Aurelia Butler

The expression “flawless from every angle” is commonly used to characterize a celebrity’s appearance. This doesn’t simply imply that they appear attractive from a specific viewpoint, but rather that their appeal remains consistent and appealing from various angles and perspectives. Recently, a research team from Pohang University of Science and Technology (POSTECH) has employed metasurface to fabricate angle-dependent holograms with multiple functions, capturing significant interest within the academic community.
A research team comprising Professor Junsuk Rho from the Department of Mechanical Engineering and the Department of Chemical Engineering and PhD candidate Joohoon Kim from the Department of Mechanical Engineering at the POSTECH created metasurface display technology. This technology allows holograms to display multiple images based on the observer’s viewing angle. The findings were recently published in Nano Letters, an international journal focusing on nanoscale research and applications.
Objects can appear distinct depending on the viewer’s position, a concept that can be harnessed in holographic technology to generate cinematic and realistic 3D holograms presenting different images based on the viewing angle. However, the current challenge lies in controlling light dispersion according to the angle, making the application of nano-optics in this context a complex endeavor.
The team addressed this challenge by leveraging metasurfaces, artificial nanostructures capable of precisely manipulating the characteristics of light. These metasurfaces are incredibly thin and lightweight, approximately one-hundredth the thickness of a human hair, making them promising for applications in miniaturized displays such as virtual and augmented reality devices. Through the use of metasurfaces, the team devised a system that controls light to convey only a specific phase of information at a given angle, resulting in diverse images based on the angle of incidence.
In their experiments, the team’s metasurface generated distinct 3D holographic images at angles of both +35 degrees and -35 degrees for left-circular polarization. Remarkably, the team achieved the production of different images for incident light by using a single metasurface, contingent on the specific polarization. Notably, the holographic display demonstrated an extensive viewing angle of 70 degrees (±35 degrees), enabling observers to perceive the three-dimensional hologram from various directions.
Professor Junsuk Rho who led the research explained, “We have successfully achieved an effective display from diverse angles.” He added, “We anticipate this technology will make significant contributions to the commercialization of technology in virtual and augmented reality displays, encrypted imaging, information storage, and other applications.”
The study was conducted with the support from the program of POSCO-POSTECH-RIST Convergence Research Center program, the STEAM Research Program of the National Research Foundation of Korea funded by the Ministry of Science and ICT, and the Alchemist fellowship of the Ministry of Trade, Industry and Energy. More

After a recent car crash, John Murray-Bruce wished he could have seen the other car coming. The crash reaffirmed the University of South Florida assistant professor of computer science and engineering’s mission to create a technology that could do just that: See around obstacles and ultimately expand one’s line of vision.
Using a single photograph, Murray-Bruce and his doctoral student, Robinson Czajkowski, created an algorithm that computes highly accurate, full-color three-dimensional reconstructions of areas behind obstacles — a concept that can not only help prevent car crashes, but help law enforcement experts in hostage situations, search-and-rescue and strategic military efforts.
“We’re turning ordinary surfaces into mirrors to reveal regions, objects and rooms that are outside our line of vision,” Murray-Bruce said. “We live in a 3D world, so obtaining a more complete 3D picture of a scenario can be critical in a number of situations and applications.”
As published in Nature Communications, Czajkowski and Murray-Bruce’s research is the first-of-its-kind to successfully reconstruct a hidden scene in 3D using an ordinary digital camera. The algorithm works by using information from the photo of faint shadows cast on nearby surfaces to create a high-quality reconstruction of the scene. While it is more technical for the average person, it could have broad applications.
“These shadows are all around us,” Czajkowski said. “The fact we can’t see them with our naked eye doesn’t mean they’re not there.”
The idea of seeing around obstacles has been a topic of science-fiction movies and books for decades. Murray-Bruce says this research takes significant strides in bringing that concept to life.
Prior to this work, researchers have only used ordinary cameras to create rough 2D reconstructions of small spaces. The most successful demonstrations of 3D imaging of hidden scene all required specialized, expensive equipment.

