Computers Math

3D integrated circuits are a key part of improving the efficiency of electronics to meet the considerable demands of consumers. They are constantly being developed, but translating theoretical findings into actual devices is not easy. Now, a new design by a research team from Japan can turn these theories into reality.
In a study recently published for the VLSI Symposium 2023, researchers from Institute of Industrial Science, The University of Tokyo have reported a deposition process for nanosheet oxide semiconductor. The oxide semiconductor resulting from this process has high carrier mobility and reliability in transistors.
3D integrated circuits are made up of multiple layers that each play a role in the overall function. Oxide semiconductors are attracting a lot of attention as materials for various circuit components because they can be processed at low temperature, while still having high carrier mobility and low charge leakage, and are able to withstand high voltages.
There are also advantages to using oxides rather than metals in processes where electrodes may be exposed to oxygen during the integration process and become oxidized.
However, developing the processes needed to reliably deposit very thin layers of oxide semiconductor materials in the manufacture of devices is challenging and has not been fully established to date. Recently, the researchers have reported an atomic layer deposition (ALD) technique that produces layers appropriate for large-scale integration.
“Using our process, we carried out a systematic study of field effect transistors (FETs) to establish their limitations and optimize their properties,” explains lead author of the study, Kaito Hikake. FETs control the current flow in a semiconductor. “We tuned the ratio of the components and adjusted the preparation conditions and our findings led to the development of a multi-gate nanosheet FET for normally-off operation and high reliability.”
The findings revealed that a FET made from the chosen oxide semiconductor by ALD had the best performance. The multi-gate nanosheet FET is believed to be the first to combine high carrier mobility and reliability characteristics with normally-off operation.
“In rapidly moving areas such as electronics, it is important to translate proof of concept findings into industrially relevant processes,” says Masaharu Kobayashi, senior author. “We believe that our study provides a robust technique that can be used to produce devices that meet the market’s need for manufacturable 3D integrated circuits with high function.”
The findings in this study have provided a solution to one of the big obstacles in the manufacturing of electronic devices with semiconductors. Hopefully, this will bring more designs of electronics with high functionality to actual products. More

A group of researchers from Tohoku University has unveiled a new material that exhibits enormous magnetoresistance, paving the way for developments in non-volatile magnetoresistive memory (MRAM).
Details of their unique discovery were published in the Journal of Alloys and Compounds on May 29, 2023.
Today, the demand for advancements in hardware that can efficiently process large amounts of digital information and in sensors has never been greater, especially with governments deploying technological innovations to achieve smarter societies.
Much of this hardware and sensors rely on MRAM and magnetic sensors, and tunnel magnetoresistive devices make up the majority of such devices.
Tunnel magnetoresistive devices exploit the tunnel magnetoresistance effect to detect and measure magnetic fields. This is tied to the magnetization of ferromagnetic layers in magnetic tunnel junctions. When the magnets are aligned, a low resistance state is observed, and electrons can easily tunnel through the thin insulating barrier between them. When the magnets are not aligned, the tunneling of electrons becomes less efficient and leads to higher resistance. This change in resistance is expressed as the magnetoresistive ratio, a key figure in determining the efficiency of tunneling magnetoresistive devices. The higher the magnetoresistance ratio, the better the device is.
Current tunnel magnetoresistive devices comprise magnesium oxide and iron-based magnetic alloys, like iron-cobalt. Iron-based alloys have a body-centered cubic crystal structure in ambient conditions and exhibit a huge tunnel magnetoresistance effect in devices with a rock salt-type magnesium oxide.

