A significant and escalating worldwide health burden is the aging population and its demand for accurate medical diagnostics. Of particular concern are osteo diseases such as osteoporosis as these become significantly more apparent in elderly populations. Unfortunately, current diagnostic methods are poor predictors of pathology outcomes such as fracture risk. The challenge then is to identify and develop enhanced and new approaches to bone quality appraisal that can be employed for accurate prognosis, targeted therapies and therapy assessment. It has been well demonstrated that 'bone quality' (including characteristics such as collagen/mineral ratio, collagen integrity, mineral crystallite size, microstrain) has a marked affect on a bone's mechanical properties and is probably the 'missing' information required to produce diagnostically predictive models of fracture. Unfortunately there is no current technique for its in vivo determination. However, such information is embodied within conventional X-ray scatter signatures (e.g. X-ray diffraction) although these are weak and present significant measurement difficulties. We have identified a potential route to in vivo measurement of these clinically valuable but weak signals. The technology has only recently become available through independent innovations for (i) X-ray generation (flat panel/source-on-a-chip) and (ii) obtaining 'amplified' information from scattered X-rays (using 'focal construct technology', FCT). Critically, the multi-emitter flat panel X-ray source will enable us to engineer precisely the analytical X-ray beam required to provide scatter signatures enhanced by several orders of magnitude. The FCT beam topology also enables the simultaneous measurement of the absorbed X-rays (to estimate bone mineral density) and those coherently (and incoherently) scattered. The source-on-a-chip technology is manufactured as a flat-panel device capable of generating X-rays from very low-power sources. It has the additional advantage that it may be fabricated to produce an X-ray source of precisely the geoemetric form required for FCT. Ultimately we envisage these techniques being integrated within a single imaging/DEXA/scatter system to provide a comprehensive diagnostic tool. The nature of the techniques also enables design towards hand portable devices for point-of-patient care. Thus this proposal principally concerns the development of a new instrument that will be subsequently used to examine the possibilities of applying our novel technologies to a number of different areas and therefore it will enable a new and exciting research capability.

This project will bring exciting advances to X-ray imaging by revealing the true nature of materials buried in 3-dimensional scans. The main limitation of conventional X-ray absorption imaging is that the image forming signals are a function of the attenuation coefficient, which tells us almost nothing about the chemical or crystallographic structure of the object under inspection. However, it is well understood that if diffracted flux, rather than the transmitted X-rays, is collected then slice images may be reconstructed using similar algorithms to conventional computed tomography (CT). The measurement of the energy or wavelength of the diffracted X-rays together with their associated diffraction angles enables the calculation of crystallographic parameters to identify, for example, the material phase of a sample. Scientists and engineers routinely measure diffracted flux from carefully prepared samples in instruments called diffractometers. Typically, this 'molecular fingerprinting' process uses relatively soft radiation and long inspection times of which both are impractical for security and in vivo diagnostic imaging. Despite significant efforts over the decades, there is little evidence of the 'gold standard' specificity and sensitivity achieved in laboratory settings being realised in time critical, commercially viable 3-dimensional imaging technologies. For example, the security screening industry has recognised the potential for X-ray diffraction as a 'gold standard' probe since the early 1990s. The challenge in this sector includes identifying powders, liquids, aerosols, and gels buried amongst the clutter of everyday objects in security scans of luggage. State-of-the-art CT spectroscopic scanners are limited fundamentally and are unable to deal adequately with homemade explosives. A main limitation of using diffracted radiation is that the signals are often orders of magnitude weaker in comparison with the primary incident beam. This fundamental limitation leads to long inspection times i.e. minutes or hours per point measurement, which in general is impractical for imaging. We have previously demonstrated a focal construct geometry (FCG) method where a hollow or conical shell beam produces high-intensity patterns or caustics in the diffracted flux from a sample. The bright caustics enable high-speed measurements that can be deconvoluted to form depth-resolved sectional images. Our novel method enables spatial features much smaller than the diameter of the interrogating beam to be resolved accurately in the reconstructed images. In keeping with standard computed tomography, FCG tomography in absorption and diffraction both use similar reconstruction principles. In this project, we propose reducing the total number of X-ray measurements and X-ray dose by more than 90% by applying sporadic sampling to FCG absorption/diffraction signals. We use a state-of-the-art flat panel X-ray source with multiple X-ray emission points optically coupled to energy resolving detectors. We treat the array of emitters as a virtual or spatially offset linear array (SOLA) to implement sporadic sampling independently of the minimum separation between emitter points (limited by the emitter physics) and to minimise crosstalk between measurements. We expect our method to enable the collection of diffraction and absorption signals at the same scan rate to realise depth-resolved material specific imaging. A successful demonstration of our method would establish a platform technology scalable in both X-ray energy and inspection space. This work will maintain the UK at the forefront of these unique and exciting scientific developments in security and diagnostic imaging.

