̽��ֱ�� of Cambridge - Cambridge Graphene Centre

̽��ֱ�� spin-out secures funding to improve AI energy efficiency and bandwidth

Anonymous — Mon, 24 Mar 2025 14:24:15 +0000

CamGraPhIC - co-founded Professor Andrea Ferrari, Director of the Cambridge Graphene Centre, and Dr Marco Romagnoli of CNIT in Italy - is developing new types of photonic circuits for energy-efficient, high-bandwidth, optical interconnect technology.

̽��ֱ��investment will support continued innovation in graphene photonics transceivers, a technology that could improve energy efficiency, reduce latency, and increase bandwidth for artificial intelligence (AI) and cellular data transmission.

With the investment, CamGraPhIC will enhance its research and development capabilities and establish a pilot manufacturing line. ̽��ֱ��facility will demonstrate a scalable mass production process compatible with commercial semiconductor and photonics foundries.

̽��ֱ��funding round was co-led by CDP Venture Capital, NATO Innovation Fund, Sony Innovation Fund, and Join Capital, with participation from Bosch Ventures, Frontier IP Group plc, and Indaco Ventures.

CamGraPhIC’s graphene-based transceivers provide a viable, stable, and scalable alternative to current silicon-based photonics. These transceivers deliver higher bandwidth density, and exceptional latency performance, while consuming 80% less energy than traditional pluggable data centre optical transceivers.

̽��ֱ��company say their innovation is particularly effective for transferring large volumes of data between graphic processing units (GPUs) and high bandwidth memory (HBM), which are fundamental to generative AI and high-performance computing.

̽��ֱ��transceivers operate efficiently across a broad temperature range, eliminating the need for complex and costly cooling systems. Thanks to a simplified device architecture enabled by the integration of graphene into the photonic structure, these transceivers are also more cost-effective to manufacture.

Thanks to this funding, CamGraPhIC will expand to applications in avionics, automotive advanced driver-assistance systems (ADAS), and space, where rugged, high-performance transceivers offer significant technical and commercial advantages over existing technologies.

“We are thrilled for this new phase in the journey towards commercialisation of CamGraPhIC groundbreaking and energy efficient devices, to speed up development of AI hardware, without impacting global emissions,” said Ferrari. “Having Sony, Bosch and NATO as shareholders and board members will help focus the work towards the most relevant applications, including defence and security.”

“With the backing of renowned investors, we are excited to propel towards commercialisation the Graphene Photonics technology to overcome the interconnection bottleneck of regenerative AI processing systems and driving the next leap in scaling bandwidth and reducing energy consumption for the future of optical data communications, ” said Romagnoli, former Head of Research Sector - Advanced Technologies for Photonic Integration of the Pisa National Inter- ̽��ֱ�� Consortium for Telecommunications (CNIT), and now Chief Scientific Officer of CamGraPhIC.

A ̽��ֱ�� of Cambridge spin-out company working to improve AI efficiency and bandwidth has raised €25 million in new funding.

CamGraPhIC

Marco Romagnoli (L) and Andrea Ferrari (R)

̽��ֱ��text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright © ̽��ֱ�� of Cambridge and licensors/contributors as identified. All rights reserved. We make our image and video content available in a number of ways – on our main website under its Terms and conditions, and on a range of channels including social media that permit your use and sharing of our content under their respective Terms.

Yes

Scientists develop ‘smart pyjamas’ to monitor sleep disorders

sc604 — Tue, 18 Feb 2025 11:06:44 +0000

̽��ֱ��team, led by the ̽��ֱ�� of Cambridge, developed printed fabric sensors that can monitor breathing by detecting tiny movements in the skin, even when the pyjamas are worn loosely around the neck and chest.

̽��ֱ��sensors embedded in the smart pyjamas were trained using a ‘lightweight’ AI algorithm and can identify six different sleep states with 98.6% accuracy, while ignoring regular sleep movements such as tossing and turning. ̽��ֱ��energy-efficient sensors only require a handful of examples of sleep patterns to successfully identify the difference between regular and disordered sleep.

̽��ֱ��researchers say that their smart pyjamas could be useful for the millions of people in the UK who struggle with disordered sleep to monitor their sleep, and how it might be affected by lifestyle changes. ̽��ֱ��results are reported in the Proceedings of the National Academy of Sciences (PNAS).