There have been two notable studies using these iron-based alloys that produced magnetoresistive devices displaying high magnetoresistance ratios. The first in 2004 was by the National Institute of Advanced Industrial Science and Technology in Japan and IBM; and the second came in 2008, when researchers from Tohoku University reported on a magnetoresistance ratio exceeding 600% at room temperature, something that jumped to 1000% with temperatures near zero kelvin.
Since those breakthroughs, various institutes and companies have invested considerable effort in honing device physics, materials, and processes. Yet aside from iron-based alloys, only some Heusler-type ordered magnetic alloys have displayed such enormous magnetoresistance.
Dr. Tomohiro Ichinose and Professor Shigemi Mizukami from Tohoku University recently began exploring thermodynamically metastable materials to develop a new material capable of demonstrating similar magnetoresistance ratios. To do so, they focused on the strong magnetic properties of cobalt-manganese alloys, which have a body-centered cubic metastable crystal structure.
“Cobalt-manganese alloys have face-centered cubic or hexagonal crystal structures as thermodynamically stable phases. Because this stable phase exhibits weak magnetism, it has never been studied as a practical material for tunnel magnetoresistive devices,” said Mizukami.
Back in 2020, the group reported on a device that used a cobalt-manganese alloy with metastable body-centered cubic crystal structure.
Using data science and/or high-throughput experimental methods, they built upon this discovery, and succeeded in obtaining huge magnetoresistance in devices by adding a small amount of iron to the metastable body-centered cubic cobalt-manganese alloy. The magnetoresistance ratio was 350% at room temperature and also exceeded 1000% at a low temperature. Additionally, the device fabrication employed the sputtering method and a heating process, something compatible with current industries.
“We have produced the third instance of a new magnetic alloy for tunneling magnetoresistive devices showing huge magnetoresistance, and it sets an alternative direction of travel for future improvements,” adds Mizukami. More

The patterns of light hold tremendous promise for a large encoding alphabet in optical communications, but progress is hindered by their susceptibility to distortion, such as in atmospheric turbulence or in bent optical fibre.  Now researchers at the University of the Witwatersrand (Wits) have outlined a new optical communication protocol that exploits spatial patterns of light for multi-dimensional encoding in a manner that does not require the patterns to be recognised, thus overcoming the prior limitation of modal distortion in noisy channels.  The result is a new encoding state-of-the-art of over 50 vectorial patterns of light sent virtually noise-free across a turbulent atmosphere, opening a new approach to high-bit-rate optical communication.  Published this week in Laser & Photonics Reviews, the Wits team from the Structured Light Laboratory in the Wits School of Physics used a new invariant property of vectorial light to encode information.  This quantity, which the team call “vectorness”, scales from 0 to 1 and remains unchanged when passing through a noisy channel.  Unlike traditional amplitude modulation which is 0 or 1 (only a two-letter alphabet), the team used the invariance to partition the 0 to 1 vectorness range into more than 50 parts (0, 0.02, 0.04 and so on up to 1) for a 50-letter alphabet.  Because the channel over which the information is sent does not distort the vectorness, both sender and received will always agree on the value, hence noise-free information transfer.  The critical hurdle that the team overcame is to use patterns of light in a manner that does not require them to be “recognised”, so that the natural distortion of noisy channels can be ignored.  Instead, the invariant quantity just “adds up” light in specialised measurements, revealing a quantity that doesn’t see the distortion at all.“This is a very exciting advance because we can finally exploit the many patterns of light as an encoding alphabet without worrying about how noisy the channel is,” says Professor Andrew Forbes, from the Wits School of Physics. “In fact, the only limit to how big the alphabet can be is how good the detectors are and not at all influenced by the noise of the channel.”Lead author and PhD candidate Keshaan Singh adds: “To create and detect the vectorness modulation requires nothing more than conventional communications technology, allowing our modal (pattern) based protocol to be deployed immediately in real-world settings.”The team have already started demonstrations in optical fibre and in fast links across free-space, and believe that the approach can work in other noisy channels, including underwater. More

