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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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TZOFFSETFROM:+0800
TZOFFSETTO:+0800
TZNAME:HKT
DTSTART:20240101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250312T100000
DTEND;TZID=Asia/Hong_Kong:20250312T110000
DTSTAMP:20260512T023523
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
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2025/03/123213.jpg
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250312T110000
DTEND;TZID=Asia/Hong_Kong:20250312T120000
DTSTAMP:20260512T023523
CREATED:20250304T094302Z
LAST-MODIFIED:20250304T094302Z
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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
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250312T150000
DTEND;TZID=Asia/Hong_Kong:20250312T160000
DTSTAMP:20260512T023523
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
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
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