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14th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2025

作者： Ghosh, Mohendro Kumar Juds, Mark A. Grainger, Brandon El Shafei, Ahmad Borowy, Bogdan S. Ohodnicki, Paul University of Pittsburgh Department of Mechanical Engineering and Material Science Pittsburgh United States University of Pittsburgh Department of Electrical and Computer Engineering Pittsburgh United States Eaton Research Labs Eaton Corporation Menomonee FallsWI United States

ISBN: (纸本)9798331516116

A significant emphasis exists on enabling a new generation of medium voltage and medium frequency solid state transformers using ferrite and nanocrystalline alloys for grid-tied applications including renewable integration and electric vehicle charging. Isolation requirements become a critical aspect of the transformer design in such cases, with a key design requirement being the winding-to-winding and core-to-winding spacing requirements. This paper investigates the critical impacts of isolation requirements on medium frequency transformer designs, comparing nanocrystalline and ferrite core materials in such applications. It is found that leakage flux loss becomes a critical factor as isolation distance increases. To illustrate this, a reluctance-based Magnetic Equivalent Circuit (MEC) of a single-phase shell-type transformer constructed with two EE cores is used to estimate leakage flux and associated leakage loss. Experimental results populated from a 10 kHz transformer prototype validate the model and design methodology. This paper also presents the design trade-offs between leakage flux core loss and isolation space, incorporating multi-objective optimization of 50 kW, 10 kHz transformer with ferrite and nanocrystalline cores, based on the NSGA-II algorithm under specific constrained conditions. © 2025 IEEE.

关键词： Transformer windings

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A Microfluidic Platform for Modeling Molecular Communication

A Microfluidic Platform for Modeling Molecular Communication

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Communications and Networking (BalkanCom), International Balkan Conference on

作者： Gokce Duzyol Merve Gorkem Durmaz Oguzhan Yetimoglu Abdurrahman Dilmac Zeliha Cansu Canbek Ozdil Ali Emre Pusane Tuna Tugcu Department of Electrical and Electronics Engineering Bogazici University Istanbul Turkey Department of Computer Engineering Bogazici University Istanbul Turkey Department of Material Science and Nanotechnology Engineering Yeditepe University Istanbul Turkey

Molecular communication (MC) exploits the opportunities to build up a communication link at a small scale to improve the capabilities of nanomachines by forming a network between multiple machines. Even though there are significant theoretical and conceptual frameworks for the further advancement of MC, only a few studies seem to deal with more practical aspects of communication theory, especially on a micro-scale. To this extent, this paper presents an experimental platform based on microfluidics to investigate the physical response of MC environments to enable practical applications. In this communication scenario, polystyrene microbeads are used to convey the information in a controlled microenvironment to transmit encoded data under continuous pressure-driven background flow. The observation is performed with an integrated optical setup. The resulting videos are processed by an image processing algorithm for automated particle recognition and counting. Outcome data are decoded to reproduce the transmitted bit sequences. By adopting the number of microbeads as the information carrier, signals are modulated via on-off keying and demodulated in the same way. As a result, we achieve information transmission and particle detection in a microenvironment. The proposed experimental model’s behavior is also verified by simulations utilizing the COMSOL software.

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LRMP: Layer Replication with Mixed Precision for Spatial In-memory DNN Accelerators

arXiv

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arXiv 2023年

作者： Nallathambi, Abinand Bose, Christin David Haensch, Wilfried Raghunathan, Anand School of Electrical and Computer Engineering Purdue University West LafayetteIN United States Material Science Division Argonne National Laboratory ArgonneIL United States

In-memory computing (IMC) with non-volatile memories (NVMs) has emerged as a promising approach to address the rapidly growing computational demands of Deep Neural Networks (DNNs). Mapping DNN layers spatially onto NVM-based IMC accelerators achieves high degrees of parallelism. However, two challenges that arise in this approach are the highly non-uniform distribution of layer processing times and high area requirements. We propose LRMP, a method to jointly apply layer replication and mixed precision quantization to improve the performance of DNNs when mapped to area-constrained NVM-based IMC accelerators. LRMP uses a combination of reinforcement learning and integer linear programming to search the replication-quantization design space using a model that is closely informed by the target hardware architecture. Across five DNN benchmarks, LRMP achieves 2.8-9× latency and 11.8-19× throughput improvement at iso-accuracy. © 2023, CC BY.

