Projects
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| Reference Number | EP/U536866/1 | |
| Title | Intelligent Nanofabrication for Nanophotonic Devices | |
| Status | Started | |
| Energy Categories | Renewable Energy Sources (Solar Energy, Photovoltaics) 20%; Not Energy Related 80%; |
|
| Research Types | Basic and strategic applied research 100% | |
| Science and Technology Fields | ENGINEERING AND TECHNOLOGY (Mechanical, Aeronautical and Manufacturing Engineering) 100% | |
| UKERC Cross Cutting Characterisation | Not Cross-cutting 100% | |
| Principal Investigator |
Professor KF MacDonald Optoelectronics Research Centre (ORC) University of Southampton |
|
| Award Type | Standard | |
| Funding Source | EPSRC | |
| Start Date | 01 October 2025 | |
| End Date | 30 September 2028 | |
| Duration | 36 months | |
| Total Grant Value | £748,652 | |
| Industrial Sectors | Manufacturing | |
| Region | South East | |
| Programme | NC : Engineering | |
| Investigators | Principal Investigator | Professor KF MacDonald , Optoelectronics Research Centre (ORC), University of Southampton |
| Other Investigator | Dr B Mills , Optoelectronics Research Centre (ORC), University of Southampton Professor N Zheludev , Optoelectronics Research Centre (ORC), University of Southampton |
|
| Web Site | ||
| Objectives | ||
| Abstract | We seek to improve the efficacy, efficiency and reproducibility of focused ion beam (FIB) nanofabrication processes for advanced photonic materials and devices, and in turn their optical performance and energy efficiency, through the application of deep learning. Our methods will enable rapid optimization of nanostructures for optical function informed by real process and material characteristics, rather than analytical/numerical approximations or wasteful trial-and-error. Deep learning offers a novel, systematic, data-driven approach to modelling the FIB milling process that is applicable to arbitrary nano/microstructural geometries in any target medium, whereby milling outcomes can be accurately predicted without the need for knowledge or understanding of (often unavailable/inaccessible) material parameters, the fundamental physics of ion-atom interactions, or the mathematical description of time-dependent 3D structural geometry. We will show how neural networks can: rapidly accrue understanding of the complex relationships among numerous sample and system parameters that affect process outcomes for a variety of metal, semiconductor and dielectric target materials commonly used in nanophotonic devices, to expedite or negate the need for conventional 'dose testing'; be configured to solve challenging inverse problems, answering the question "what input design will generate a desired output structure?"; optimize (rather than just simulate) milling processes, to answer the question "what input design and process parameters will best deliver a structure with prescribed optical and/or nanomechanical properties?". Alongside these trained functionalities we expect emergent capabilities, such as the ability to make accurate predictions for unseen target media, and inherent compensation for systematic artefacts. The project aligns to the EPSRC strategic priority on "Frontiers in Engineering and Technology": it will leverage novel capabilities and ideas in deep learning and nanophotonics to facilitate "breakthroughs in ... tools and techniques enabling researchers and businesses to make, measure and model more efficiently and effectively," and to "accelerate design to manufacture of the new materials needed for a more resilient, sustainable UK". FIB milling is a key enabling technology in many areas of fundamental and applied research, and high-tech (electronics and photonics) industrial applications of importance to the UK's position at the forefront of physical and bio sciences research and advanced technology development - for micro/nanoscale rapid prototyping, materials/device characterisation and cross-sectional/tomographic imaging (including in support of other high-throughput, e.g. lithographic, nanofabrication processes), and transmission electron microscopy sample preparation. Our initial focus will be on applications in nanophotonics (e.g. to the optimized fabrication of metasurface optics and energy-efficient optomechanical time crystals), but the techniques developed will be transferrable to beneficiaries in other domains of engineering, physical science and technology, deployable on any FIB platform, and indeed adaptable to other direct-write micro/nanofabrication processes | |
| Data | No related datasets |
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| Projects | No related projects |
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| Publications | No related publications |
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| Added to Database | 07/01/26 | |
