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X-ORIGINAL-URL:https://ece.hku.hk
X-WR-CALDESC:Events for Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系
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TZID:Asia/Hong_Kong
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DTSTART:20240101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250307T093000
DTEND;TZID=Asia/Hong_Kong:20250307T223000
DTSTAMP:20260512T022506
CREATED:20250304T072753Z
LAST-MODIFIED:20250306T015705Z
UID:110555-1741339800-1741386600@ece.hku.hk
SUMMARY:Revolutionizing Power Electronics with Heterogeneous Integration
DESCRIPTION:Abstract\nTraditional power electronic equipment has long relied on discrete active and passive components\, with performance enhancements often requiring trade-offs. Despite technological advancements\, manufacturing processes remain labor-intensive and largely unchanged for decades. \nThe advent of wide-bandgap (WBG) power semiconductor devices\, such as silicon carbide (SiC) and gallium nitride (GaN)\, has significantly reduced conduction and switching losses compared to silicon-based counterparts. However\, current design methodologies primarily follow a ‘plug-and-play’ approach\, yielding only incremental improvements in efficiency and power density without fully leveraging the transformative potential of these technologies. \nThis presentation explores the integration of matrix magnetics with WBG power devices to drive a fundamental shift in power electronics design and manufacturing through heterogeneous integration. This holistic approach enables simultaneous enhancements in efficiency\, power density\, cost\, and electromagnetic interference (EMI) performance. Additionally\, it streamlines traditionally labor-intensive manufacturing processes—particularly those involving magnetics and system assembly—through automation. \nThe discussion will feature multiple research examples demonstrating heterogeneous integration of matrix magnetics in power converters across diverse applications and power ranges. These include high-frequency power converters for artificial intelligence (AI) and high-performance computing systems\, battery chargers for electric vehicles\, and solid-state transformers for DC power distribution. \n\nSpeaker\nProf. Qiang LI\nCenter for Power Electronics Systems (CPES)\,\nVirginia Tech \nBiography of the Speaker\nQiang Li received the B.S. and M.S. degrees from Zhejiang University\, China\, in 2003 and 2006\, respectively\, and the Ph.D. degree from Virginia Tech\, Blacksburg\, VA\, in 2011. He is currently a full professor in the Center for Power Electronics Systems (CPES) at Virginia Tech. His research interests include high-frequency power conversion and control\, high-density electronics packaging and magnetic integration\, as well as power solutions for high-performance computing\, data centers\, electric vehicles\, and energy storage systems. With over 300 peer-reviewed technical publications\, including 100 journal articles\, he has received eight prize paper awards and holds 26 U.S. patents. He currently serves as the Chair of Academic Affairs for the IEEE Power Electronics Society and is an associate editor for both the IEEE Transactions on Power Electronics and the IEEE Journal of Emerging and Selected Topics in Power Electronics. He is also a recipient of the U.S. National Science Foundation (NSF) Career Award. \nOrganiser\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20250307-2/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2025/03/1280-2.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250307T100000
DTEND;TZID=Asia/Hong_Kong:20250307T110000
DTSTAMP:20260512T022506
CREATED:20250228T090613Z
LAST-MODIFIED:20250228T090613Z
UID:110553-1741341600-1741345200@ece.hku.hk
SUMMARY:RPG Seminar – Six Degrees of Freedom Tracking and Wireless Charging for Capsule Endoscopy