关键词： Deep neural networks

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In-situ real-time monitoring of GeSn growth using UHV-CVD to achieve high-quality material with lasing at 2250 nm and 100 K [Invited]

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Optical materials Express 2025年第6期15卷 1371-1381页

作者： Grant, Joshua Acharya, Sudip Golden, Alexander Yang, Enbo Stanchu, Hryhorii Chang, Guo-En Sun, Greg Du, Wei Yu, Shui-Qing Department of Electrical Engineering and Computer Science University of Arkansas FayettevilleAR72701 United States Material Science and Engineering Program University of Arkansas FayettevilleAR72701 United States Institute for Nanoscience and Engineering University of Arkansas FayettevilleAR72701 United States Department of Mechanical Engineering National Chung Cheng University Chiayi62102 Taiwan Department of Engineering University of Massachusetts BostonMA02125 United States

GeSn alloys hold great promise for the development of Si photonics due to its direct bandgap with sufficient high Sn composition, tunable bandgap energies covering broad infrared wavelength range, and compatibility with the complementary metal-oxide-semiconductor (CMOS) process. For the past decades, tremendous efforts have been made to develop material growth recipes to obtain device-level material quality using chemical vapor deposition (CVD) reactors. A common issue in material growth is the formation of Sn droplets (i.e., Sn segregation) that seriously degrades the material quality. However, although industrial CVD has delivered high-quality materials for device demonstration, the dynamic process of material growth remains unavailable, and only ex-situ material characterizations could provide feedback. In this work, a specially designed ultra-high-vacuum CVD (UHV-CVD) was used to study the growth dynamic by employing an in-situ optical system, which enables us to monitor the formation of Sn droplets in real-time. The Sn segregation occurring at the cooling stage after growth completion rather than during material growth was discovered, indicating that a post-growth treatment is needed to minimize the Sn droplet formation. By extending the GeSn growth time and controlling the cooling cycle, the number of Sn droplets dramatically reduced, leading to a high-quality strain-relaxed GeSn layer with 10.2% Sn composition and over 730 nm thickness. material quality is comparable with those grown using commercial RPCVD in the early stage, which was further confirmed by the demonstration of an optically pumped laser. A threshold of ∼600 kW/cm2 at 77 K and a maximum operating temperature of 100 K were obtained, with the lasing peak at ∼2250 nm. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Optical pumping

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Risk-Averse Learning with Non-Stationary Distributions

arXiv

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arXiv 2024年

作者： Wang, Siyi Wang, Zifan Yi, Xinlei Zavlanos, Michael M. Johansson, Karl H. Hirche, Sandra Department of Electrical and Computer Engineering Technical University of Munich Munich80333 Germany Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden Lab for Information & Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States Mechanical Engineering and Material Science Duke University DurhamNC27708 United States

Considering non-stationary environments in online optimization enables decision-maker to effectively adapt to changes and improve its performance over time. In such cases, it is favorable to adopt a strategy that minimizes the negative impact of change to avoid potentially risky situations. In this paper, we investigate risk-averse online optimization where the distribution of the random cost changes over time. We minimize risk-averse objective function using the Conditional Value at Risk (CVaR) as risk measure. Due to the difficulty in obtaining the exact CVaR gradient, we employ a zeroth-order optimization approach that queries the cost function values multiple times at each iteration and estimates the CVaR gradient using the sampled values. To facilitate the regret analysis, we use a variation metric based on Wasserstein distance to capture time-varying distributions. Given that the distribution variation is sub-linear in the total number of episodes, we show that our designed learning algorithm achieves sub-linear dynamic regret with high probability for both convex and strongly convex functions. Moreover, theoretical results suggest that increasing the number of samples leads to a reduction in the dynamic regret bounds until the sampling number reaches a specific limit. Finally, we provide numerical experiments of dynamic pricing in a parking lot to illustrate the efficacy of the designed algorithm. Copyright © 2024, The Authors. All rights reserved.

关键词： Learning algorithms

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An IoT-based Parameter Extraction Platform for Powder Metallurgy Sintering Furnace

An IoT-based Parameter Extraction Platform for Powder Metall...

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International Conference on Internet of Things: Systems, Management and Security

作者： Shih-Man Chang Hao Pu Lin Chin-Chuan Han Yu-Chi Wu Dept. of Computer Science and Information Engineering National United University Miao-Li Taiwan Program in Material and Chemical Engineering National United University MiaoLi Taiwan Dept. of Electrical Engineering National United University Miao-Li Taiwan

ISBN: (数字)9798350366501

ISBN: (纸本)9798350366518

Internet of Things (IoT) technology has widely used in industrial production. Most cases focused on designing new equipment or processes. However, small and medium enterprises especially the powder metallurgy manufacturers in Taiwan would not replace existing production equipment due to the finical problems. In this study, an IoT-based platform is conducted to extract the sintering parameters for technology to augment sintering furnaces by installing sensors and data transmission modules, setting a sintering parameter extraction platform for powder metallurgy sintering furnace. Long-term monitoring and remote control are the main technologies for solving three problems during the sintering process, i.e., temperature, atmosphere, and time. Monitoring targets include checking whether the heaters are damaged, ensuring stable air flow meter readings, and verifying the normal speed of the conveyor belt. Additionally, remote control technology has been developed to execute cooling and reheating processes on the sintering furnace. The staying time on-site for personnel is reduced and the sintering time is increased for production increase during working hours.