Sleep is vital for human health, yet more than 60% of adults experience poor sleep quality, leading to the loss of between 44 and 54 annual working days, and an estimated one percent reduction in global GDP. Sleep behaviours such as mouth breathing, sleep apnoea and snoring are major contributors to poor sleep quality, and can lead to chronic conditions such as cardiovascular disease, diabetes and depression.

“Poor sleep has huge effects on our physical and mental health, which is why proper sleep monitoring is vital,” said Professor Luigi Occhipinti from the Cambridge Graphene Centre, who led the research. “However, the current gold standard for sleep monitoring, polysomnography or PSG, is expensive, complicated and isn’t suitable for long-term use at home.”

Home devices that are simpler than PSG, such as home sleep tests, typically focus on a single condition and are bulky or uncomfortable. Wearable devices such as smartwatches, while more comfortable to wear, can only infer sleep quality, and are not effective for accurately monitoring disordered sleep.

“We need something that is comfortable and easy to use every night, but is accurate enough to provide meaningful information about sleep quality,” said Occhipinti.

To develop the smart pyjamas, Occhipinti and his colleagues built on their earlier work on a smart choker for people with speech impairments. ̽��ֱ��team re-designed the graphene-based sensors for breath analysis during sleep, and made several design improvements to increase sensitivity.

“Thanks to the design changes we made, the sensors are able to detect different sleep states, while ignoring regular tossing and turning,” said Occhinpinti. “ ̽��ֱ��improved sensitivity also means that the smart garment does not need to be worn tightly around the neck, which many people would find uncomfortable. As long as the sensors are in contact with the skin, they provide highly accurate readings.”

̽��ֱ��researchers designed a machine learning model, called SleepNet, that uses the signals captured by the sensors to identify sleep states including nasal breathing, mouth breathing, snoring, teeth grinding, central sleep apnoea (CSA), and obstructive sleep apnoea (OSA). SleepNet is a ‘lightweight’ AI network, that reduces computational complexity to the point where it can be run on portable devices, without the need to connect to computers or servers.

“We pruned the AI model to the point where we could get the lowest computational cost with the highest degree of accuracy,” said Occhinpinti. “This way we are able to embed the main data processors in the sensors directly.”

̽��ֱ��smart pyjamas were tested on healthy patients and those with sleep apnoea, and were able to detect a range of sleep states with an accuracy of 98.6%. By treating the smart pyjamas with a special starching step, they were able to improve the durability of the sensors so they can be run through a regular washing machine.

̽��ֱ��most recent version of the smart pyjamas are also capable of wireless data transfer, meaning the sleep data can be securely transferred to a smartphone or computer.

“Sleep is so important to health, and reliable sleep monitoring can be key in preventative care,” said Occhipinti. “Since this garment can be used at home, rather than in a hospital or clinic, it can alert users to changes in their sleep that they can then discuss with their doctor. Sleep behaviours such as nasal versus mouth breathing are not typically picked up in an NHS sleep analysis, but it can be an indicator of disordered sleep.”

̽��ֱ��researchers are hoping to adapt the sensors for a range of health conditions or home uses, such as baby monitoring, and have been in discussions with different patient groups. They are also working to improve the durability of the sensors for long-term use.

̽��ֱ��research was supported in part by the EU Graphene Flagship, Haleon, and the Engineering and Physical Sciences Research Council (EPSRC), part of UK Research and Innovation (UKRI).

Reference:
Chenyu Tang, Wentian Yi et al. ‘A deep learning-enabled smart garment for accurate and versatile monitoring of sleep conditions in daily life.’ PNAS (2025). DOI: 10.1073/pnas.2420498122

Researchers have developed comfortable, washable ‘smart pyjamas’ that can monitor sleep disorders such as sleep apnoea at home, without the need for sticky patches, cumbersome equipment or a visit to a specialist sleep clinic.

We need something that is comfortable and easy to use every night, but is accurate enough to provide meaningful information about sleep quality

Luigi Occhipinti

Illustration and photograph of 'smart pyjamas'

Yes

Cambridge to trial cutting-edge semiconductor technologies for wider use in major European project

sc604 — Tue, 10 Dec 2024 10:34:10 +0000

Photonic chips transmit and manipulate light instead of electricity, and offer significantly faster performance with lower power consumption than traditional electronic chips. 

̽��ֱ��Cambridge Graphene Centre and Cornerstone Photonics Innovation Centre at the ̽��ֱ�� of Southampton will partner with members from across Europe to host a pilot line, coordinated by the Institute of Photonic Sciences in Spain, combining state-of-the-art equipment and expertise from 20 research organisations.

