This proposal seeks funding to create a Centre for Doctoral Training (CDT) in Connected Electronic and Photonic Systems (CEPS). Photonics has moved from a niche industry to being embedded in the majority of deployed systems, ranging from sensing, biophotonics and advanced manufacturing, through communications from the chip-to-chip to transcontinental scale, to display technologies, bringing higher resolution, lower power operation and enabling new ways of human-machine interaction. These advances have set the scene for a major change in commercialisation activity where electronics photonics and wireless converge in a wide range of information, sensing, communications, manufacturing and personal healthcare systems. Currently manufactured systems are realised by combining separately developed photonics, electronic and wireless components. This approach is labour intensive and requires many electrical interconnects as well as optical alignment on the micron scale. Devices are optimised separately and then brought together to meet systems specifications. Such an approach, although it has delivered remarkable results, not least the communications systems upon which the internet depends, limits the benefits that could come from systems-led design and the development of technologies for seamless integration of electronic photonics and wireless systems. To realise such connected systems requires researchers who have not only deep understanding of their specialist area, but also an excellent understanding across the fields of electronic photonics and wireless hardware and software. This proposal seeks to meet this important need, building upon the uniqueness and extent of the UCL and Cambridge research, where research activities are already focussing on higher levels of electronic, photonic and wireless integration; the convergence of wireless and optical communication systems; combined quantum and classical communication systems; the application of THz and optical low-latency connections in data centres; techniques for the low-cost roll-out of optical fibre to replace the copper network; the substitution of many conventional lighting products with photonic light sources and extensive application of photonics in medical diagnostics and personalised medicine. Many of these activities will increasingly rely on more advanced systems integration, and so the proposed CDT includes experts in electronic circuits, wireless systems and software. By drawing these complementary activities together, and building upon initial work towards this goal carried out within our previously funded CDT in Integrated Photonic and Electronic Systems, it is proposed to develop an advanced training programme to equip the next generation of very high calibre doctoral students with the required technical expertise, responsible innovation (RI), commercial and business skills to enable the £90 billion annual turnover UK electronics and photonics industry to create the closely integrated systems of the future. The CEPS CDT will provide a wide range of methods for learning for research students, well beyond that conventionally available, so that they can gain the required skills. In addition to conventional lectures and seminars, for example, there will be bespoke experimental coursework activities, reading clubs, roadmapping activities, responsible innovation (RI) studies, secondments to companies and other research laboratories and business planning courses. Connecting electronic and photonic systems is likely to expand the range of applications into which these technologies are deployed in other key sectors of the economy, such as industrial manufacturing, consumer electronics, data processing, defence, energy, engineering, security and medicine. As a result, a key feature of the CDT will be a developed awareness in its student cohorts of the breadth of opportunity available and the confidence that they can make strong impact thereon.

In the rapidly growing field of genomics, pattern matching against a sequence of nucleotides or amino acids is critical to the assembly, annotation and comparison of complex genomes. This is essential to tackle some of the leading diseases in humans (e.g. cancer) as well as identify and prevent the pathogens that can decimate stable crops that much of the world's population depend on for food-security. As gene-sequencing technologies continue to evolve, the public sequencing databases that contain this knowledge are doubling in size every 18 months or less. Identifying and understanding newly sequenced genes through extensive searches of these DNA databases is becoming prohibitively expensive for genomic researchers; it requires access to large high-performance computing (HPC) resources that consume vast amounts of energy for power and cooling. This project's goal is to design and implement a revolutionary optical processing device that can perform large-scale gene database searches powered by just a standard mains supply. Its success will dramatically reduce the capital and energy costs required to undertake these fundamental DNA-searching calculations.

When manufacturing any kind of electronic device, patterning is required to achieve small features, such as different regions of materials with different functions. The ever-increasing complexity of modern electronics and photonics has led to a plethora of approaches to substrate patterning. For each of these approaches, there are always compromises between the speed of patterning (write speed), the minimum feature size, versatility and cost. The most dominant patterning process in electronics and photonics manufacturing is mask-based photolithography. Here, the chip to be patterned is coated with a light-sensitive material known as a "resist," and light is shone onto the resist through a mask with deliberately placed holes. Light that passes through the holes causes a chemical change in the resist, and thus the pattern is transferred from the mask onto the chip. The disadvantage is that each photolithography mask is only suitable for a one particular type of chip design and cannot be reconfigured for the manufacture of other chip designs, and mask design and fabrication is time-consuming and costly. Alternative patterning techniques, known as direct-write lithography, do enable great flexibility in device design, but at the expense of slow patterning speeds, and often large capital and operating costs. Here, we propose a novel process for photolithography, which we name holographic multi-beam interference lithography (HMBIL). HMBIL promises large area patterning with sub-wavelength resolution as well as fast write speeds, short development times, low costs and a dynamically reconfigurable choice of exposure pattern. Using HMBIL, we will demonstrate patterning of arbitrarily-shaped 100 nm feature sizes over large areas with high throughput (>25 cm^2 device area in under 1 hour), which is currently unachievable with direct-write lithography techniques. As a proof-of-principle, we will demonstrate the capability of HMBIL for manufacturing an example device structure: multispectral filter arrays. These filter arrays, when integrated with an image sensor, will allow the acquisition of light spectra for applications as diverse as medical imaging to remote sensing. HMBIL manufacture of multispectral filter arrays will open up a range of avenues for custom detectors and imaging sensors for security, industrial or medical applications. We envisage this versatile new HMBIL process primarily in two locations in the manufacturing chain: Firstly, as a means of rapid prototyping of nanofabricated designs and secondly, as a means of large scale production of individually customised components. This will revolutionise manufacturing processes across a broad range of application areas including miniaturised optoelectronics, versatile point-of-care diagnostic devices, displays and image sensors, on-chip photonics (waveguides and photonic crystals), plasmonics, nano/micro-electromechanical machines, microfluidics, embedded systems and the internet of things, and many more.