DESCRIPTION:Zoom \nLink: https://hku.zoom.us/j/95045238787?pwd=bAEbF49kv8uPbKPQG1n3B5I9bSphnT.1\nMeeting ID: 950 4523 8787\nPassword: 123456 \nAbstract\nCapsule endoscopy is an emerging technology providing a non-invasive approach to diagnose and monitor gastrointestinal diseases. However\, this technology has fundamental limitations of short-range of investigation due to limited battery capacity and uncertain position of the capsule inside the gastrointestinal tract. This research work attempts to address these two critical issues using a novel approach to seamlessly combine wireless battery charging\, and 6 degrees of freedom (6DoF) tracking of capsule endoscopy on a single apparatus. A full-scale complete prototype is presented in this paper\, wherein the receiving coil is integrated in a fully functional capsule\, and the transmitting coil is able to accommodate an adult human body. In the tracking operation\, a gradual DC magnetic field is generated in the transmitting coil. The magnetic field intensity surrounding the capsule is measured and consolidated with inertial measurement to identify the capsule’s position and orientation. In the evaluation\, the maximum mean absolute error for attitude angles and position are less than 3.80° and 2.07 mm\, respectively. A relatively uniform AC magnetic field will be generated while a low battery is detected. During wireless charging\, the capsule receives 376.08 mW of power\, resulting in an estimated charging rate of 2C for the capsule battery. \nSpeaker\nMr. ZHANG Heng\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nBiography of the Speaker\nHeng ZHANG received the B.Eng. degree in 2017\, and the M.Eng. degree in 2020\, both in Electrical Engineering and Automation\, from Taiyuan University of Technology\, Taiyuan\, Shanxi\, China. From 2021 to 2022\, he was a research assistant with the Chinese University of Hong Kong\, Hong Kong. He is currently working toward the Ph.D. degree in electrical and electronic engineering from the University of Hong Kong\, Hong Kong. His research interests include sensing technology\, medical robotic design and control\, wireless power transfer\, and power electronics. \nOrganiser\nProf. Chi-Kwan Lee \nAll are welcome.
URL:https://ece.hku.hk/events/20250307-1/
LOCATION:Online via Zoom
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250312T100000
DTEND;TZID=Asia/Hong_Kong:20250312T110000
DTSTAMP:20260512T022506
CREATED:20250312T012704Z
LAST-MODIFIED:20250312T012704Z
UID:110582-1741773600-1741777200@ece.hku.hk
SUMMARY:2D Materials for Next-Generation Electronics: From Low-Power Logic to Monolithic Memory
DESCRIPTION:Abstract\nSilicon has been the dominant material for electronic computing for decades and very likely will stay dominant for the foreseeable future. However\, it is well-known that Moore’s law that propelled Silicon into this dominant position is long dead. Therefore\, a fervent search for (i) new semiconductors that could directly replace silicon or (ii) new architectures with novel materials/devices added onto silicon or (iii) new physics/state-variables or a combination of above has been the subject of much of the electronic materials and devices research of the past 2 decades. The above problem is further complicated by the changing paradigm of computing from arithmetic centric to data centric in the age of billions of internet-connected devices and artificial intelligence as well as the ubiquity of computing in ever more challenging environments. Therefore\, there is a pressing need for complementing and supplementing Silicon to operate with greater efficiency\, speed and handle greater amounts of data. This is further necessary since a completely novel and paradigm changing computing platform (e.g. all optical computing or quantum computing) remains out of reach for now. The above is however not possible without fundamental innovation in new electronic materials and devices. Therefore\, in this talk\, I will try to make the case of how novel layered two-dimensional (2D) chalcogenide materials1 and three-dimensional (3D) nitride materials might present interesting avenues to overcome some of the limitations being faced by Silicon hardware. I will start by presenting our ongoing and recent work on integration of 2D chalcogenide semiconductors with silicon2 to realize low-power tunnelling field effect transistors. In particular I will focus on In-Se based 2D semiconductors2 for this application and extend discussion on them to phase-pure\, epitaxial thin-film growth over wafer scales\,3 at temperatures low-enough to be compatible with back end of line (BEOL) processing in Silicon fabs. I will then switch gears to discuss memory devices from 2D materials when integrated with emerging wurtzite structure ferroelectric nitride materials4 namely aluminium scandium nitride (AlScN). First\, I will