̽��ֱ��PIXEurope consortium has been selected by the European Commission and Chips Joint Undertaking, a European initiative aiming to bolster the semiconductor industry by fostering collaboration between member states and the private sector. ̽��ֱ��consortium is supported by €380m in total funding.

̽��ֱ��UK participants will be backed by up to £4.2 million in funding from the Department of Science, Innovation and Technology (DSIT), match-funded by Horizon Europe. ̽��ֱ��UK joined the EU’s Chips Joint Undertaking in March 2024, allowing the country to collaborate more closely with European partners on semiconductor innovation.

̽��ֱ��new pilot line will combine state-of-the-art equipment and expertise from research organisations across 11 countries. It aims to encourage the adoption of cutting-edge photonic technologies across more industries to boost their efficiency.

Photonic chips are already essential across a wide range of applications, from tackling the unprecedented energy demands of datacentres, to enabling high-speed data transmission for mobile and satellite communications. In the future, these chips will become ever more important, unlocking new applications in healthcare, AI and quantum computing. 

Researchers at the Cambridge Graphene Centre will be responsible for the integration of graphene and related materials into photonic circuits for energy efficient, high-speed communications and quantum devices. “This may lead to life-changing products and services, with huge economic benefit for the UK and the world,” said Professor Andrea C. Ferrari, Director of the Cambridge Graphene Centre.

̽��ֱ��global market for photonic integrated circuits (PICs) production is expected to grow by more than 400% in the next 10 years. By the end of the decade, the global photonics market is expected to exceed €1,500bn, a figure comparable to the entire annual gross domestic product of Spain.

This growth is due to the demand from areas such as telecommunications, artificial intelligence, image sensing, automotive and mobility, medicine and healthcare, environmental care, renewable energy, defense and security, and a wide range of consumer applications.

̽��ֱ��combination of microelectronic chips and photonic chips provides the necessary features and specifications for these applications. ̽��ֱ��former are responsible for information processing by manipulating electrons within circuits based on silicon and its variants, while the latter uses photons in the visible and infrared spectrum ranges in various materials.

̽��ֱ��new pilot line aims to offer cutting-edge technological platforms, transforming and transferring innovative and disruptive integrated photonics processes and technologies to accelerate their industrial adoption. ̽��ֱ��objective is the creation of European-owned/made technology in a sector of capital importance for technological sovereignty, and the creation and maintenance of corresponding jobs in the UK and across Europe.

“My congratulations to Cornerstone and the Cambridge Graphene Centre on being selected to pioneer the new pilot line – taking a central role in driving semiconductor innovation to the next level, encouraging adoption of new technologies,” said Science Minister Lord Vallance. “ ̽��ֱ��UK laid the foundations of silicon photonics in the 1990s, and by pooling our expertise with partners across Europe we can address urgent global challenges including energy consumption and efficiency.”

“ ̽��ֱ��UK’s participation in the first Europe-wide photonics pilot line marks the start of the world’s first open access photonics integrated circuits ecosystem, stimulating new technology development with industry and catalyse disruptive innovation across the UK, while strengthening UK collaboration with top European institutions working in the field,” said Ferrari.

“PIXEurope is the first photonics pilot line that unifies the whole supply chain from design and fabrication, to testing and packaging, with technology platforms that will support a broad spectrum of applications,” said CORNERSTONE Coordinator Professor Calum Littlejohns. “I am delighted that CORNERSTONE will form a crucial part of this programme.”

̽��ֱ��Chips JU will also launch new collaborative R&D calls on a range of topics in early 2025. UK companies and researchers are eligible to participate.

̽��ֱ�� ̽��ֱ�� of Cambridge is one of two UK participants named as part of the PIXEurope consortium, a collaboration between research organisations from across Europe which will develop and manufacture prototypes of their products based on photonic chips.

Yes

G7 representatives meet in Cambridge to discuss semiconductors

hcf38 — Thu, 26 Sep 2024 09:35:45 +0000

Representatives of the Semiconductors Point of Contact Group from the G7 group of nations met in Cambridge on 26 September. ̽��ֱ��meeting was held at ARM, which designs over 95% of the processors in the world. Representatives from the ̽��ֱ�� of Cambridge, as well as representatives from local semiconductor companies, participated in the events.