Real-time processing of high-resolution images is essential for many new A.I. technologies. However, currently the computational needs, cost and energy requirements are prohibitive for many mainstream applications, not to mention the lack of portability of the processing systems or latency issues if computing is done via the cloud. New approaches must be adopted to meet these challenging demands. Highly parallel computing is widely agreed as the only viable way to achieve the level of performance needed for real-time imaging. However, the complexity and number of circuit components required to achieve this with traditional semiconductor CMOS approaches impacts the overall system's speed and optical resolution. Thus, there is a need to develop new types of circuit components that are specifically designed for neuromorphic computing. Memristors are two terminal electronic devices that have attracted intense research interest owing to their simple fabrication, low-cost manufacture, low power operation and their capacity for ultra-high density, non-volatile data storage. In recent years, memristor performances have advanced considerably. Very high levels of endurance (120 billion cycles) and retention (>10 years) have been achieved, and ultra-high-density cross-bar arrays have been realized with scalability down to 2 nm. However, it is their ability to emulate the memory and learning properties of biological synapses and their potential to produce a new generation of ultra-high performance artificial intelligent devices that has ignited researchers' interest in these remarkable devices. Many basic neuronal functions have been demonstrated and memristor arrays have been shown to efficiently carry out processing in the analogue domain, removing the computational bottlenecks associated with the large number of vector-matrix operations. This combined with recent improvements in device reliability gives a promising outlook for their future use as the world seeks new technologies to circumvent the end of Moore's law and the problems of traditional von Neumann computing, which has inherent bottlenecks in the way information is processed and transported. Recently there has been a drive towards the development of memristor devices that can be read, written or have their switching characteristics modified by the application of light. The development of these devices, termed Optical Memristors, arises due to several potential benefits. Optical systems are free of sources of electronic noise and capacitive coupling effects, which limit the operating speed of traditional electronic devices. The combination of memristor technology with optical systems offers the additional advantage of high-speed data routing while consuming little power, as well as integration as a building block within future optical computer architectures. In this proposal a new type of computer vision recognition system is proposed based on optical memristors (OM) and cellular nonlinear networks (CNN) that leverages the unique capacity of OM's to detect light and store information while also exploiting CNN's ability to simultaneously process the information in all cells at once. This will enable ultra-fast real-time (in-memory and parallel) computation. The approach outlined contrasts with standard vision recognition systems which are inherently limited by data transfer bottlenecks and the slow, serial processing of information. This research will therefore pave the way to a new generation of ultra-fast, high-resolution vision recognition systems that will impact a wide range of current societal needs (e.g. safer autonomous driving, better security systems) and numerous applications in medicine (e.g. high throughput cell imaging for early cancer diagnostics).

In PHOENIX, we create the next generation of compact photonic integrated circuits (PIC) offering a continuous and efficient control over optical signals. A barium titanate (BTO) on silicon nitride (SiN) platform will be optimized to enable novel functionalities and produce enhanced PICs. The novel functionalities stem from a combination of materials having a metal-insulator transition with epitaxial ferroelectrics. Vanadium oxides (VOx) deliver a maximum contrast in absorption while Barium Titanate (BTO) offers an efficient and programmable control of the phase of an optical signal through Pockels and photorefractive effects. The developed technologies will be demonstrated in four uses cases in high-impact emerging applications: 1) fully homomorphic encryption, 2) 5G infrastructure, 3) inference of deep neural networks and 4) training of deep neural networks. The project has four main objectives: a) to provide novel photonic technologies with enhanced functionalities thanks to the integration of VOx and BTO, b) to provide a BTO/SiN waveguide platform for subsequent manufacturing of PICs and an upgraded version of such a platform integrating VOx with the potential to improve their performance and scalability, c) to build up the demonstrators, and d) to advance in the understanding, realization and upscaling of high-quality oxide thin-films by molecular beam epitaxy (MBE) on large area. The validation of the developed technology will be completed with an extrapolation to benchmark against representative existing systems and a roadmap for photonic-electronic integration. The project will perform a market analysis and a techno-economic evaluation in order to define business models and exploitation plans that ensure the sustainability of the PHOENIX platform to reduce innovation-to market-time and R&I costs for disruptive high-tech SMEs and maximize the impact of the 4 user cases demonstrators