present on Ferroelectric Field Effect Transistors (FE-FETs) made from 2D materials when integrated with AlScN and make the case for 2D semiconductors in this application.5-7 Finally\, I will end with showing our most recent results on scaling 2D/AlScN FE-FETs\, achieving ultra-high carrier and current densities8 in ferroelectrically gated MoS2 and also demonstrate negative-capacitance FETs9 by engineering the AlScN/dielectric/2D interface. \nSpeaker\nProf. Deep Jariwala\nAssociate Professor\nPeter and Susanne Armstrong Distinguished Scholar\nElectrical and Systems Engineering Primary\nMaterials Science and Engineering\nThe University of Pennsylvania \nBiography of the Speaker\nDeep Jariwala is an Associate Professor and the Peter & Susanne Armstrong Distinguished Scholar in the Electrical and Systems Engineering as well as Materials Science and Engineering at the University of Pennsylvania (Penn). Deep completed his undergraduate degree in Metallurgical Engineering from the Indian Institute of Technology in Varanasi and his Ph.D. in Materials Science and Engineering at Northwestern University. Deep was a Resnick Prize Postdoctoral Fellow at Caltech before joining Penn to start his own research group. His research interests broadly lie at the intersection of new materials\, surface science and solid-state devices for computing\, opto-electronics and energy harvesting applications in addition to the development of correlated and functional imaging techniques. Deep’s research has been widely recognized with several awards from professional societies\, funding bodies\, industries as well as private foundations\, the most notable ones being the Optica Adolph Lomb Medal\, the Bell Labs Prize\, the AVS Peter Mark Memorial Award\, IEEE Photonics Society Young Investigator Award\, IEEE Nanotechnology Council Young Investigator Award\, IUPAP Early Career Scientist Prize in Semiconductors and the Alfred P. Sloan Fellowship. He has published over 150 journal papers with more than 21000 citations and holds several patents. He serves as the Associate Editor for ACS Nano Letters and has been appointed as a Distinguished Lecturer for the IEEE Nanotechnology Council for 2025. \nOrganisers\nProfessor Han Wang\, Department of Electrical and Electronic Engineering\, HKU\nIEEE ED/SSC Hong Kong Joint Chapter \nAll are welcome!
URL:https://ece.hku.hk/events/20250312-3/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250312T110000
DTEND;TZID=Asia/Hong_Kong:20250312T120000
DTSTAMP:20260512T022506
CREATED:20250304T094302Z
LAST-MODIFIED:20250304T094302Z
UID:110564-1741777200-1741780800@ece.hku.hk
SUMMARY:RPG Seminar – Advancing Implicit Neural Representations for Efficiency and Robustness on Hardware
DESCRIPTION:This RPg seminar will be conducted in hybrid mode via Zoom link and in venue CB-601J. \nZoom Link: https://hku.zoom.us/j/9174230511 \nAbstract\nThis seminar presents advances in hardware-efficient Implicit Neural Representations (INRs)\, addressing the significant challenges in deploying these continuous signal representations on resource-constrained platforms. Our research spans three distinct hardware environments\, each with tailored solutions. For FPGAs\, we introduce Distribution-Aware Hadamard Quantization and QuadINR with hardware-friendly quadratic activations\, demonstrating substantial efficiency gains without compromising quality. For emerging compute-in-memory (CIM) architectures\, we present novel robustness techniques—gradient-based regularization and low-rank constraints—that maintain representation fidelity despite hardware-induced perturbations. For GPU acceleration\, we showcase training innovations including progressive resolution training and frequency-aware sampling that reduce training time. \nSpeaker\nMr. Zhou Wenyong\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nBiography of the Speaker\nWenyong Zhou received the B.E. degree in Electronic Science and Engineering from Tianjin University\, Tianjin\, China\, in 2019 and M.S. degree in Computer Engineering from Northwestern University\, Evanston\, US\, in 2021. He is currently pursuing the Ph.D. degree with the Department of Electrical and Electronic Engineering\, The University of Hong Kong\, Hong Kong. His current research interests include implicit neural representation and efficient model design. \nOrganiser\nProf. Ngai Wong \nAll are welcome.