A central difficulty in controlling greenhouse gas emissions to slow down climate change is finding them in the first place.
Such is the case with methane, a colorless, odorless gas that is the second most abundant greenhouse gas in the atmosphere today, after carbon dioxide. Although it has a shorter life than carbon dioxide, according to the U.S. Environmental Protection Agency, it’s more than 25 times as potent as CO2 at trapping heat, and is estimated to trap 80 times more heat in the atmosphere than CO2 over 20 years.
For that reason, curbing methane has become a priority, said UC Santa Barbara researcher Satish Kumar, a doctoral student in the Vision Research Lab of computer scientist B.S. Manjunath.
“Recently, at the 2022 International Climate Summit, methane was actually the highlight because everybody is struggling with it,” he said.
Even with reporting requirements in the U.S., methane’s invisibility means that its emissions are likely going underreported. In some cases the discrepancies are vast, such as with the Permian Basin, an 86,000-square-mile oil and natural gas extraction field located in Texas and New Mexico that hosts tens of thousands of wells. Independent methane monitoring of the area has revealed that the site emits eight to 10 times more methane than reported by the field’s operators.
In the wake of the COP27 meetings, the U.S. government is now seeking ways to tighten controls over these types of “super emitting” leaks, especially as oil and gas production is expected to increase in the country in the near future. To do so, however, there must be a way of gathering reliable fugitive emissions data in order to assess the oil and gas operators’ performance and levy appropriate penalties as needed.

Enter MethaneMapper, an artificial intelligence-powered hyperspectral imaging tool that Kumar and colleagues have developed to detect real-time methane emissions and trace them to their sources. The tool works by processing hyperspectral data gathered during overhead, airborne scans of the target area.
“We have 432 channels,” Kumar said. Using survey images from NASA’s Jet Propulsion Laboratory, the researchers take pictures starting from 400 nanometer wavelengths, and at intervals up to 2,500 nanometers — a range that encompasses the spectral signatures of hydrocarbons, including that of methane. Each pixel in the photograph contains a spectrum and represents a range of wavelengths called a “spectral band.” From there, machine learning takes on the huge amount of data to differentiate methane from other hydrocarbons captured in the imaging process. The method also allows users to see not just the magnitude of the plume, but also its source.
Hyperspectral imaging for methane detection is a hot field, with companies jumping into the fray with equipment and detection systems. What makes MethaneMapper stand out is the diversity and depth of data collected from various types of terrain that allows the machine learning model to pick out the presence of methane against a backdrop of different topographies, foliage and other backgrounds.
“A very common problem with the remote sensing community is that whatever is designed for one place won’t work outside that place,” Kumar explained. Thus, a remote sensing program will often learn what methane looks like against a certain landscape — say, the dry desert of the American Southwest — but pit it against the rocky shale of Colorado or the flat expanses of the Midwest, and the system might not be as successful.
“We curated our own data sets, which cover approximately 4,000 emissions sites,” Kumar said. “We have the dry states of California, Texas and Arizona. But we have the dense vegetation of the state of Virginia too. So it’s pretty diverse.” According to him, MethaneMapper’s performance accuracy currently stands at 91%.

The current operating version of MethaneMapper relies on airplanes for the scanning component of the system. But the researchers are setting some ambitious sights for a satellite-enabled program, which has the potential to scan wider swaths of terrain repeatedly, without the greenhouse gasses that airplanes emit. The major tradeoff between using planes and using satellites is in the resolution, Kumar said.
“You can detect emissions as small as 50 kg per hour from an airplane,” he said. With a satellite, the threshold increases to about 1000 kg or 1 ton per hour. But for the purpose of monitoring emissions from oil and gas operations, which tend to emit in the thousands of kilograms per hour, it’s a small price to pay for the ability to scan larger parts of the Earth, and in places that might not be on the radar, so to speak.
“The most recent case, I think seven or eight months ago, were emissions from an oil rig off the coast somewhere toward Mexico,” Kumar said, “which was emitting methane at a rate of 7,610 kilograms per hour for six months. And nobody knew about it.
“And methane is so dangerous,” he continued. “The amount of damage that carbon dioxide will do in a hundred years, methane can do in only 1.2 years.” Satellite detection could not only track carbon emissions on the global scale, it can also be used to direct subsequent airplane-based scans for higher-resolution investigations.
Ultimately, Kumar and colleagues want to bring the power of AI and hyperspectral methane imaging to the mainstream, making it available to a wide variety of users even without expertise in machine learning.
“What we want to provide is an interface through a web platform such as BisQue, where anyone can click and upload their data and it can generate an analysis,” he said. “I want to provide a simple and effective interface that anyone can use.”
The MethaneMapper project is funded by National Science Foundation award SI2-SSI #1664172. The project is part of the Center for Multimodal Big Data Science and Healthcare initiative at UC Santa Barbara, led by Prof. B.S. Manjunath. Additionally, MethaneMapper will be featured as a Highlight Paper at the 2023 Computer Vision and Pattern Recognition (CVPR) Conference — the premiere event in the computer vision field — to be held June 18-22 in Vancouver, British Columbia. More

