A-Meta
A UK-US Collaboration for Active Metamaterials
The 樱花动漫 has received a 拢1.8 million research grant for a collaboration with , , and the in the USA to develop advanced, reconfigurable metamaterials that will revolutionise technologies such as wireless communication networks, remote imaging and artificial intelligence type computing.
The project will be led by experts from . It is one of 12 projects nationwide funded by a total 拢17 million investment from the EPSRC in their International Centre to Centre Research Collaboration programme.
The project, called 鈥淎-Meta鈥�, aims to develop a new family of metamaterials, a term used to describe artificially structured materials with properties that go beyond those found in nature.
The functionality of traditional metamaterials is however fixed at the time of fabrication in, making it hard to adapt them to the multifunctionality and reconfigurability essential to many of today鈥檚 applications.
A-Meta breaks from tradition by developing tunable, reconfigurable and programmable metamaterials, and will include input from a wide range of prestigious international partners, including:
- (USA)
UK
– Principle Investigator
– Co-investigator (RC1 lead)
– Co-investigator (RC2 lead)
– Co-investigator (RC3 lead)
– Co-investigator (RC1)
– Co-Investigator (RC3)
– Research-Co-Investigator (RC2)
– Researcher (RC2)
– Researcher (RC1)
– Researcher (RC3)
– PhD Researcher (RC1)
USA
(RC 1 & 2)
(RC 1 & 2)
(RC3)
(RC1)
(RC 2)
(RC 1 & 2)
The objective here is to combine all-dielectric and plasmonic metasurface concepts with chalcogenide phase-changematerials to provide fast, dynamic, and tunable control of the amplitude and phase of light. Such active control leads to a wide range of important technical applications, such as dynamic beam steering, holographic displays, reconfigurable and dynamic lensing, optical modulation and the focus of this work- optical analogue computing and multi/hyper-spectral sensing.
Analog optical computing has seen an increase in attention in recent years due to potential advantages it holds over its digital counterpart, including intrinsic parallelism, low-loss transmission and ultra-high bandwidth. Fast and energy efficient optical computing systems have been demonstrated, for example implementing neural networks and matrix-vector multiplication. In A-Meta we are developing metamaterial-based optical computing systems for a range of computational tasks, including image processing, matrix-vector multiplication, optical mode conversion and more. Specifically - along with A-Meta collaborators at the City University of New York, the University of North Carolina at Charlotte and the University of Oxford - we have developed novel convolutional optical computing approaches using active phase-change metasurfaces that enable emerging applications in artificial intelligence-assisted imaging and vision devices with switchable multitasking.
T鈥宧e lead for this Research Challenge is .
Here we will address the lack of practical microwave and terahertz modulators by exploiting the change in conductivity of a semiconductor upon illumination with an optical light source by a novel approach to reduce charge carrier recombination. Fast, high-resolution, and efficient GHz-THz spatial light modulators will be transformative to a new generation of reconfigurable complex systems for imaging, communications, beam steering and more.
Having aligned the optical excitation array with a periodic metasurface, we are able to controllably excite small regions around each meta-atom, and probe its function to the metasurface response. The image shows the photoactive regions around individual meta-atoms in a dual frequency band metasurface: we see individual switching of meta-atoms in both the low (red) and high (green) frequency bands of the surface, as well as defective meta-atoms.
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T鈥宧e lead for this Research Challenge is .鈥�
We will develop a new generation of dynamically tunable elastic-wave metamaterials from the micro- to the macroscopic to address (i) the creation of phononic test artefacts enabling in-situ monitoring of additive manufacturing (AM) processes, and (ii) the incorporation of advanced materials, using AM, to create tunable phononic materials and devices for applications such as signal processing.
T鈥宧e lead for this Research Challenge is .
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