“Our work achieves a similar result using far less,” Czajkowski said. “You don’t need to spend a million dollars on equipment for this anymore.”
Czajkowski and Murray-Bruce expect it will be 10 to 20 years before the technology is robust enough to be adopted by law enforcement and car manufacturers. Right now, they plan to continue their research to further improve the technology’s speed and accuracy to expand its applications in the future, including self-driving cars to improve their safety and situational awareness.
“In just over a decade since the idea of seeing around corners emerged, there has been remarkable progress and there is accelerating interest and research activity in the area,” Murray-Bruce said. “This increased activity, along with access to better, more sensitive cameras and faster computing power form the basis for my optimism on how soon this technology will become practical for a wide range of scenarios.”
While the algorithm is still in the development phase, it is available for other researchers to test and reproduce in their own space. More

Fashioned from the same element found in sand and covered by intricate patterns, microchips power smartphones, augment appliances and aid the operation of cars and airplanes. Now, scientists at the U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL) are developing computer simulation codes that will outperform current simulation techniques and aid the production of microchips using plasma, the electrically charged state of matter also used in fusion research. These codes could help increase the efficiency of the manufacturing process and potentially stimulate the renaissance of the chip industry in the United States.
“Because devices with microchips are essential to our daily lives, how and where they are made is a matter of national security,” said Igor Kaganovich, a principal research physicist who leads the low-temperature modeling group at PPPL. “Robust and reliable simulation tools that can accurately predict plasma behavior and shorten the manufacturing and design cycle of silicon chips could help the U.S. regain a leadership role in this field and maintain it for decades.”
Picking up the pace
One PPPL research effort involves reducing the time computers need to simulate microchip plasma reactors. This innovation would help private industry use more complex and accurate simulations widely and aid their drive to lower microchip costs. “Companies would like to use simulations to improve their processes, but they typically are computationally expensive,” said Andrew Tasman Powis, co-author of the paper reporting the results in Physics of Plasmas and computational research associate at PPPL. “We are doing our best to counter this trend.”
Physicists usually want simulations to reproduce plasma as accurately as possible, generating virtual pictures that reveal the intricacies of plasma behavior with very fine details. That process requires algorithms, programs following a set of rules, that simulate plasma in very short time increments and in small volumes of space. The catch is that such detailed simulations require powerful computers running for days or weeks at a time. That time frame is too long and too expensive for companies that want to use the simulations to improve their microchip manufacturing processes.
The researchers delved into plasma physics history to find already developed algorithms that might be able to shorten the amount of time necessary to simulate microchip plasma. The researchers found suitable algorithms from the 1980s; when tested, the algorithms demonstrated a capability to model microchip plasma systems in much less time and with only a small reduction in accuracy.
In essence, the researchers found that they could get good simulations even though they were modeling plasma particles within larger spaces and using longer time increments. “This development is important because it could save companies both time and money,” said Haomin Sun, the study’s lead researcher and a former graduate student in Princeton University’s Program in Plasma Physics, based at PPPL. “That means that with the same amount of computational resources, you can create more simulations. More simulations not only allow you to find ways to improve manufacturing, but also to learn more physics in general. We can make more discoveries using our limited resources.”
Related research led by Powis reinforces this possibility. In a paper published in Physics of Plasmas, Powis confirms that computer codes can generate accurate models of plasma particles while using virtual “cells” or small volumes of space that exceed a standard measure in plasma physics known as the Debye length. This development means that the codes can in effect use fewer cells and reduce the need for computing time. “This is good news because reducing the number of cells could lower the computational cost of the simulation and therefore improve performance,” Powis said.

The algorithms can simulate so-called “capacitively coupled plasma reactors,” which create the plasma that engineers use to etch narrow channels in a wafer of silicon. These tiny passageways form the microcircuitry that allows the microchip to function. “We are interested in modeling this process so we can learn how to control the properties of the plasma, predict what they would be like in a new machine, and then predict the etching properties so we can improve the process,” Powis said.
The team plans to test the algorithms further by adding the effects of different kinds of wall and electrode materials. “We want to continue to build confidence in these algorithms so we can be sure the results are accurate,” Powis said.
Recognizing and overcoming inherent limits
Another research effort focuses on errors that can creep into plasma simulations because of the inherent limitations of the simulation methods themselves, which model smaller numbers of plasma particles than are present in real plasma.
“When you simulate plasma, you would ideally like to track every single particle and know where it is at all times,” said Sierra Jubin, graduate student in the Princeton Program in Plasma Physics and lead author of the paper reporting the results in Physics of Plasmas. “But we don’t have infinite computing power, so we can’t do that.”
To get around this difficulty, researchers design code to represent millions of particles as one giant particle. Doing so simplifies the computer’s task, but also amplifies the interactions of the virtual mega-particles. As a result, a change in the proportion of particles moving at one speed versus how many are moving at another — a process known as thermalization — happens more quickly than it does in nature. Essentially, the simulation does not match reality.