Semiconductors underpin nearly every electrical, optical and quantum device, from mobile phones and medical equipment to electric vehicles. They are of global strategic significance due to the integral role they play in net zero, artificial intelligence and quantum technology.

̽��ֱ��G7 Semiconductor Points of Contact group is dedicated to facilitating information exchange and sharing best practices among G7 members. ̽��ֱ��PoC Group plans to exchange information on issues impacting the semiconductor industry, including pre-competitive industrial research and development priorities, sustainable manufacturing, the effect of non-market policies and practices, and crisis coordination channels.

Cambridge was chosen for the meeting in part because of its strong innovation ecosystem, which has produced more ‘unicorns’ – privately held startup companies valued at over US$1 billion – than anywhere else in the UK.

A 2023 report found that the ̽��ֱ�� of Cambridge contributes nearly £30 billion to the UK economy annually and supports more than 86,000 jobs across the UK, including 52,000 in the East of England.

For every £1 the ̽��ֱ�� spends, it creates £11.70 of economic impact, and for every £1 million of publicly-funded research income it receives, it generates £12.65 million in economic impact across the UK.

̽��ֱ��National Quantum Strategy (2023) and the National Semiconductor Strategy (2023) highlight the UK’s national strengths in quantum and photonic technologies and compound semiconductors. These sectors foster growth and create high-skilled jobs, and position the UK as a hub of global innovation.

Dr Diarmuid O’Brien, Pro-Vice-Chancellor for Innovation at the ̽��ֱ�� of Cambridge, said: “Semiconductors are a vital technology for the UK’s economic growth, and Cambridge is a leader in semiconductor research and development. Working with our partners in academia, industry and government, we can develop next-generation semiconductor technologies and companies, and train the next generation of semiconductor scientists and engineers.”

Professor Andrea Ferrari, Director of the Cambridge Graphene Centre, hosted, on behalf of the ̽��ֱ��, a formal dinner in Pembroke College for the attendees of the G7 Semiconductors group. He stated that: “Cambridge played a key foundational role in the development of electronics. ̽��ֱ��electron was discovered here in 1897, by JJ Thomson. In 1961, electron beam lithography, a key method for integrated circuit fabrication, was invented in Cambridge. These early achievements were followed by many advances in circuit design, innovative advanced materials, photonic and quantum communications. It is thus fitting that the G7 Semiconductors representatives met at the heart of where all started.”

Science Minister Lord Vallance said: "Semiconductors are an unseen but vital component in so many of the technologies we rely on in our lives and backing UK innovators offers a real opportunity to growth these firms into industry leaders, strengthening our £10 billion sector and ensuring it drives economic growth. Hosting the G7 semiconductors Points of Contact group is also a chance to showcase the UK’s competitive and growing sector and make clear our commitment to keeping the UK at the forefront of advancing technology."

Representatives from the G7 have met in Cambridge to discuss the main priorities for the future development of semiconductors and their impact on the global economy.

Yes

‘Smart choker’ uses AI to help people with speech impairment to communicate

sc604 — Fri, 13 Sep 2024 13:40:19 +0000

̽��ֱ��smart choker, developed by researchers at the ̽��ֱ�� of Cambridge, incorporates electronic sensors in a soft, stretchable fabric, and is comfortable to wear. ̽��ֱ��device could be useful for people who have temporary or permanent speech impairments, whether due to laryngeal surgery, or conditions such as Parkinson’s, stroke or cerebral palsy.

By incorporating machine learning techniques, the smart choker can also successfully recognise differences in pronunciation, accent and vocabulary between users, reducing the amount of training required.

̽��ֱ��choker is a type of technology known as a silent speech interface, which analyses non-vocal signals to decode speech in silent conditions – the user only needs to mouth the words in order for them to be captured. ̽��ֱ��captured speech signals can then be transferred to a computer or speaker to facilitate conversation.

Tests of the smart choker showed it could recognise words with over 95% accuracy, while using 90% less computational energy than existing state-of-the art technologies. ̽��ֱ��results are reported in the journal npj Flexible Electronics.

“Current solutions for people with speech impairments often fail to capture words and require a lot of training,” said Dr Luigi Occhipinti from the Cambridge Graphene Centre, who led the research. “They are also rigid, bulky and sometimes require invasive surgery to the throat.”