The critical importance of capabilities for semiconductor research in the UK is recognised as part of a national strategy, as stressed in the recent BEIS Report 'The semiconductor industry in the UK'. Particular strength in research is centred around a number of cleanroom facilities located at academic institutuions. The University of Southampton hosts a range of cutting-edge nanofabrication tools which enable a range of research activities in electronic and photonic devices. Fabrication of semiconductor devices and circuits becomes cost effective when processed on a large wafer. However, process efficiency can only be achieved if an ultra-high-resolution scanning electron microscope (SEM) with material characterisation system is available to provide high throughput feedback results to improve fabrication and facilitate novel process development. Manually operated SEMs are a common imaging tool for characterisation used in academic research but automated in-line imaging of wafers throughout a process flow is required to achieve fast imaging and shorten inspection time from fabrication processes. The aim of the proposal is to acquire an ultra-high-resolution SEM (UHR-SEM) capable of material characterisation for wafers up to 200 mm in diameter at the University of Southampton. As device feature sizes are reduced, dimension and performance variations across the wafer become an issue which must be mitigated at the early stage of the fabrication. Therefore, the proposed UHR-SEM will be unique within the UK academic landscape since it will perform automated in-line imaging and analysis of entire wafers up to 200 mm in diameter at sub-nm resolution. The system will also have a low landing voltage on samples to reduce surface damage during imaging of delicate devices and patterned resists, as well as a good depth of focus for the inspection of thick multi-stack materials. The UHR-SEM will address the main challenges in large wafer imaging such as generating relevant surface metrology information at nanoscale dimensions and creating a detailed map showing various material parameters such as chemical composition and defect distribution.

Machine learning (ML), in particular Deep Learning (DL) is one of the fastest growing areas of modern science and technology, which has potentially enormous and transformative impact on all areas of our life. The applications of DL embrace many disciplines such as (bio-)medical sciences, computer vision, the physical sciences, the social sciences, speech recognition, gaming, music and finance. DL based algorithms are now used to play chess and GO at the highest level, diagnose illness, drive cars, recruit staff and even make legal judgements. The possible applications in the future are almost unlimited. Perhaps DL methods will be used in the future to predict the weather and climate, of even human behaviour. However, alongside this explosive growth has been a concern that there is a lack of explainability behind DL and the way that DL based algorithms make their decisions. This leads to a lack of trustworthiness in the use of the algorithms. A reason for this is that the huge successes of deep learning is not well understood, the results are mysterious, and there is a lack of a clear link between the data training DL algorithms (which is often vague and unstructured) and the decisions made by these algorithms. Part of the reason for this is that DL has advanced so fast, that there is a lack of understanding of its foundations. According to the leading computer scientist Ali Rahimi at NIPS 2017: 'We say things like "machine learning is the new electricity". I'd like to offer another analogy. Machine learning has become alchemy!' Indeed, despite the roots of ML lying in mathematics, statistics and computer science there currently is hardly any rigorous mathematical theory for the setup, training and application performance of deep neural networks. We urgently need the opportunity to change machine learning from alchemy into science. This programme grant aims to rise to this challenge, and, by doing so, to unlock the future potential of artificial intelligence. It aims to put deep learning onto a firm mathematical basis, and will combine theory, modelling, data, computation to unlock the next generation of deep learning. The grant will comprise an interlocked set of work packages aimed to address both the theoretical development of DL (so that it becomes explainable) and the algorithmic development (so that it becomes trustworthy). These will then be linked to the development of DL in a number of key application areas including image processing, partial differential equations and environmental problems. For example we will explore the question of whether it is possible to use DL based algorithms to forecast the weather and climate faster and more accurately than the existing physics based algorithms. The investigators on the grant will be doing both theoretical investigations and will work with end-users of DL in many application areas. Mindful that policy makers are trying to address the many issues raised by DL, the investigators will also reach out to them through a series of workshops and conferences. The results of the work will also be presented to the public at science festivals and other open events.