URL:https://ece.hku.hk/events/20250312-1/
LOCATION:Room CB-601J\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250312T150000
DTEND;TZID=Asia/Hong_Kong:20250312T160000
DTSTAMP:20260512T022506
CREATED:20250310T012708Z
LAST-MODIFIED:20250310T012708Z
UID:110579-1741791600-1741795200@ece.hku.hk
SUMMARY:RPG Seminar – Personalized Video Fragment Recommendation
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/96780307210?pwd=1wIMCFlBwNOSDiVMsEFxnVgzm5kLnn.1\nMeeting ID: 967 8030 7210\nPassword: 943198 \nAbstract\nIn the current digital landscape\, capturing user attention for long video content is increasingly challenging due to diminishing attention spans. While video production is shifting towards shorter formats\, there remains significant value in leveraging the huge volume of extant long videos to meet fast-paced user consumption habits. Addressing this challenge\, our talk presents a novel framework designed to recommend specific video fragments from long videos that align with individual user profiles. Our approach is based on three key insights: the inter-fragment contextual effect\, which leverages the complex relationships among fragments within a long video; the intra-fragment contextual effect\, which models herding effects from multi-modal signals in a single video fragment; and video-level preferences\, which ensure consistency with users’ overarching interests. Extensive experiments demonstrate that our model achieves superior performance across multiple key metrics\, outperforming state-of-the-art methods. \nSpeaker\nMr. Wang Jiaqi\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nBiography of the Speaker\nJiaqi Wang received the B.E. degree in Computer Science and Technology from South China University of Technology. He is currently pursuing the Ph.D. degree with the Department of Electrical and Electronic Engineering\, The University of Hong Kong\, Hong Kong. His current research interests include recommendation systems and deep learning. \nOrganiser\nProf. Edith Ngai \nAll are welcome.
URL:https://ece.hku.hk/events/20250312-2/
LOCATION:Online via Zoom
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250313T100000
DTEND;TZID=Asia/Hong_Kong:20250313T113000
DTSTAMP:20260512T022506
CREATED:20250211T081812Z
LAST-MODIFIED:20250212T083236Z
UID:108702-1741860000-1741865400@ece.hku.hk
SUMMARY:In-memory\, Mixed Analog-digital Architectures for Energy-efficient Computing Applications
DESCRIPTION:Abstract\nThere is simultaneously an interest for more energy-efficient hardware in challenging applications\, as well as a drive to overhaul the von Neumann architecture toward more brain-like architectures. Compute-in-Memory (CIM) is one emerging paradigm addressing key memory bottlenecks such as bandwidth limitations\, access latency\, and high data movement energy in conventional computing platforms.\nIn part one\, we use this paradigm to accelerate the solving of challenging optimization problems. We describe our in-memory\, hardware approach built around modified Hopfield neural networks to accelerate problem classes such as Boolean satisfiability (3-SAT). In an algorithm-hardware co-design process\, we have developed three different architectures steadily improving on the state-of-the-art. We discuss issues encountered in mapping native optimization problems to physical hardware\, precision demands\, and matching mixed analog-digital blocks to the algorithmic needs. Quantitative performance comparisons to competing approaches in both mature and emerging technologies will be presented.\nIn the second part of the talk\, we extend CIM beyond matrix-dominant workloads\, exploring its potential for novel and powerful models like Kolmogorov-Arnold Networks (KAN). Our new work\, called “KA-CIM\,” provides hardware acceleration for KANs. KANs offer significant parameter reduction over traditional neural networks for AI+Science applications but relies on computationally expensive non-linear functions. We present an innovative memory-centric design that enables energy-efficient and flexible computation of non-linear functions central to KAN\, while efficiently executing KAN inference.