Quantum computing uses the principles of quantum mechanics to encode and elaborate data, meaning that it could one day solve computational problems that are intractable with current computers. While the latter work with bits, which represent either a 0 or a 1, quantum computers use quantum bits, or qubits — the fundamental units of quantum information.
“With applications ranging from drug discovery to optimization and simulations of complex biological systems and materials, quantum computing has the potential to reshape vast areas of science, industry, and society,” says Professor Vincenzo Savona, director of the Center for Quantum Science and Engineering at EPFL.
Unlike classical bits, qubits can exist in a “superposition” of both 0 and 1 states at the same time. This allows quantum computers to explore multiple solutions simultaneously, which could make them significantly faster in certain computational tasks. However, quantum systems are delicate and susceptible to errors caused by interactions with their environment.
“Developing strategies to either protect or qubits from this or to detect and correct errors once they have occurred is crucial for enabling the development of large-scale, fault-tolerant quantum computers,” says Savona. Together with EPFL physicists Luca Gravina, and Fabrizio Minganti, they have made a significant breakthrough by proposing a “critical Schrödinger cat code” for advanced resilience to errors. The study introduces a novel encoding scheme that could revolutionize the reliability of quantum computers.
What is a “critical Schrödinger cat code”?
In 1935, physicist Erwin Schrödinger proposed a thought experiment as a critique of the prevailing understanding of quantum mechanics at the time — the Copenhagen interpretation. In Schrödinger’s experiment, a cat is placed in a sealed box with a flask of poison and a radioactive source. If a single atom of the radioactive source decays, the radioactivity is detected by a Geiger counter, which then shatters the flask. The poison is released, killing the cat.
According to the Copenhagen view of quantum mechanics, if the atom is initially in superposition, the cat will inherit the same state and find itself in a superposition of alive and dead. “This state represents exactly the notion of a quantum bit, realized at the macroscopic scale,” says Savona.
In past years, scientists have drawn inspiration by Schrödinger’s cat to build an encoding technique called “Schrödinger’s cat code.” Here, the 0 and 1 states of the qubit are encoded onto two opposite phases of an oscillating electromagnetic field in a resonant cavity, similarly to the dead or alive states of the cat.
“Schrödinger cat codes have been realized in the past using two distinct approaches,” explains Savona. “One leverages anharmonic effects in the cavity, the other relying on carefully engineered cavity losses. In our work, we bridged the two by operating in an intermediate regime, combining the best of both worlds. Although previously believed to be unfruitful, this hybrid regime results in enhanced error suppression capabilities.” The core idea is to operate close to the critical point of a phase transition, which is what the ‘critical’ part of the critical cat code refers to.
The critical cat code has an additional advantage: it exhibits exceptional resistance to errors that result from random frequency shifts, which often pose significant challenges to operations involving multiple qubits. This solves a major problem and paves the way to the realization of devices with several mutually interacting qubits — the minimal requirement for building a quantum computer.
“We are taming the quantum cat,” says Savona. “By operating in a hybrid regime, we have developed a system that surpasses its predecessors, which represents a significant leap forward for cat qubits and quantum computing as a whole. The study is a milestone on the road towards building better quantum computers, and showcases EPFL’s dedication in advancing the field of quantum science and unlocking the true potential of quantum technologies. More