In a consortium led by Heriot-Watt with St Andrews, Glasgow, Strathclyde, Edinburgh and Dundee, this proposal for an "EPSRC CDT in Industry-Inspired Photonic Imaging, Sensing and Analysis" responds to the priority area in Imaging, Sensing and Analysis. It recognises the foundational role of photonics in many imaging and sensing technologies, while also noting the exciting opportunities to enhance their performance using emerging computational techniques like machine learning. Photonics' role in sensing and imaging is hard to overstate. Smart and autonomous systems are driving growth in lasers for automotive lidar and smartphone gesture recognition; photonic structural-health monitoring protects our road, rail, air and energy infrastructure; and spectroscopy continues to find new applications from identifying forgeries to detecting chemical-warfare agents. UK photonics companies addressing the sensing and imaging market are vital to our economy (see CfS) but their success is threatened by a lack of doctoral-level researchers with a breadth of knowledge and understanding of photonic imaging, sensing and analysis, coupled with high-level business, management and communication skills. By ensuring a supply of these individuals, our CDT will consolidate the UK industrial knowledge base, driving the high-growth export-led sectors of the economy whose photonics-enabled products and services have far-reaching impacts on society, from consumer technology and mobile computing devices to healthcare and security. Building on the success of our CDT in Applied Photonics, the proposed CDT will be configured with most (40) students pursuing an EngD degree, characterised by a research project originated by a company and hosted on their site. Recognizing that companies' interests span all technology readiness levels, we are introducing a PhD stream where some (15) students will pursue industrially relevant research in university labs, with more flexibility and technical risk than would be possible in an EngD project. Overwhelming industry commitment for over 100 projects represents a nearly 100% industrial oversubscription, with £4.38M cash and £5.56M in-kind support offered by major stakeholders including Fraunhofer UK, NPL, Renishaw, Thales, Gooch and Housego and Leonardo, as well as a number of SMEs. Our request to EPSRC for £4.86M will support 35 students, from a total of 40 EngD and 15 PhD researchers. The remaining students will be funded by industrial (£2.3M) and university (£0.93M) contributions, giving an exceptional 2:3 cash gearing of EPSRC funding, with more students trained and at a lower cost / head to the taxpayer than in our current CDT. For our centre to be reactive to industry's needs a diverse pool of supervisors is required. Across the consortium we have identified 72 core supervisors and a further 58 available for project supervision, whose 1679 papers since 2013 include 154 in Science / Nature / PRL, and whose active RCUK PI funding is £97M. All academics are experienced supervisors, with many current or former CDT supervisors. An 8-month frontloaded residential phase in St Andrews and Edinburgh will ensure the cohort gels strongly, and will equip students with the knowledge and skills they need before beginning their research projects. Business modules (x3) will bring each cohort back to Heriot-Watt for 1-week periods, and weekend skills workshops will be used to regularly reunite the cohort, further consolidating the peer-to-peer network. Core taught courses augmented with specialist options will total 120 credits, and will be supplemented by professional skills and responsible innovation training delivered by our industry partners and external providers. Governance will follow our current model, with a mixed academic-industry Management Committee and an independent International Advisory Board of world-leading experts.