The sensing, processing and transport of information is at the heart of modern life, as can be seen from the ubiquity of smart-phone usage on any street. From our interactions with the people who design, build and use the systems that make this possible, we have created a programme to make possible the first data interconnects, switches and sensors that use lasers monolithically integrated on silicon, offering the potential to transform Information and Communication Technology (ICT) by changing fundamentally the way in which data is sensed, transferred between and processed on silicon chips. The work builds on our demonstration of the first successful telecommunications wavelength lasers directly integrated on silicon substrates. The QUDOS Programme will enable the monolithic integration of all required optical functions on silicon and will have a similar transformative effect on ICT to that which the creation of silicon integrated electronic circuits had on electronics. This will come about through removing the need to assemble individual components, enabling vastly increased scale and functionality at greatly reduced cost.

Optical networks underpin the global digital communications infrastructure, and their development has simultaneously stimulated the growth in demand for data, and responded to this demand by unlocking the capacity of fibre-optic channels. The work within the UNLOC programme grant proved successful in understanding the fundamental limits in point-to-point nonlinear fibre channel capacity. However, the next-generation digital infrastructure needs more than raw capacity - it requires channel and flexible resource and capacity provision in combination with low latency, simplified and modular network architectures with maximum data throughput, and network resilience combined with overall network security. How to build such an intelligent and flexible network is a major problem of global importance. To cope with increasingly dynamic variations of delay-sensitive demands within the network and to enable the Internet of Skills, current optical networks overprovision capacity, resulting in both over- engineering and unutilised capacity. A key challenge is, therefore, to understand how to intelligently utilise the finite optical network resources to dynamically maximise performance, while also increasing robustness to future unknown requirements. The aim of TRANSNET is to address this challenge by creating an adaptive intelligent optical network that is able to dynamically provide capacity where and when it is needed - the backbone of the next-generation digital infrastructure. Our vision and ambition is to introduce intelligence into all levels of optical communication, cloud and data centre infrastructure and to develop optical transceivers that are optimally able to dynamically respond to varying application requirements of capacity, reach and delay. We envisage that machine learning (ML) will become ubiquitous in future optical networks, at all levels of design and operation, from digital coding, equalisation and impairment mitigation, through to monitoring, fault prediction and identification, and signal restoration, traffic pattern prediction and resource planning. TRANSNET will focus on the application of machine techniques to develop a new family of optical transceiver technologies, tailored to the needs of a new generation of self-x (x = configuring, monitoring, planning, learning, repairing and optimising) network architectures, capable of taking account of physical channel properties and high-level applications while optimising the use of resources. We will apply ML techniques to bring together the physical layer and the network; the nonlinearity of the fibres brings about a particularly complex challenge in the network context as it creates an interdependence between the signal quality of all transmitted wavelength channels. When optimising over tens of possible modulation formats, for hundreds of independent channels, over thousands of kilometres, a brute force optimisation becomes unfeasible. Particular challenges are the heterogeneity of large scale networks and the computational complexity of optimising network topology and resource allocation, as well as dynamical and data-driven management, monitoring and control of future networks, which requires a new way of thinking and tailored methodology. We propose to reduce the complexity of network design to allow self-learned network intelligence and adaptation through a combination of machine learning and probabilistic techniques. This will lead to the creation of computationally efficient approaches to deal with the complexity of the emerging nonlinear systems with memory and noise, for networks that operate dynamically on different time- and length-scales. This is a fundamentally new approach to optical network design and optimisation, requiring a cross-disciplinary approach to advance machine learning and heuristic algorithm design based on the understanding of nonlinear physics, signal processing and optical networking.