Poems, essays and even books — is there anything the open AI platform ChatGPT can’t handle? These new AI developments have inspired researchers at TU Delft and the Swiss technical university EPFL to dig a little deeper: For instance, can ChatGPT also design a robot? And is this a good thing for the design process, or are there risks? The researchers published their findings in Nature Machine Intelligence.
What are the greatest future challenges for humanity? This was the first question that Cosimo Della Santina, assistant professor, and PhD student Francesco Stella, both from TU Delft, and Josie Hughes from EPFL, asked ChatGPT. “We wanted ChatGPT to design not just a robot, but one that is actually useful,” says Della Santina. In the end, they chose food supply as their challenge, and as they chatted with ChatGPT, they came up with the idea of creating a tomato-harvesting robot.
Helpful suggestions
The researchers followed all of ChatGPT’s design decisions. The input proved particularly valuable in the conceptual phase, according to Stella. “ChatGPT extends the designer’s knowledge to other areas of expertise. For example, the chat robot taught us which crop would be most economically valuable to automate.” But ChatGPT also came up with useful suggestions during the implementation phase: “Make the gripper out of silicone or rubber to avoid crushing tomatoes” and “a Dynamixel motor is the best way to drive the robot.” The result of this partnership between humans and AI is a robotic arm that can harvest tomatoes.
ChatGPT as a researcher
The researchers found the collaborative design process to be positive and enriching. “However, we did find that our role as engineers shifted towards performing more technical tasks,” says Stella. In Nature Machine Intelligence, the researchers explore the varying degrees of cooperation between humans and Large Language Models (LLM), of which ChatGPT is one. In the most extreme scenario, AI provides all the input to the robot design, and the human blindly follows it. In this case, the LLM acts as the researcher and engineer, while the human acts as the manager, in charge of specifying the design objectives.
Risk of misinformation
Such an extreme scenario is not yet possible with today’s LLMs. And the question is whether it is desirable. “In fact, LLM output can be misleading if it is not verified or validated. AI bots are designed to generate the ‘most probable’ answer to a question, so there is a risk of misinformation and bias in the robotic field,” Della Santina says. Working with LLMs also raises other important issues, such as plagiarism, traceability and intellectual property.
Della Santina, Stella and Hughes will continue to use the tomato-harvesting robot in their research on robotics. They are also continuing their study of LLMs to design new robots. Specifically, they are looking at the autonomy of AIs in designing their own bodies. “Ultimately an open question for the future of our field is how LLMs can be used to assist robot developers without limiting the creativity and innovation needed for robotics to rise to the challenges of the 21st century,” Stella concludes. More

