors, tools such as image processing technique, voice recognition and autonomous drone navigation, are adopted to pre-process and analyze data. In recent years, plenty of technologies associating with deep learning Al processor including cognitive spectrum sensing, computer vision and semantic reasoning become a focus in current research

Observation of the ground-state-geometric phase in a Heisenberg XY model

Geometric phases play a central role in a variety of quantum phenomena, especially in condensed matter physics. Recently, it was shown that this fundamental concept exhibits a connection to quantum phase transitions where the system undergoes a qualitative change in the ground state when a control parameter in its Hamiltonian is varied. Here we report the first experimental study using the geometric phase as a topological test of quantum transitions of the ground state in a Heisenberg XY spin model. Using NMR interferometry, we measure the geometric phase for different adiabatic circuits that do not pass through points of degeneracy.Comment: manuscript (4 pages, 3 figures) + supporting online material (6 pages + 7 figures), to be published in Phys. Rev. Lett. (2010

Process variation in Laser Powder Bed Fusion of Ti-6Al-4V

In this work, a concept of using surface roughness data as an evaluation tool of the process variation in a commercial Laser Powder Bed Fusion (L-PBF) machine is demonstrated. The interactive effects of powder recoating, spatter generation, gas flow and heat transfer are responsible for the intra-build quality inconsistency of the L-PBF process. Novel specimens and experiments were designed to investigate how surface roughness varies across the build volume and with the progression of a build. The variation in roughness has a clear and repeatable pattern due to the strong impact of the orientation of inclined surface to the laser origin. The effects of other factors such as exposure sequence of specimens, build height, and recoating process are less prominent and are difficult to isolate. A neural network regression model was built upon the large dataset in measured Ra values. The neural network model was applied to predict distribution of roughness within the build volume under hypothetical processing conditions. Connections between the predicted variation in roughness and underlying physical mechanisms are discussed. The present work has value for machine qualification and modifications which lead to the manufacturing of parts with better consistency in quality. The detailed variation observed in surface roughness can be used as a reference for designing experiments to optimise processing parameters in order to minimise the roughness of inclined surfaces

EDA: Explicit Text-Decoupling and Dense Alignment for 3D Visual Grounding

3D visual grounding aims to find the object within point clouds mentioned by free-form natural language descriptions with rich semantic cues. However, existing methods either extract the sentence-level features coupling all words or focus more on object names, which would lose the word-level information or neglect other attributes. To alleviate these issues, we present EDA that Explicitly Decouples the textual attributes in a sentence and conducts Dense Alignment between such fine-grained language and point cloud objects. Specifically, we first propose a text decoupling module to produce textual features for every semantic component. Then, we design two losses to supervise the dense matching between two modalities: position alignment loss and semantic alignment loss. On top of that, we further introduce a new visual grounding task, locating objects without object names, which can thoroughly evaluate the model's dense alignment capacity. Through experiments, we achieve state-of-the-art performance on two widely-adopted 3D visual grounding datasets, ScanRefer and SR3D/NR3D, and obtain absolute leadership on our newly-proposed task. The source code will be available at https://github.com/yanmin-wu/EDA.Comment: 16 pages with 5 pages of supplementary materia