This proposal addresses the two major roadblocks in the development of graphene for high-performance nano-optoelectronics, namely how to efficiently and reliably integrate them in pristine conditions in electronic devices, and how harness the exceptional properties of graphene. Specifically, proof of principle of ultra-thin body tunnel field effect transistors (UB-TFET) are proposed consisting of two-dimensional (2D) all epitaxial graphene/boron nitride heterostructures with a viable large scale integration scheme. Tunnel transistors are an efficient alternative to standard field effect transistors designs that are inefficient for graphene because of the lack of a bandgap. Importantly UB-TFET should overcome the thermal limitation of thermioic sub-threshold swing in common transistors. The TFET will be based on epitaxial graphene on SiC (epigraphene, or EG)/BN structures; the most advanced implementation will utilize the recently discovered exceptional conductance properties of epigraphene nano-ribbons that are quantized single channel ballistic conductors at room temperature. But having excellent graphene is far from having a device and the active component has to be integrated. This project is based on the fundamental realization that only (hetero)-epitaxial growth can provide the required atomic control for reliable devices. Epitaxial growth insures clean interfaces and precise orientation of the stacked layers, avoiding trapped molecules and the randomness inherent to layer transfer. However, despite this absolute requirement, very little progress has been made up to now to grow large 2D dielectric on graphene; most dielectric deposition needs chemical modification of the graphene surface for adhesion, which invariably compromises the graphene electronic performance. Hexagonal boron nitride (h-BN) layers is considered the best substrate for graphene, but only micron size BN flakes are available, making the integration tedious, unreliable and impossible at large scale. In this proposal we will grow h-BN epitaxialy on epigraphene by metalorganic vapor phase epitaxy (MOVPE). As demonstrated in preliminary work by this three-team partnership, this technique provides exceptional unmatched graphene/h-BN epitaxial interfaces as required for high performance electronics, and immediate upscaling capabilities. The SiC/EG/h-BN heterostructure will give access to graphene properties in an exceptionally reproducible and clean environment, not otherwise available. Growth conditions will be investigated to produce ultra thin h-BN on epigraphene, which have not been achieved yet. This proposal will then follow two tracks to build UB-TFETs, demonstrating proof of principle of vertical and lateral BN/EG-based FETs. Our ultimate goal is to combine ballistic epigraphene nanoribbons in tunneling devices to enable a new generation of electronic devices. This is an extremely promising alternative to the standard FET paradigm that can enable ultra-high frequency operation as well as low power operation. This project is a tight well-focused partnership between three teams with a history of highly successful collaboration and perfect complementarity: CNRS-Institut Néel (Grenoble), CNRS/ONERA-Laboratoire d’Etude des Matériaux (Châtillon), and CNRS/Georgia Institute of Technology -UMI 2958 (Metz, in collaboration with GT Atlanta). We will build up on the important milestone of epitaxial h-BN growth on EG, towards critical development including ultra-thin BN and fabrication of tunnel transistors devices. IN will be in charge of providing epigraphene, will design and realized transistor devices and perform transport measurements; the UMI team will produce the BN epitaxial film and provide basic structural study for rapid optimization of the growth process; LEM will perform advanced structural and optical studies, in particular HR-TEM studies, critical to the layer characterization of ultra thin 2D films.

While silicon-based solar cell technologies dominate the photovoltaic (PV) market today, their performance is limited. Indeed, the world record efficiency for Si-based PVs has been static at 25% for several years now. III-V multijunction PVs, on the other hand, have recently attained efficiencies > 40% and new record performances emerge regularly. Although tandem PV geometries have been developed combining crystalline and amorphous silicon, it has not been possible so far to form devices with efficiencies to rival III-V multijunctions. NOVAGAINS aims to benefit from combining the maturity of the Si technology with the potential efficiency gains associated with IIIV PV through the development of a novel tandem PV involving the integration of an InGaN based junction on a monocrystalline Si junction by means of a compliant ZnO interfacial template layer which doubles as a tunnel junction. Although the (In)GaN alloy has been used extensively in LEDs, its’ use in solar cell technology has drawn relatively little attention. Nevertheless, the InGaN materials system offers a huge potential to develop superior efficiency PV devices. The primary advantage of InGaN is the direct-band gap, which can be tuned to cover a range from 0.7 eV to 3.4 eV. As such, this is the only system which encompasses as much of the solar spectrum. Indeed, the fact that InGaN can provide such tunability of the bandgap means that PV conversion efficiencies greater than 50% can be anticipated. Unfortunately, it is very difficult to grow GaN based films of high materials quality directly on Si because they do not have a good crystallographic match. ZnO can be grown more readily on such substrates, however, because of its’ more compliant nature. Indeed, well-crystallized and highly-oriented ZnO can even be grown directly on the native amorphous SiO2 layer. Since ZnO shares the same wurtzite structure as GaN and there is less than 2% lattice mismatch it has been demonstrated that it is then possible to grow InGaN/GaN epitaxially on ZnO/Si using the specialized know-how offered by the consortium. Modeling indicates that when optimized, stacked InGaN and Si cells coupled by tunneling through a ZnO interlayer offer the perspective of tandem cells with overall solar conversion efficiencies in excess of 30%.