\nWe also discuss analog CIM using commodity DRAM architectures. Unlike SRAM- and emerging memory-based CIM accelerators\, DRAM-based CIM offers both high memory capacity and technological maturity\, making it an attractive candidate for large-scale AI workloads. This portion of the talk will present both the opportunities and constraints of using commodity DRAM for CIM. A novel analog CIM architecture will be presented\, which mitigates several of these constraints and demonstrates how area- and power-intensive ADCs can be efficiently integrated within an area-optimized commodity DRAM design. Furthermore\, a key feature of this architecture is its Dual-Mode functionality\, enabling it to seamlessly operate as both conventional main memory and an accelerator. \nSpeakers\nProf. John Paul Strachan\nProfessor\, RWTH Aachen University\, Aachen\, Germany\nHead\, Peter Grünberg Institute (PGI-14)\, Forschungszentrum Jülich\, Jülich\, Germany\n\nDr. Chirag Sudarshan\nPostdoctoral Researcher\nPeter Grünberg Institute (PGI-14)\, Forschungszentrum Jülich\, Jülich\, Germany \nBiography of the Speakers\nProf. John Paul Strachan directs the Peter Grünberg Institute on Neuromorphic Compute Nodes (PGI-14) at Forschungszentrum Jülich and is a Professor at RWTH Aachen.  Previously he led the Emerging Accelerators team as a Distinguished Technologist at Hewlett Packard Labs\, HPE. His teams explore novel types of hardware accelerators using emerging device technologies\, with expertise spanning materials\, device physics\, circuits\, architectures\, benchmarking and building prototype systems. Their interests span applications in machine learning\, network security\, and optimization. John Paul has degrees in physics and electrical engineering from MIT and a PhD in applied physics from Stanford University. He has over 60 patents\, has authored or co-authored over 100 peer-reviewed papers\, and been the PI in many USG research grants. He has previously worked on nanomagnetic devices for memory for which he was awarded the Falicov Award from the American Vacuum Society\, and has developed sensing systems for precision agriculture in a company which he co-founded. He serves in professional societies including IEEE IEDM ExComm\, the Nanotechnology Council ExComm\, and past program chair and steering member of the International Conference on Rebooting Computing.\n\nDr. Chirag Sudarshan is currently a Postdoctoral Researcher at the Peter Grünberg Institute on Neuromorphic Compute Nodes (PGI-14)\, Forschungszentrum Jülich\, working under the supervision of Prof. John Paul Strachan. He received his Master’s degree (2017) and Ph.D. (2023) in Electrical Engineering from the University of Kaiserslautern-Landau\, Germany. During his Ph.D.\, he extensively worked on novel DRAM architectural designs and is now developing innovative compute-in-memory architectures with emerging memory technologies for neuromorphic applications. His contributions have been recognized with a special academic achievement award from the Department of Electrical and Computer Engineering and WIPOTEC GmbH following his master’s studies. He has authored or co-authored 23 publications\, filed six patents\, and currently serves as a reviewer for journals such as Results in Engineering and Micromachines. His research interests include compute-in-memory architectures\, neuromorphic computing\, emerging memory technologies\, and DRAM architectures. \nOrganiser\n\nProf. Can Li\, Department of Electrical and Electronic Engineering\, The University of Hong Kong; and\nCenter for Advanced Semiconductor and Integrated Circuits\n\nAll are welcome! 