“This is a problem because if we don’t address this issue, we won’t be modeling the phenomena as they actually occur in the world,” Jubin said. “And if we want to know how many electrons are moving at a particular speed, generating ions or reactive chemical species that interact with the materials used to make microchips, we won’t be getting an accurate picture.”
To compensate for these computational errors, the researchers found that they could make the mega-particle volumes larger and less dense, muting their interactions and slowing down the changes in particle velocities. “In effect, these results put boundaries on what is possible in microchip plasma simulations, point out constraints that we have to consider, and put forth some solutions,” Jubin said.
Jubin’s findings reinforce the notion that current simulation techniques must be improved. Whether because codes used today require small volume sizes and time increments that together slow simulations or because they produce errors based on computational requirements, scientists need new solutions. “This is actually a paradigm shift in the field,” Kaganovich said, “and PPPL is leading the way.”
This research was supported by PPPL’s laboratory-directed research and development (LDRD) program. Computer calculations were performed at the National Energy Research Scientific Computing Center (NERSC), a DOE user facility located at Lawrence Berkeley National Laboratory, as well as the ANTYA high-powered computing facility at India’s Institute for Plasma Research. The team included researchers from Princeton University, the Swiss Plasma Center at the Ecole Polytechnique Federale de Lausanne, India’s Birla Institute of Technology and Science, India’s Homi Bhabha National Institute, the University of Alberta at Edmonton, Applied Materials, Inc., and China’s Sino-French Institute of Nuclear Engineering and Technology. More

Despite significant progress in developing AI systems that can understand the physical world like humans do, researchers have struggled with modelling a certain aspect of our visual system: the perception of light.
“Determining the influence of light in a given photograph is a bit like trying to separate the ingredients out of an already baked cake.” explains Chris Careaga, a PhD student in the Computational Photography Lab at SFU. The task requires undoing the complicated interactions between light and surfaces in a scene. This problem is referred to as intrinsic decomposition, and has been studied for nearly half a century.
In a new paper published in the journal ACM Transactions on Graphics, researchers in the Computational Photography Lab develop an AI approach to intrinsic decomposition that works on a wide range of images. Their method automatically separates an image into two layers: one with only lighting effects and one with the true colours of objects in the scene. “The main innovation behind our work is to create a system of neural networks that are individually tasked with easier problems. They work together to understand the illumination in a photograph,” Careaga adds.
Although intrinsic decomposition has been studied for decades, SFU’s new invention is the first in the field to accomplish this task for any HD image that a person might take with their camera. “By editing the lighting and colours separately, a whole range of applications that are reserved for CGI and VFX become possible for regular image editing,” says Dr. Ya??z Aksoy, who leads the Computational Photography Lab at SFU. “This physical understanding of light makes it an invaluable and accessible tool for content creators, photo editors, and post-production artists, as well as for new technologies such as augmented reality and spatial computing.”
The group has since extended their intrinsic decomposition approach, applying it to the problem of image compositing: “When you insert an object or person from one image into another, it’s usually obvious that it’s edited since the lighting and colours don’t match” explains Careaga. “Using our intrinsic decomposition technique, we can alter the lighting of the inserted object to make it appear more realistic in the new scene.” In addition to publishing a paper on this, presented at SIGGRAPH Asia last December, the group has also developed a computer interface that allows users to interactively edit the lighting of these “composited” images. S. Mahdi H. Miangoleh, a PhD student in Aksoy’s lab, also contributed to this work.
Aksoy and his team plan to extend their methods to video for use in film post-production, and further develop AI capabilities in terms of interactive illumination editing. They emphasize a creativity-driven approach to AI in film production, aiming to empower independent and low-budget productions. To better understand the challenges in these production settings, the group has developed a computational photography studio at the Simon Fraser University campus where they conduct research in an active production environment. They also produce videos explaining their work which you can check out here: More