In order to reduce carbon emissions and mitigate the effects of climate change, the energy sector requires an urgent energy transition at a global scale. In the domain of photovoltaics, despite the great effort devoted for large scale implementation, price reduction is still the main concern to become fully cost-competitive with traditional energy sources. In this frame, two main parameters can lead to photovoltaic cost-per-Watt reduction, namely higher conversion efficiency and lower production cost. The purpose of INMoSt is the realization of low-cost, high-efficiency, multi-junction solar cells using a single material family, namely III-nitride semiconductors. This target becomes possible by combination of a series of innovative technologies. First, recent developments of the InGaN-nanopyramid growth method have made it possible to enhance the In incorporation in the material which reducing the density of structural defects. Then, the implementation of an h-BN-based simple lift-off and transfer process allow a drastic reduction of the fabrication costs. Finally, the improvement of the conductivity of the p-region and of the p-contact is now possible by depositing Mg-doped layers by molecular-beam epitaxy and using an n+/p+ tunnel contact scheme. The combination of these recent breakthroughs have set the basis for the implementation of low-cost (re-use of the substrate) and high-efficiency InGaN solar cells. The first milestone will be the demonstration of beyond-state-of-the-art, free-standing, and flexible InGaN-based solar cells. This will be realized by the encapsulation into PDMS of the lifted-off solar cells. The ultimate goal will be the fabrication of a stack of such solar cells, each step with a different band gap in order to grant access to a large region of the solar spectrum, and using a process fully compatible with conventional integrated circuit production technology. The INMoSt consortium brings together two partners with complementary experimental and theoretical expertise and capabilities: GT CNRS and CEA-IRIG-PHELIQS. INMoSt researchers possess backgrounds in science and engineering with expertise in experimental and theoretical aspects of nitride materials and nanostructures, growth kinetics, semiconductor fabrication processes, material characterization, and device physics. The functional strategy of the project is based on three main building blocks of technology optimization: simulation and design, epitaxial growth and device fabrication. Assessment of these building blocks will be assisted by material characterization and device tests and measurements. Photovoltaics is becoming a major industry, with constant growth in terms of economic and social benefits. Preparing the next steps of development, in particular the 30-30-30 challenge (production of photovoltaic modules with a >30% energy conversion efficiency for a <30 c$/Wp price by 2030), starting from basic research and innovation is extremely important. INMoSt will provide the first low-cost, high-efficiency, multi-junction solar cells (SC) using a single material family, namely III-nitride semiconductors.

Marine environments undergo rapid changes under the influence of various pressures (human footprint, climate change) and the monitoring of their ecosystem status becomes critical. Such a monitoring requires gathering data, to process them and to extract indicators summarizing the status of the environment that is otherwise too highly dimensional to be grasped by a human being. In recent years, the massive availability of data combined with powerful machine learning algorithms and the associated hardware led to significant advances in domains that were not even dreamed about in the last few years (image classification, automatic translation, text to speech, action selection, ...). Marine ecosystems, where progress has been made in collecting large amounts of data, could also benefit from these AI advances. However, the data in environmental sciences are often sparse either in time, space or relative to the measured variables, and imbalanced which constitute challenges for AI algorithms. This leads to the two directions followed in the SMART-BIODIV proposal: 1) harnessing the power of machine learning algorithms to complete and process sparse and imbalanced data that we often encounter in environmental sciences and 2) designing indicators to qualify the ecological status of the considered environments. Even if the data are scattered, there are several heterogeneous databases that constitute as many points of view that can be combined to build a coherent and complete state of the ecosystem. We will study the potential of interpolation algorithms in time and space as well as predictive models based on co-occurrences. We will also exploit the large image databases collected by the partners on marine plankton and make them available to the challenge participants. More prospectively, we will study the feasibility of including symbolic data, such as food webs, to constrain the evolution of the state of the ecosystem and inject this knowledge of the interdependencies between the dimensions of the state to improve its estimation. These data, grouped, merged and completed, will then serve as a basis for the calculation of taxonomic and trait-based indicators, which will be designed on the basis of our expertise in freshwater bioindication. To reach the challenge’s objectives, our consortium gathers complementary expertises in deep learning, computer vision, oceanography, plankton imaging, and freshwater bioindication. In addition, our experts in AI (GeorgiaTech, CentraleSupelec) and biodiversity (LOV, LIEC) have a strong record of fruitful interdisciplinary collaborations (co-supervised PhD, co-authored articles).

Mobile robots are more and more efficient but limited by the problem of variation of motion properties, as is the case for applications in natural environments. Sharp transitions can be estimated reactively, but are difficult to predict, and lack of anticipation can lead to inappropriate or even hazardous behaviors. This project aims to overcome this problem by proposing adaptive mechanisms for robotic behavior by anticipating these variations from scene perception. The project proposes to develop machine learning approaches to predict and map the interaction conditions. It will also develop stable supervision processes to select and modify on-line several control modes. Tested on realistic scenarios using the robotic platforms available from the project members, such developments will strengthen the autonomy of robots to offer efficient and safe solutions to societal issues, particularly for agriculture.