̽��ֱ��smart choker developed by Occhipinti and his colleagues outperforms current technologies on accuracy, requires less computing power, is comfortable for users to wear, and can be removed whenever it’s not needed. ̽��ֱ��choker is made from a sustainable bamboo-based textile, with strain sensors based on graphene ink incorporated in the fabric. When the sensors detect any strain, tiny, controllable cracks form in the graphene. ̽��ֱ��sensitivity of the sensors is more than four times higher than existing state of the art.

“These sensors can detect tiny vibrations, such as those formed in the throat when whispering or even silently mouthing words, which makes them ideal for speech detection,” said Occhipinti. “By combining the ultra-high sensitivity of the sensors with highly efficient machine learning, we’ve come up with a device we think could help a lot of people who struggle with their speech.”

Vocal signals are incredibly complex, so associating a specific signal with a specific word requires a high level of computational processing. “On top of that, every person is different in terms of the way they speak, and machine learning gives us the tools we need to learn and adapt the interpretation of signals from person to person,” said Occhipinti.

̽��ֱ��researchers trained their machine learning model on a database of the most frequently used words in English, and selected words which are frequently confused with each other, such as ‘book’ and ‘look’. ̽��ֱ��model was trained with a variety of users, including different genders, native and non-native English speakers, as well as people with different accents and different speaking speeds.

Thanks to the device’s ability to capture rich dynamic signal characteristics, the researchers found it possible to use lightweight neural network architectures with simplified depth and signal dimensions to extract and enhance the speech information features. This resulted in a machine learning model with high computational and energy efficiency, ideal for integration in battery-operated wearable devices with real-time AI processing capabilities.

“We chose to train the model with lots of different English speakers, so we could show it was capable of learning,” said Occhipinti. “Machine learning has the capability to learn quickly and efficiently from one user to the next, so the retraining process is quick.”

Tests of the smart choker showed it was 95.25% accurate in decoding speech. “I was surprised at just how sensitive the device is,” said Occhipinti. “We couldn’t capture all the signals and complexity of human speech before, but now that we can, it unlocks a whole new set of potential applications.”

Although the choker will have to undergo extensive testing and clinical trials before it is approved for use in patients with speech impairments, the researchers say that their smart choker could also be used in other health monitoring applications, or for improving communication in noisy or secure environments.

̽��ֱ��research was supported in part by the EU Graphene Flagship and the Engineering and Physical Sciences Research Council (EPSRC), part of UK Research and Innovation (UKRI).

Reference:
Chenyu Tang et al. ‘Ultrasensitive textile strain sensors redefine wearable silent speech interfaces with high machine learning efficiency.’ npj Flexible Electronics (2024). DOI: 10.1038/s41528-024-00315-1

Researchers have developed a wearable ‘smart choker’ that uses a combination of flexible electronics and artificial intelligence techniques to allow people with speech impairments to communicate by detecting tiny movements in the throat.

Luigi Occhipinti

Smart Choker

Yes

Sensors made from ‘frozen smoke’ can detect toxic formaldehyde in homes and offices

sc604 — Fri, 09 Feb 2024 19:00:00 +0000

̽��ֱ��researchers, from the ̽��ֱ�� of Cambridge, developed sensors made from highly porous materials known as aerogels. By precisely engineering the shape of the holes in the aerogels, the sensors were able to detect the fingerprint of formaldehyde, a common indoor air pollutant, at room temperature.

̽��ֱ��proof-of-concept sensors, which require minimal power, could be adapted to detect a wide range of hazardous gases, and could also be miniaturised for wearable and healthcare applications. ̽��ֱ��results are reported in the journal Science Advances.

Volatile organic compounds (VOCs) are a major source of indoor air pollution, causing watery eyes, burning in the eyes and throat, and difficulty breathing at elevated levels. High concentrations can trigger attacks in people with asthma, and prolonged exposure may cause certain cancers.

Formaldehyde is a common VOC and is emitted by household items including pressed wood products (such as MDF), wallpapers and paints, and some synthetic fabrics. For the most part, the levels of formaldehyde emitted by these items are low, but levels can build up over time, especially in garages where paints and other formaldehyde-emitting products are more likely to be stored.

According to a 2019 report from the campaign group Clean Air Day, a fifth of households in the UK showed notable concentrations of formaldehyde, with 13% of residences surpassing the recommended limit set by the World Health Organization (WHO).

“VOCs such as formaldehyde can lead to serious health problems with prolonged exposure even at low concentrations, but current sensors don’t have the sensitivity or selectivity to distinguish between VOCs that have different impacts on health,” said Professor Tawfique Hasan from the Cambridge Graphene Centre, who led the research.