URL:https://ece.hku.hk/events/20250313-2/
LOCATION:Lecture Theatre CB-A\, G/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250313T103000
DTEND;TZID=Asia/Hong_Kong:20250313T113000
DTSTAMP:20260512T022506
CREATED:20250102T023031Z
LAST-MODIFIED:20250211T042315Z
UID:19666-1741861800-1741865400@ece.hku.hk
SUMMARY:Quantum Technologies with Hexagonal Boron Nitride
DESCRIPTION:Abstract\nEngineering robust\, solid-state quantum systems is amongst the most pressing challenges to realise scalable quantum photonic circuitry. While several 3D systems (such as diamond or gallium arsenide) have been thoroughly studied\, solid state emitters in van der Waals (vdW) and two dimensional (2D) materials are still in their infancy. \nIn this presentation\, I will discuss the appeal of an emerging vdW crystal – hexagonal boron nitride (hBN). This unique system possesses a large bandgap of ~ 6 eV and can host single defects that can act as ultra-bright quantum light sources. In addition\, some of these defects exhibit spin dependent fluorescence that can be initialised and coherently manipulated. I will discuss in details various methodologies to engineer these defects and show their peculiar properties. Furthermore\, I will discuss how hBN crystals can be carefully sculpted into nanoscale photonic resonators to confine and guide light at the nanoscale. Taking advantage of the unique 2D nature of hBN\, I will also show promising avenues to integrate hBN emitters with silicon nitride photonic crystal cavities. \nAll in all\, hBN possesses all the vital constituents to become the leading platform for integrated quantum photonics. To this extent\, I will highlight the challenges and opportunities in engineering hBN quantum photonic devices and will frame it more broadly in the growing interest with 2D materials nanophotonics. \nSpeaker\nProf. Igor Aharonovich\nSchool of Mathematical and Physical Sciences\,\nFaculty of Science\,\nUniversity of Technology Sydney \nBiography of the Speaker\nIgor Aharonovich is an award-winning scientist working on cutting-edge research into quantum sources that are able to generate\, encode and distribute quantum information. A Professor in the School of Mathematical and Physical Sciences at UTS\, Igor investigates optically active defects in solids\, with the aim of identifying a new generation of ultra-bright solid state quantum emitters. He is a chief investigator at the ARC Centre of Excellence for Transformative Meta-Optical Materials (TMOS)\, and leads an international collaboration investigating the chemical structure of crystal imperfections\, or defects\, in the nanomaterial hexagonal boron nitride (hBN). \nIn 2016\, Igor and his team discovered the first quantum emitters in 2D materials that operate at room temperature based on defects in hBN. He has co-authored more than 200 peer-reviewed publications\, including one of the most cited reviews on diamond photonics. He has also written a road map for solid state single-photon sources. In 2019\, Igor co-founded the inaugural online photonics conference\, Photonics Online Meetup\, which attracted more than 1100 attendees from around the world\, and which was highlighted by top science outlets. The conference now runs twice a year. He has received several international awards including the Pawsey Medal (2017)\, the IEEE Photonics Young Investigator Award (2016) and in 2020 he was the recipient of the Kavli Foundation Early Career Lectureship in Materials Science from Materials Research Society. In 2021\, he became a Fellow of the Optical Society (OSA)\, and in 2024 elected as a fellow of SPIE.\nRead more about the speaker’s biography: https://profiles.uts.edu.au/Igor.Aharonovich \nOrganiser\nProf. Zhiqin Chu\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nSupported by\nTam Wing Fan Innovation Wing Two \nAll are welcome! \nDirection: https://innowings.engg.hku.hk/innowing2/visitors
URL:https://ece.hku.hk/events/20250313-1/
LOCATION:Tam Wing Fan Innovation Wing Two\, G/F\, Run Run Shaw Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250313T110000
DTEND;TZID=Asia/Hong_Kong:20250313T113000
DTSTAMP:20260512T022506
CREATED:20250312T065738Z
LAST-MODIFIED:20250312T073813Z
UID:110586-1741863600-1741865400@ece.hku.hk
SUMMARY:On The Interplay of T and R in VCM-based 1T1R Structures