Significantly improved electric vehicle (EV) batteries could be a step closer thanks to a new study led by University of Oxford researchers, published today in Nature. Using advanced imaging techniques, this revealed mechanisms which cause lithium metal solid-state batteries (Li-SSBs) to fail. If these can be overcome, solid-state batteries using lithium metal anodes could deliver a step-change improvement in EV battery range, safety and performance, and help advance electrically powered aviation.
One of the co-lead authors of the study Dominic Melvin, a PhD student in the University of Oxford’s Department of Materials, said: ‘Progressing solid-state batteries with lithium metal anodes is one of the most important challenges facing the advancement of battery technologies. While lithium-ion batteries of today will continue to improve, research into solid-state batteries has the potential to be high-reward and a gamechanger technology.’
Li-SSBs are distinct from other batteries because they replace the flammable liquid electrolyte in conventional batteries with a solid electrolyte and use lithium metal as the anode (negative electrode). The use of the solid electrolyte improves the safety, and the use of lithium metal means more energy can be stored. A critical challenge with Li-SSBs, however, is that they are prone to short circuit when charging due to the growth of ‘dendrites’: filaments of lithium metal that crack through the ceramic electrolyte. As part of the Faraday Institution’s SOLBAT project, researchers from the University of Oxford’s Departments of Materials, Chemistry and Engineering Science, have led a series of in-depth investigations to understand more about how this short-circuiting happens.
In this latest study, the group used an advanced imaging technique called X-ray computed tomography at Diamond Light Source to visualise dendrite failure in unprecedented detail during the charging process. The new imaging study revealed that the initiation and propagation of the dendrite cracks are separate processes, driven by distinct underlying mechanisms. Dendrite cracks initiate when lithium accumulates in sub-surface pores. When the pores become full, further charging of the battery increases the pressure, leading to cracking. In contrast, propagation occurs with lithium only partially filling the crack, through a wedge-opening mechanism which drives the crack open from the rear.
This new understanding points the way forward to overcoming the technological challenges of Li-SSBs. Dominic Melvin said: ‘For instance, while pressure at the lithium anode can be good to avoid gaps developing at the interface with the solid electrolyte on discharge, our results demonstrate that too much pressure can be detrimental, making dendrite propagation and short-circuit on charging more likely.’
Sir Peter Bruce, Wolfson Chair, Professor of Materials at the University of Oxford, Chief Scientist of the Faraday Institution, and corresponding author of the study, said: ‘The process by which a soft metal such as lithium can penetrate a highly dense hard ceramic electrolyte has proved challenging to understand with many important contributions by excellent scientists around the world. We hope the additional insights we have gained will help the progress of solid-state battery research towards a practical device.’
According to a recent report by the Faraday Institution, SSBs may satisfy 50% of global demand for batteries in consumer electronics, 30% in transportation, and over 10% in aircraft by 2040.
Professor Pam Thomas, CEO, Faraday Institution, said: ‘SOLBAT researchers continue to develop a mechanistic understanding of solid-state battery failure — one hurdle that needs to be overcome before high-power batteries with commercially relevant performance could be realised for automotive applications. The project is informing strategies that cell manufacturers might use to avoid cell failure for this technology. This application-inspired research is a prime example of the type of scientific advances that the Faraday Institution was set up to drive.’ More

The debut of artificial intelligence chatbot ChatGPT has set the world abuzz with its ability to churn out human-like text and conversations. Still, many telltale signs can help us distinguish AI chatbots from humans, according to a study published on June 7 in the journal Cell Reports Physical Science. Based on the signs, the researchers developed a tool to identify AI-generated academic science writing with over 99% accuracy.
“We tried hard to create an accessible method so that with little guidance, even high school students could build an AI detector for different types of writing,” says first author Heather Desaire, a professor at the University of Kansas. “There is a need to address AI writing, and people don’t need a computer science degree to contribute to this field.”
“Right now, there are some pretty glaring problems with AI writing,” says Desaire. “One of the biggest problems is that it assembles text from many sources and there isn’t any kind of accuracy check — it’s kind of like the game Two Truths and a Lie.”
Although many AI text detectors are available online and perform fairly well, they weren’t built specifically for academic writing. To fill the gap, the team aimed to build a tool with better performance precisely for this purpose. They focused on a type of article called perspectives, which provide an overview of specific research topics written by scientists. The team selected 64 perspectives and created 128 ChatGPT-generated articles on the same research topics to train the model. When they compared the articles, they found an indicator of AI writing — predictability.
Contrary to AI, humans have more complex paragraph structures, varying in the number of sentences and total words per paragraph, as well as fluctuating sentence length. Preferences in punctuation marks and vocabulary are also a giveaway. For example, scientists gravitate towards words like “however,” “but” and “although,” while ChatGPT often uses “others” and “researchers” in writing. The team tallied 20 characteristics for the model to look out for.
When tested, the model aced a 100% accuracy rate at weeding out AI-generated full perspective articles from those written by humans. For identifying individual paragraphs within the article, the model had an accuracy rate of 92%. The research team’s model also outperformed an available AI text detector on the market by a wide margin on similar tests.
Next, the team plans to determine the scope of the model’s applicability. They want to test it on more extensive datasets and across different types of academic science writing. As AI chatbots advance and become more sophisticated, the researchers also want to know if their model will stand.
“The first thing people want to know when they hear about the research is ‘Can I use this to tell if my students actually wrote their paper?'” said Desaire. While the model is highly skilled at distinguishing between AI and scientists, Desaire says it was not designed to catch AI-generated student essays for educators. However, she notes that people can easily replicate their methods to build models for their own purposes. More