A new study from SMU psychologist Sarah Kucker and colleagues reveals that passive video use among toddlers can negatively affect language development, but their caregiver’s motivations for exposing them to digital media could also lessen the impact.
Results show that children between the ages of 17 and 30 months spend an average of nearly two hours per day watching videos — a 100 percent increase from prior estimates gathered before the COVID pandemic. The research reveals a negative association between high levels of digital media watching and children’s vocabulary development.
Children exposed to videos by caregivers for their calming or “babysitting” benefits tended to use phrases and sentences with fewer words. However, the negative impact on language skills was mitigated when videos were used for educational purposes or to foster social connections — such as through video chats with family members.
“In those first couple years of life, language is one of the core components of development that we know media can impact,” said Kucker, assistant professor of psychology in SMU’s Dedman College of Humanities & Sciences. “There’s less research focused on toddlers using digital media than older ages, which is why we’re trying to understand better how digital media affects this age group and what type of screen time is beneficial and what is not.”
Published in the journal Acta Paediatrica, the study involved 302 caregivers of children between 17 and 30 months. Caregivers answered questions about their child’s words, sentences, and how much time they spend on different media activities each day. Those activities included video/TV, video games, video chat, and e-books, with caregivers explaining why they use each activity with their child. Print book reading was also compared.
Researchers looked at the amount of media use and the reasons provided by caregivers for their children’s media use. These factors were then compared to the children’s vocabulary and length using two or more words together (the mean length of utterance).
Kucker suggests that caregivers need to consider what kind of videos their children are watching (whether for learning or fun) and how they interact with toddlers watching videos. She acknowledges that parents often use digital media to occupy children while they complete tasks. Kucker recommends caregivers consider how much digital media they allow young children and if they can interact with them while using it.
The study’s findings underscore the need for parents, caregivers, and educators to be aware of the potential effects of digital media on language development in children 30 months and under. By understanding the types of digital media children are exposed to and the reasons behind its usage, appropriate measures can be taken to ensure more healthy language development.
Future research by Kucker and her colleagues will continue to explore the types of videos young children watch, how they use screens with others, and if young children watching digital media for two hours is the new normal and, if so, how that impacts language development.
Research team members included Rachel Barr, from Georgetown University and Lynn K. Perry, from the University of Miami. Research reported in this press release was supported by the Eunice Kennedy Shriver National Institute Of Child Health & Human Development of the National Institutes of Health under Award Number R15HD101841. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. More

A collaborative project led by Professor Zhiqin Chu, Professor Can Li and Professor Ngai Wong, at the Department of Electrical and Electronic Engineering of the University of Hong Kong (HKU) has made a breakthrough in enhancing the speed and resolution of widefield quantum sensing, leading to new opportunities in scientific research and practical applications.
By collaborating with scientists from Mainland China and Germany, the team has successfully developed a groundbreaking quantum sensing technology using a neuromorphic vision sensor, which is designed to mimic the human vision system. This sensor is capable of encoding changes in fluorescence intensity into spikes during optically detected magnetic resonance (ODMR) measurements. The key advantage of this approach is that it results in highly compressed data volumes and reduced latency, making the system more efficient than traditional methods. This breakthrough in quantum sensing holds potential for various applications in fields such as monitoring dynamic processes in biological systems.
The research paper has been published in the journal Advanced Science titled “Widefield Diamond Quantum Sensing with Neuromorphic Vision Sensors.”
“Researchers worldwide have spent much effort looking into ways to improve the measurement accuracy and spatiotemporal resolution of camera sensors. But a fundamental challenge remains: handling the massive amount of data in the form of image frames that need to be transferred from the camera sensors for further processing. This data transfer can significantly limit the temporal resolution, which is typically no more than 100 fps due to the use of frame-based image sensors. What we did was trying to overcome the bottleneck,” said Zhiyuan Du, the first author of the paper and PhD candidate at the Department of Electrical and Electronic Engineering
Du said his professor’s focus on quantum sensing had inspired him and other team members to break new ground in the area. He is also driven by a passion for integrating sensing and computing.
“The latest development provides new insights for high-precision and low-latency widefield quantum sensing, with possibilities for integration with emerging memory devices to realise more intelligent quantum sensors,” he added.
The team’s experiment with an off-the-shelf event camera demonstrated a 13× improvement in temporal resolution, with comparable precision in detecting ODMR resonance frequencies with the state-of-the-art highly specialized frame-based approach. The new technology was successfully deployed in monitoring dynamically modulated laser heating of gold nanoparticles coated on a diamond surface. “It would be difficult to perform the same task using existing approaches,” Du said.