“We wanted to develop a sensor that is small and doesn’t use much power, but can selectively detect formaldehyde at low concentrations,” said Zhuo Chen, the paper’s first author.

̽��ֱ��researchers based their sensors on aerogels: ultra-light materials sometimes referred to as ‘liquid smoke’, since they are more than 99% air by volume. ̽��ֱ��open structure of aerogels allows gases to easily move in and out. By precisely engineering the shape, or morphology, of the holes, the aerogels can act as highly effective sensors.

Working with colleagues at Warwick ̽��ֱ��, the Cambridge researchers optimised the composition and structure of the aerogels to increase their sensitivity to formaldehyde, making them into filaments about three times the width of a human hair. ̽��ֱ��researchers 3D printed lines of a paste made from graphene, a two-dimensional form of carbon, and then freeze-dried the graphene paste to form the holes in the final aerogel structure. ̽��ֱ��aerogels also incorporate tiny semiconductors known as quantum dots.

̽��ֱ��sensors they developed were able to detect formaldehyde at concentrations as low as eight parts per billion, which is 0.4 percent of the level deemed safe in UK workplaces. ̽��ֱ��sensors also work at room temperature, consuming very low power.

“Traditional gas sensors need to be heated up, but because of the way we’ve engineered the materials, our sensors work incredibly well at room temperature, so they use between 10 and 100 times less power than other sensors,” said Chen.

To improve selectivity, the researchers then incorporated machine learning algorithms into the sensors. ̽��ֱ��algorithms were trained to detect the ‘fingerprint’ of different gases, so that the sensor was able to distinguish the fingerprint of formaldehyde from other VOCs.

“Existing VOC detectors are blunt instruments – you only get one number for the overall concentration in the air,” said Hasan. “By building a sensor that can detect specific VOCs at very low concentrations in real time, it can give home and business owners a more accurate picture of air quality and any potential health risks.”

̽��ֱ��researchers say the same technique could be used to develop sensors to detect other VOCs. In theory, a device the size of a standard household carbon monoxide detector could incorporate multiple different sensors within it, providing real-time information about a range of different hazardous gases. “At Warwick, we're developing a low-cost multi-sensor platform that will incorporate these new aerogel materials and, coupled with AI algorithms, detect different VOCs,” said co-author Professor Julian Gardner from Warwick ̽��ֱ��.

“By using highly porous materials as the sensing element, we’re opening up whole new ways of detecting hazardous materials in our environment,” said Chen.

̽��ֱ��research was supported in part by the Henry Royce Institute, and the Engineering and Physical Sciences Research Council (EPSRC), part of UK Research and Innovation (UKRI). Tawfique Hasan is a Fellow of Churchill College, Cambridge.

Reference:
Zhuo Chen et al. ‘Real-time, noise and drift resilient formaldehyde sensing at room temperature with aerogel filaments.’ Science Advances (2024). DOI: 10.1126/sciadv.adk6856

Researchers have developed a sensor made from ‘frozen smoke’ that uses artificial intelligence techniques to detect formaldehyde in real time at concentrations as low as eight parts per billion, far beyond the sensitivity of most indoor air quality sensors.

NASA/JPL-Caltech

Silica aerogel

Yes

Licence type:

Public Domain

Graphene heads to the moon

sc604 — Wed, 30 Nov 2022 15:35:21 +0000

Cambridge researchers are part of a European project testing graphene’s ability to protect spacecraft against the sticky, sharp dust on the moon’s surface – a challenge for lunar missions since the Apollo era.

Artificial intelligence powers record-breaking all-in-one miniature spectrometers

sc604 — Thu, 20 Oct 2022 18:00:00 +0000

We see light and colours around us every day. However, to analyse the information it carries, we must analyse light using spectrometers, in the lab. These devices detect sparkles and substances that our eyes would otherwise not notice.

Now, an international team of researchers, including the ̽��ֱ�� of Cambridge, have designed a miniaturised spectrometer that breaks all current resolution records, and does so in a much smaller package, thanks to computational programmes and artificial intelligence.

̽��ֱ��new miniaturised devices could be used in a broad range of sectors, from checking the quality of food to analysing starlight or detecting faint clues of life in outer space. ̽��ֱ��results are reported in the journal Science.