M-ENGINE proposes a unique solution to the rapidly increasing bandwidth demands of data centers. With the massive growth of AI and social media in an increasingly connected world, data centers are expected to account for 20% of Europe's energy use by 2030, posing a significant challenge to meet the EU's climate goals. Current solutions to increase bandwidth in optical communications involve adding more single-channel lasers, which neither meets the capacity needs nor the energy requirements. Our proposal offers a scalable solution based on the Nobel prize-winning technology of optical frequency combs to provide highly coherent multi-channel lasers for high-capacity, low energy consumption data transmission. M-ENGINE's solution can replace 100s of individual lasers used in connecting data centers with just one compact system. The proposal combines Enlightra's photonic chip technology with X-Celeprint's cutting-edge solution of micro-transfer printing for scalable heterogeneous integration of all necessary photonic and electronic components. Eblana photonics’ high-power distributed feedback lasers will be transformed for transfer printing on the wafer scale, while Deutsches Elektronen-Synchrotron (DESY) and Laboratoire Interdisciplinaire Carnot de Bourgogne (ICB) will contribute recent breakthroughs in chip-integrated frequency combs enabling increased efficiency, stability, and equalized power of the generated data channels. Dublin City University (DCU) will perform independent performance testing for telecom before test devices are sent out to customers for pilot projects. The result will be a scalable photonic chip engine meeting future data needs with reliability, long-term operation, and a clear business case. M-ENGINE's primary market focus will be data centers, but it will have the flexibility to address related markets, such as photonic computing. The consortium aims to create a viable solution in 5 years when the market is expected to be valued at €14Bn.

Video, mobile and cloud have driven network bandwidth to grow at astonishing rates, estimated at about 40% growth year over year. Between 2000 and 2009 channel data rates in commercial optical fibre networks were peaking at 10Gb/s. Introduction of coherent technology caused a step change and around 2011 rates jumped to 100Gb/s for long haul transmission. It was enabled by advances in digital signal processing, narrow linewidth lasers, coherent receivers and optical modulators. Such modulators are used to control both the level and phase of the optical signal to send data and are the main topic of this proposal. State of the art modulators are based on a common optical transmitter circuit (a Mach-Zehnder Modulator) to encode the data. In 2012 researchers at the Univ. Southampton invented a new device and applied for a patent on the concept. It is based on optical injection locking (OIL) and allows directly modulated lasers to be used to encode the data. Demonstration systems were developed in partnership with Eblana Photonics who provided the OIL laser devices and is the exclusive licensee to the patent IP. Eblana, established in Dublin in 2001, provides laser sources for sensing and high volume comms applications. The technique has received strong interest from the scientific community and, significantly, network system manufacturers who have stated eagerness to trial prototypes. Differentiators are that the output is linear, drive electronics are fundamentally simpler and uses half the number of high speed connections compared to the competition. Key advantages include low cost, low power consumption and amenable to miniaturisation to very small sizes. Coherent 100G transmission will migrate to shorter reach and wider usage in metropolitan networks (60-800km range) and in enterprise and access at shorter distances and these are the market focus. Eblana Photonics will exploit this technology to become the highest volume supplier of such modulators in the world.

The centre will focus on negative emission technologies. Most climate policy specialists in the UK and around the world consider these will be essential to mitigate the worst impacts of climate change. At present the Supergen Bioenergy hub has 2 research projects on BECCS (focused on gasification), the Oxford based greenhouse removal hub works with 4 demonstrators (on biochar, peatlands, enhanced weathering and afforestation), all focused on academic research in UK institutes. This project will work with both Supergen and the GGR Hub (as well as the dmonstrators which have Nottingham and Aston leadership and participation) to expand the research to the currently neglected areas of engineered GGR solutions. The scale and level of activity often makes it difficult for individual universiteis to engage fully in the needs of the sector and so the CDT will address that by providing a wide pool of supervisors, facilities and disciplinary perspectives. No other centre currently does this for PhD students. No other centre has or is planned to address the future skills need with the huge anticipated expansion of this centre. The main technological themes are: Direct air capture and CO2 storage Direct air capture and CO2 utilization Biochar synthesis and utilisation Biomass to materials and chemicals CO2 Utilization Biomass to energy with carbon capture and storage