Tyndall National Institute - Conference Items

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    The IMOCO4.E reference framework for intelligent motion control systems
    (Institute of Electrical and Electronics Engineers (IEEE), 2023-10-12) Mohamed, Sajid; van der Veen, Gijs; Kuppens, Hans; Vierimaa, Matias; Kanellos, Tassos; Stoutjesdijk, Henry; Masiero, Riccardo; Määttä, Kalle; van der Weit, Jan Wytze; Ribeiro, Gabriel; Bergmann, Ansgar; Colombo, Davide; Arenas, Javier; Keary, Alfie; Goubej, Martin; Rouxel, Benjamin; Kilpeläinen, Pekka; Kadikis, Roberts; Armendia, Mikel; Blaha, Petr; Stokkermans, Joep; Čech, Martin; Beltman, Arend-Jan; Horizon 2020
    Intelligent motion control is integral to modern cyber-physical systems. However, smart integration of intelligent motion control with commercial and industrial systems requires domain expertise, industrial ‘know-how’ of the production processes, and resilient adaptation for the various engineering phases. The challenge is amplified with the adoption of advanced digital twin approaches, big data and artificial intelligence in the various industrial domains. This paper proposes the IMOCO4.E reference framework for the smart integration of intelligent motion control with commercial platforms (e.g. from SMEs) and industrial systems. The IMOCO4.E reference framework brings together the architecture, data management, artificial intelligence and digital twin viewpoints from the industrial users of the large-scale ‘Intelligent Motion Control under Industry4.E’ (IMOCO4.E) consortium. The framework envisions a generic platform for designing, developing, and implementing novice and complex motion-controlled industrial systems. Refinements and instantiations of the framework for the IMOCO4.E industrial cases validate the framework’s applicability for various industrial domains throughout the engineering phases and under different constraints imposed on the industrial cases.
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    Atomistic study of Urbach tail energies in (Al,Ga)N quantum well systems
    (Institute of Electrical and Electronics Engineers (IEEE), 2023-10-09) O’Donovan, Michael; Finn, Robert; Schulz, Stefan; Koprucki, Thomas; Leibniz-Gemeinschaft; Science Foundation Ireland; Sustainable Energy Authority of Ireland
    Aluminium gallium nitride is a system of interest for developing ultraviolet (UV) optoelectronic devices. Here Urbach tails induced by carrier localization effects play a key role in determining device behaviour. We study the electronic structure of Al x Ga 1−x N/Al y Ga 1−y N single quantum wells using an atomistic framework. Results show that the density of states exhibits a tail at low energies due to disorder in the alloy microstructure. Our analysis allows for insight into the orbital character of the states forming the Urbach tails, which can affect light polarization characteristics, and important quantity for deep UV light emitters.
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    Theoretical investigation of carrier transport and recombination processes for deep UV (Al,Ga)N light emitters
    (Institute of Electrical and Electronics Engineers (IEEE), 2023-10-09) Finn, Robert; O’Donovan, Michael; Farrell, Patricio; Streckenbach, Timo; Moatti, Julien; Koprucki, Thomas; Schulz, Stefan; Sustainable Energy Authority of Ireland; Science Foundation Ireland; Leibniz-Gemeinschaft; Labex
    We present a theoretical study on the impact of alloy disorder on carrier transport and recombination rates in an (Al,Ga)N single quantum well based LED operating in the deep UV spectral range. Our calculations indicate that alloy fluctuations enable ‘percolative pathways’ which can result in improved carrier injection into the well, but may also increase carrier leakage from the well. Additionally, we find that alloy disorder induces carrier localization effects, a feature particularly noticeable for the holes. These localization effects can lead to locally increased carrier densities when compared to a virtual crystal approximation which neglects alloy disorder. We observe that both radiative and non-radiative recombination rates are increased. Our calculations also indicate that Auger-Meitner recombination increases faster than the radiative rate, based on a comparison with a virtual crystal approximation.
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    Investigating the competition of radiative and nonradiative recombination in (In,Ga)N quantum wells
    (Institute of Electrical and Electronics Engineers (IEEE), 2023-10-09) Schulz, Stefan; McMahon, Joshua; Kioupakis, E.; Barrett, R. M.; Ahumada-Lazo, R.; Alanis, J. A .; Parkinson, P.; Kappers, M. J.; Oliver, R. A.; Binks, D.; Science Foundation Ireland; University of Michigan; Engineering and Physical Sciences Research Council; UK Research and Innovation
    We present a combined theoretical and experimental analysis of Auger recombination in c-plane (In,Ga)N quantum wells. On the theoretical side we use an atomistic model that accounts for random alloy fluctuations to investigate the impact that temperature and carrier density has on the radiative and Auger recombination rate. Our calculations indicate a weak temperature dependence of the Auger rate compared to the temperature dependence of the radiative rate. However, with increasing carrier density the Auger rate increases more strongly when compared to the radiative rate. Our theory results indicate an onset of the efficiency drop at carrier densities ≳ 1×10 19 cm −3 , in very good agreement with our photoluminescence studies on similar (In,Ga)N quantum well samples. Overall, we find that alloy enhanced Auger recombination is sufficient to explain the experimental data investigated here.
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    Deep reinforcement learning for combined coverage and resource allocation in UAV-aided RAN-slicing
    (Institute of Electrical and Electronics Engineers (IEEE), 2023-09-27) Bellone, Lorenzo; Galkin, Boris; Traversi, Emiliano; Natalizio, Enrico
    Network slicing is a well assessed approach enabling virtualization of the mobile core and radio access network (RAN) in the emerging 5th Generation New Radio. Slicing is of paramount importance when dealing with the emerging and diverse vertical applications entailing heterogeneous sets of requirements. 5G is also envisioning Unmanned Aerial Vehicles (UAVs) to be a key element in the cellular network standard, aiming at their use as aerial base stations and exploiting their flexible and quick deployment to enhance the wireless network performance. This work presents a UAV-assisted 5G network, where the aerial base stations (UAV-BS) are empowered with network slicing capabilities aiming at optimizing the Service Level Agreement (SLA) satisfaction ratio of a set of users. The users belong to three heterogeneous categories of 5G service type, namely, enhanced mobile broadband (eMBB), ultra-reliable low-latency communication (URLLC), and massive machine-type communication (mMTC). A first application of multi-agent and multi-decision deep reinforcement learning for UAV-BS in a network slicing context is introduced, aiming at the optimization of the SLA satisfaction ratio of users through the joint allocation of radio resources to slices and refinement of the UAV-BSs 2-dimensional trajectories. The performance of the presented strategy have been tested and compared to benchmark heuristics, highlighting a higher percentage of satisfied users (at least 10.5% more) in a variety of scenarios.