Traditionally, spectrometers rely on bulky components to filter and disperse light. Modern approaches simplify these components to shrink footprints, but still suffer from limited resolution and bandwidth. Additionally, traditional spectrometers are heavy and take up extraordinary amounts of space, which limits their applications in portable and mobile devices.

To tackle these problems, and shrink the size of the system, researchers have coupled layered materials with artificial intelligence algorithms. ̽��ֱ��result is an all-in-one spectrometer thousands of times smaller than current commercial systems. At the same time, it offers performance comparable to benchtop systems. In other words, these new spectrometers will provide portable alternatives to uncover otherwise invisible information, without even going into the lab.

“We eliminate the need for detector arrays, dispersive components, and filters. It’s an all-in-one, miniaturised device that could revolutionise this field,” said Dr Hoon Hahn Yoon, from Aalto ̽��ֱ�� in Finland, first author of the paper. This spectrometer-on-chip technology is expected to offer high performance and new usability across science and industry.

̽��ֱ��detector uses van der Waals heterostructures – a ‘sandwich’ of different ingredients, including graphene, molybdenum disulfide, and tungsten diselenide. Different combinations of material components enable light detection beyond the visible spectrum, as far as the near-infrared region. This means the spectrometer detects more than just colour, enabling applications such as chemical analysis and night vision.

“We detect a continuum spectrum of light, opening a world of possibilities in a myriad of markets,” said Yoon. “Exploring other material combinations could uncover further functionalities, including even broader hyperspectral detection and improved resolution.”

Artificial intelligence is a key aspect of these devices, commonly called ‘computational’ spectrometers. This technology compensates for the inherent noise increase that inevitably occurs when the optical component is wholly removed.

“We were able to use mathematical algorithms to successfully reconstruct the signals and spectra, it’s a profound and transformative technological leap,” said lead author Professor Zhipei Sun, also from Aalto ̽��ֱ��, and a former member of Cambridge’s Department of Engineering. “ ̽��ֱ��current design is just a proof-of-concept. More advanced algorithms, as well as different combinations of materials, could soon provide even better miniaturised spectrometers.”

Spectrometers are used for toxin detection in food and cosmetics, cancer imaging, and in spacecraft – including the James Webb Space Telescope. And they will soon become more common thanks to the development and advancement of technologies such as the Internet of Things and Industry 4.0.

̽��ֱ��detection of light – and the full analysis of spectroscopic information – has applications in sensing, surveillance, smart agriculture, and more. Among the most promising applications for miniaturised spectrometers are chemical and biochemical analysis, thanks to the capabilities of the devices to detect light in the infrared wavelength range.

̽��ֱ��new devices could be incorporated into instruments like drones, mobile phones, and lab-on-a-chip platforms, which can carry out several experiments in a single integrated circuit. ̽��ֱ��latter also opens up opportunities in healthcare. In this field, spectrometers and light-detectors are already key components of imaging and diagnostic systems – the new miniaturised devices could enable the simultaneous visualisation and detection of ‘chemical fingerprints’, leading to possibilities in the biomedical area.

“Our miniaturised spectrometers offer high spatial and spectral resolution at the micrometre and nanometre scales, which is particularly exciting for responsive bio-implants and innovative imaging techniques,” said co-author Professor Tawfique Hasan, from the Cambridge Graphene Centre.

This technology has huge potential for scalability and integration, thanks to its compatibility with well-established industrial processes. It could open up the future for the next generation of smartphone cameras that evolve into hyperspectral cameras that conventional colour cameras cannot do. Researchers hope their contribution is a stepping stone towards the development of more advanced computational spectrometers, with record-breaking accuracy and resolution. This example, they say, is just the first of many.

Reference:
Hoon Hahn Yoon et al. ‘Miniaturized Spectrometers with a Tunable van der Waals Junction.’ Science (2022). DOI: 10.1126/science.add8544.

Using Artificial Intelligence (AI) to replace optical and mechanical components, researchers have designed a tiny spectrometer that breaks all current resolution records.

Suvi-Tuuli Akkanen, Mikko Turunen, Vincent Pelgrin. Aalto ̽��ֱ��.

On-chip spectrometer on a fingertip

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. Images, including our videos, are Copyright © ̽��ֱ�� of Cambridge and licensors/contributors as identified. All rights reserved. We make our image and video content available in a number of ways – as here, on our main website under its Terms and conditions, and on a range of channels including social media that permit your use and sharing of our content under their respective Terms.

Yes