Unlike traditional sensors that record the light intensity levels, neuromorphic vision sensors process the light intensity change into “spikes” similar to biological vision systems, leading to improved temporal resolution (≈µs) and dynamic range ( >120 dB). This approach is particularly effective in scenarios where image changes are infrequent, such as object tracking and autonomous vehicles, as it eliminates redundant static background signals.
“We anticipate that our successful demonstration of the proposed method will revolutionise widefield quantum sensing, significantly improving performance at an affordable cost,” said Professor Zhiqin Chu.
“This also brings closer the realisation of near-sensor processing with emerging memory-based electronic synapse devices,” said Professor Can Li.
“The technology’s potential for industrial use should be explored further, such as studying dynamic changes in currents in materials and identifying defects in microchips,” said Professor Ngai Wong. More

Synthesizing or studying certain materials in a laboratory setting often poses challenges due to safety concerns, impractical experimental conditions, or cost constraints. In response, scientists are increasingly turning to deep learning methods which involve developing and training machine learning models to recognize patterns and relationships in data that include information about material properties, compositions, and behaviors. Using deep learning, scientists can quickly make predictions about material properties based on the material’s composition, structure, and other relevant features, identify potential candidates for further investigation, and optimize synthesis conditions.
Now, in a study published on 1 February 2024 in the International Union of Crystallography Journal (IUCrJ), Professor Takashiro Akitsu, Assistant Professor Daisuke Nakane, and Mr. Yuji Takiguchi from Tokyo University of Science (TUS) have used deep learning to predict single-molecule magnets (SMMs) from a pool of 20,000 metal complexes. This innovative strategy streamlines the material discovery process by minimizing the need for lengthy experiments.
Single-molecule magnets (SMMs) are metal complexes that demonstrate magnetic relaxation behavior at the individual molecule level, where magnetic moments undergo changes or relaxation over time. These materials have potential applications in the development of high-density memory, quantum molecular spintronic devices, and quantum computing devices. SMMs are characterized by having a high effective energy barrier (Ueff) for the magnetic moment to flip. However, these values are typically in the range of tens to hundreds of Kelvins, making SMMs challenging to synthesize.
The researchers used deep-learning to identify the relationship between molecular structures and SMM behavior in metal complexes with salen-type ligands. These metal complexes were chosen as they can be easily synthesized by complexing aldehydes and amines with various 3d and 4f metals. For the dataset, the researchers worked extensively to screen 800 papers from 2011 to 2021, collecting information on the crystal structure and determining if these complexes exhibited SMM behavior. Additionally, they obtained 3D structural details of the molecules from the Cambridge Structural Database.
The molecular structure of the complexes was represented using voxels or 3D pixels, where each element was assigned a unique RGB value. Subsequently, these voxel representations served as input to a 3D Convolutional Neural Network model based on the ResNet architecture. This model was specifically designed to classify molecules as either SMMs or non-SMMs by analyzing their 3D molecular images.
When the model was trained on a dataset of crystal structures of metal complexes containing salen type complexes, it achieved a 70% accuracy rate in distinguishing between the two categories. When the model was tested on 20,000 crystal structures of metal complexes containing Schiff bases, it successfully discovered the metal complexes reported as single-molecule magnets. “This is the first report of deep learning on the molecular structures of SMMs,” says Prof. Akitsu.
Many of the predicted SMM structures involved multinuclear dysprosium complexes, known for their high Ueff values. While this method simplifies the SMM discovery process, it is important to note that the model’s predictions are solely based on training data and do not explicitly link chemical structures with their quantum chemical calculations, a preferred method in AI-assisted molecular design. Further experimental research is required to obtain the data of SMM behavior under uniform conditions.
However, this simplified approach has its advantages. It reduces the need for complex computational calculations and avoids the challenging task of simulating magnetism. Prof. Akitsu concludes: “Adopting such an approach can guide the design of innovative molecules, bringing about significant savings in time, resources, and costs in the development of functional materials.” More

