Our faculty members publish their works in prestigious research journals. Take a look at what we have been working on here.
Department Highlights
CityU scientist hunted down the first unpaired Weyl magnetic monopole
13 September 2021Similar to a magnet that always has both south and north poles, a kind of special quasiparticles in condensed matter called “Weyl Fermions” always appear in pairs with opposite chirality.
Celebration of Physics - PHY Annual Symposium 2021 (Online)
3 Aug 2021PHY Annual Symposium 2021 – Celebration of Physics was held via Zoom on 2 June 2021. This annual event has become an essential and integral part of our department’s tradition.
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College of Science Commencement for the Graduating Class of 2021
18 Aug 2021The College of Science Commencement for the graduating class of 2021 was held on 31 May 2021 at Wong Cheung Lo Hui Yuet Hall, 5/F, Lau Ming Wai Academic Building (LAU).
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Spectroscopy and Imaging
Quantum Materials
Spectroscopy and Imaging
Quantum Materials
Extreme Suppression of Antiferromagnetic Order and Critical Scaling in a Two-Dimensional Random Quantum Magnet
Extreme Suppression of Antiferromagnetic Order and Critical Scaling in a Two-Dimensional Random Quantum Magnet
Physical Review Letters, 126, 037201 (2021)
Sr2CuTeO6 is a square-lattice Néel antiferromagnet with superexchange between first-neighbor S=1/2 Cu spins mediated by plaquette centered Te ions. Substituting Te by W, the affected impurity plaquettes have predominantly second-neighbor interactions, thus causing local magnetic frustration. Our newly constructed cold neutron triple-axis spectrometer Xingzhi was used to investigate the extremely rapid suppression of the Néel order by the W induced magnetic frustration.
Read more at Physical Review Letters: https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.126.037201
19 Jan 2021
Atomic, Molecular, and Optical Physics
Applied Physics
Atomic, Molecular, and Optical Physics
Applied Physics
11 TOPS photonic convolutional accelerator for optical neural networks
11 TOPS photonic convolutional accelerator for optical neural networks
Nature, 589, 44–51 (2021)
Convolutional neural networks, inspired by biological visual cortex systems, are a powerful category of artificial neural networks that can extract the hierarchical features of raw data to provide greatly reduced parametric complexity and to enhance the accuracy of prediction. They are of great interest for machine learning tasks such as computer vision, speech recognition, playing board games and medical diagnosis. Optical neural networks offer the promise of dramatically accelerating computing speed using the broad optical bandwidths available. Here we demonstrate a universal optical vector convolutional accelerator operating at more than ten TOPS (trillions (1012) of operations per second, or tera-ops per second), generating convolutions of images with 250,000 pixels—sufficiently large for facial image recognition. We use the same hardware to sequentially form an optical convolutional neural network with ten output neurons, achieving successful recognition of handwritten digit images at 88 per cent accuracy. Our results are based on simultaneously interleaving temporal, wavelength and spatial dimensions enabled by an integrated microcomb source. This approach is scalable and trainable to much more complex networks for demanding applications such as autonomous vehicles and real-time video recognition.
Read more at Nature: https://www.nature.com/articles/s41586-020-03063-0
06 Jan 2021
Education
Our Programmes
We aim to equip our students with knowledge and critical thinking skills that benefit them in physical-science-related careers and beyond. Explore our programmes below!