DESCRIPTION:Abstract\nRedox-based resistive switching random access memory (ReRAM) which is frequently discussed as a promising non-volatile memory as well as a central element in novel neuromorphic computing applications\, is typically integrated in 1-transistor-1-resistor (1T1R) structures. While the access transistor is required as a selective device and acts as an effective current compliance during SET\, it may hinder the RESET operation due to its series resistance. We showed that this may lead to a rare endurance failure. Furthermore\, the RESET speed is affected by the voltage divider of transistor and ReRAM cell\, where the initial cell resistance\, the gate voltage and the transistor geometry (i.e.\, width over length ratio w/L) are crucial. For both\, the HRS and LRS\, we demonstrate that the operation point of the 1T1R voltage divider can be shifted between the linear and the saturation regime of the transistor transfer characteristics. \nSpeaker\nDr. Stefan Wiefels\nPGI-7\, Forschungszentrum Jülich \nBiography of the Speaker\nStefan Wiefels was born in Grevenbroich\, Germany. He received the M.Sc. degree in materials science and the Ph.D. degree in electrical engineering and information technology from RWTH Aachen University\, Aachen\, Germany\, in 2016 and 2021\, respectively. His current research group is centered around the electrical characterization of memristive devices\, reaching from Redox-based resistive switches (ReRAM) to phase change memory (PCM) and from single cells (1R)\, via 1-transistor-1-resistor (1T1R) structures to arrays and circuits. A general focus lies on the automation of measurement schemes to generate significant statistics. This allows for understanding the intrinsic variability of resistive switching devices and is crucial to identify rare failure mechanisms. Further\, variability aware algorithms to program memristive devices are developed. A general target is emulating neuromorphic functionalities using external DAC and ADC before they are integrated on chip. \nOrganisers\n– Can Li\, Department of Electrical and Electronic Engineering\, The University of Hong Kong\n– Center for Advanced Semiconductor and Integrated Circuit \nAll are welcome!
URL:https://ece.hku.hk/events/20250313-3/
LOCATION:Lecture Theatre CB-A\, G/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250320T163000
DTEND;TZID=Asia/Hong_Kong:20250320T173000
DTSTAMP:20260512T022506
CREATED:20250304T090135Z
LAST-MODIFIED:20250304T090135Z
UID:110560-1742488200-1742491800@ece.hku.hk
SUMMARY:Shant Reactive Power Compensation Technologies
DESCRIPTION:Abstract\nReactive power compensation plays a critical role in improving power quality\, enhancing voltage stability\, and optimizing the efficiency of electrical power systems. This presentation will first highlight the main applications of shunt reactive power compensators and provide an overview of key technologies\, including Static Var Compensators (SVC)\, and Static Synchronous Compensators (StatComs). Then the focus will shift to StatCom\, which is considered state-of-the-art technology with superior performance. However\, the widespread adoption of high-power StatComs is hindered by cost constraints\, partly due to the large capacitor banks required in conventional Cascaded H-Bridge (CHB) multilevel converters. The presentation will discuss research advancements to highlight\, pros and cons of operating a CHB StatCom with low capacitance values. \nSpeaker\nProf. Glen FARIVAR\nNanyang Technological University (NUT Singapore) \nBiography of the Speaker\nGlen Farivar received PhD in Electrical Engineering from the University of NSW Australia in 2016. He was a Senior Research Fellow at the Energy Research Institute at the Nanyang Technological University (ERI@N) and a Co-director of the Power Electronics and Application Research Lab at Nanyang Technological University\, Singapore. Since 2023\, he has held a lecturer position\, leading power electronics research\, at the Department of Electrical and Electronic Engineering\, University of Melbourne. He is also a co-founder of SciLeap\, a platform dedicated to promoting research integrity\, accessibility\, and openness. He is a Senior Member of IEEE and co-authored over 130 papers in the areas of high-power multilevel converters and renewable energy systems. \nOrganiser\nProf. Yi WANG\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20250320-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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END:VEVENT
END:VCALENDAR