关键词： Temperature sensors Powders Furnaces Sintering Process control Metallurgy Internet of Things Remote control Monitoring Parameter extraction

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Metasurfaces That Enable Bottle-Beam Arrays for Neutral Atom Trapping

Metasurfaces That Enable Bottle-Beam Arrays for Neutral Atom...

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CLEO: science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Fang, Chengyu Kim, Minjeong Czaplewski, David A. Mei, Hongyan Deshpande, Sanket Yu, Zhaoning Xiao, Yuzhe Yang, Xuting Dibos, Alan M. Saffman, Mark Choy, Jennifer T. Kats, Mikhail A. Department of Electrical and Computer Engineering University of Wisconsin-Madison MadisonWI53706 United States Center for Nanoscale Materials Argonne National Laboratory LemontIL60349 United States Department of Physics University of Wisconsin-Madison MadisonWI53706 United States Department of Physics University of North Texas DentonTX76201 United States Department of Material Science and Engineering University of Wisconsin-Madison MadisonWI53706 United States

We report optical metasurfaces that enable bottle-beam arrays for blue-detuned trapping of neutral atoms. Our approach only requires a single metasurface which significantly reduces the complexity of the trapping expe... 详细信息

关键词： Atoms

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Recent advances and applications of deep learning methods in materials science

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npj Computational materials 2022年第1期8卷 548-573页

作者： Kamal Choudhary Brian DeCost Chi Chen Anubhav Jain Francesca Tavazza Ryan Cohn Cheol Woo Park Alok Choudhary Ankit Agrawal Simon J.L.Billinge Elizabeth Holm Shyue Ping Ong Chris Wolverton Materials Science and Engineering Division National Institute of Standards and TechnologyGaithersburgMD20899USA Theiss Research La JollaCA92037USA DeepMaterials LLC Silver SpringMD20906USA Material Measurement Science Division National Institute of Standards and TechnologyGaithersburgMD20899USA Department of NanoEngineering University of California San DiegoSan DiegoCA92093USA Energy Technologies Area Lawrence Berkeley National LaboratoryBerkeleyCAUSA Department of Materials Science and Engineering Carnegie Mellon UniversityPittsburghPA15213USA Department of Materials Science and Engineering Northwestern UniversityEvanstonIL60208USA Department of Electrical and Computer Engineering Northwestern UniversityEvanstonIL60208USA Department of Applied Physics and Applied Mathematics and the Data Science Institute Fu Foundation School of Engineering and Applied SciencesColumbia UniversityNew YorkNY10027USA

Deep learning(DL)is one of the fastest-growing topics in materials data science,with rapidly emerging applications spanning atomistic,image-based,spectral,and textual data *** allows analysis of unstructured data and automated identification of *** recent development of large materials databases has fueled the application of DL methods in atomistic prediction in *** contrast,advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL *** this article,we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation,materials imaging,spectral analysis,and natural language *** each modality we discuss applications involving both theoretical and experimental data,typical modeling approaches with their strengths and limitations,and relevant publicly available software and *** conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations,challenges,and potential growth areas for DL methods in materials science.

关键词： learning limitations textual

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Metasurfaces That Enable Bottle-Beam Arrays for Neutral Atom Trapping

Metasurfaces That Enable Bottle-Beam Arrays for Neutral Atom...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

ISBN: (纸本)9781957171395

关键词： Atom lasers

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Tunnel Junction-Enabled Monolithically Integrated GaN Micro-Light Emitting Transistor

arXiv

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arXiv 2024年

作者： Rahman, Sheikh Ifatur Awwad, Mohammad Joishi, Chandan Jamal-Eddine, Zane Gunning, Brendan Armstrong, Andrew Rajan, Siddharth Department of Electrical and Computer Engineering The Ohio State University ColumbusOH United States Sandia National Laboratory AlbuquerqueNM United States Department of Material Science and Engineering The Ohio State University ColumbusOH United States

GaN/InGaN microLEDs are a very promising technology for next generation displays. Switching control transistors and their integration are key components in achieving high-performance, efficient displays. Monolithic integration of microLEDs with GaN switching devices provides an opportunity to control microLED output power with capacitive (voltage) control rather than current controlled schemes. This approach can greatly reduce system complexity for the driver circuit arrays while maintaining device opto-electronic performance. In this work, we demonstrate a 3-terminal GaN micro-light emitting transistor that combines a GaN/InGaN blue tunneling-based microLED with a GaN n-channel FET. The integrated device exhibits excellent gate control, drain current control and optical emission control. This work provides a promising pathway for future monolithic integration of GaN FETs with microLED to enable fast switching high efficiency microLED display and communication systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Transistors

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