New phased-array transmitter design overcomes common problems of CMOS technology in the 300 GHz band, as reported by scientists from Tokyo Tech. Thanks to its remarkable area efficiency, low power consumption, and high data rate, the proposed transmitter could pave the way to many technological applications in the 300 GHz band, including body and cell monitoring, radar, 6G wireless communications, and terahertz sensors.
Today, most frequencies above the 250 GHz mark remain unallocated. Accordingly, many researchers are developing 300 GHz transmitters/receivers to capitalize on the low atmospheric absorption at these frequencies, as well as the potential for extremely high data rates that comes with it.
However, high-frequency electromagnetic waves become weaker at a fast pace when travelling through free space. To combat this problem, transmitters must compensate by achieving a large effective radiated power. While some interesting solutions have been proposed over the past few years, no 300 GHz-band transmitter manufactured via conventional CMOS processes has simultaneously realized high output power and small chip size.
Now, a research team led by Professor Kenichi Okada from Tokyo Institute of Technology(Tokyo Tech) and NTT Corporation (Headquarters: Chiyoda-ku, Tokyo; President & CEO: Akira Shimada; “NTT”) have recently developed a 300 GHz-band transmitter that solves these issues through several key innovations. Their work will be presented in the 2024 IEEE International Solid-State Circuits Conference (ISSCC).
The proposed solution is a phased-array transmitter composed of 64 radiating elements, which are arranged in 16 integrated circuits with four antennas each. Since the elements are arranged in three dimensions by stacking printed circuit boards (PCBs), this transmitter supports 2D beam steering. Simply put, the transmitted power can be aimed both vertically and horizontally, allowing for fast beam steering and tracking receivers efficiently. Notably, the antennas used are Vivaldi antennas, which can be implemented directly on-chip and have a suitable shape and emission profile for high frequencies.
An important feature of the proposed transmitter is its power amplifier (PA)-last architecture. By placing the amplification stage right before the antennas, the system only needs to amplify signals that have already been conditioned and processed. This leads to higher efficiency and better amplifier performance.
The researchers also addressed a few common problems that arise with conventional transistor layouts in CMOS processes, namely high gate resistance and large parasitic capacitances. They optimized their layout by adding additional drain paths and vias and by altering the geometry and element placing between metal layers. “Compared to the standard transistor layout, the parasitic resistance and capacitances in the proposed transistor layout are all mitigated,” remarks Prof. Okada. “In turn, the transistor-gain corner frequency, which is the point where the transistor’s amplification starts to decrease at higher frequencies, was increased from 250 to 300 GHz.”
On top of these innovations, the team designed and implemented a multi-stage 300 GHz power amplifier to be used with each antenna. Thanks to excellent impedance matching between stages, the amplifiers demonstrated outstanding performance, as Prof. Okada highlights: “The proposed power amplifiers achieved a gain higher than 20 dB from 237 to 267 GHz, with a sharp cut-off frequency to suppress out-of-band undesired signals.” The proposed amplifier also achieves a noise figure of 15 dB which was evaluated by the noise measurement system in 300-GHz band.
The researchers tested their design through both simulations and experiments, obtaining very promising results. Remarkably, the proposed transmitter achieved a data rate of 108 Gb/s in on-PCB probe measurements, which is substantially higher than other state-of-the-art 300 GHz-band transmitters.
Moreover, the transmitter also displayed remarkable area efficiency compared to other CMOS-based designs alongside low power consumption, highlighting its potential for miniaturized and power-constrained applications. Some notable use cases are sixth-generation (6G) wireless communications, high-resolution terahertz sensors, and human body and cell monitoring. More