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- ItemModular coherent photonic-aided payload receiver for communications satellites([London] : Nature Publishing Group UK, 2019) Duarte, Vanessa C.; Prata, João G.; Ribeiro, Carlos F.; Nogueira, Rogério N.; Winzer, Georg; Zimmermann, Lars; Walker, Rob; Clements, Stephen; Filipowicz, Marta; Napierała, Marek; Nasiłowski, Tomasz; Crabb, Jonathan; Kechagias, Marios; Stampoulidis, Leontios; Anzalchi, Javad; Drummond, Miguel V.Ubiquitous satellite communications are in a leading position for bridging the digital divide. Fulfilling such a mission will require satellite services on par with fibre services, both in bandwidth and cost. Achieving such a performance requires a new generation of communications payloads powered by large-scale processors, enabling a dynamic allocation of hundreds of beams with a total capacity beyond 1 Tbit s−1. The fact that the scale of the processor is proportional to the wavelength of its signals has made photonics a key technology for its implementation. However, one last challenge hinders the introduction of photonics: while large-scale processors demand a modular implementation, coherency among signals must be preserved using simple methods. Here, we demonstrate a coherent photonic-aided receiver meeting such demands. This work shows that a modular and coherent photonic-aided payload is feasible, making way to an extensive introduction of photonics in next generation communications satellites.
- ItemStructural and electronic properties of epitaxial multilayer h-BN on Ni(111) for spintronics applications([London] : Macmillan Publishers Limited, part of Springer Nature, 2016) Tonkikh, A.A.; Voloshina, E.N.; Werner, P.; Blumtritt, H.; Senkovskiy, B.; Güntherodt, G.; Parkin, S.S.P.; Dedkov, Yu. S.Hexagonal boron nitride (h-BN) is a promising material for implementation in spintronics due to a large band gap, low spin-orbit coupling, and a small lattice mismatch to graphene and to close-packed surfaces of fcc-Ni(111) and hcp-Co(0001). Epitaxial deposition of h-BN on ferromagnetic metals is aimed at small interface scattering of charge and spin carriers. We report on the controlled growth of h-BN/Ni(111) by means of molecular beam epitaxy (MBE). Structural and electronic properties of this system are investigated using cross-section transmission electron microscopy (TEM) and electron spectroscopies which confirm good agreement with the properties of bulk h-BN. The latter are also corroborated by density functional theory (DFT) calculations, revealing that the first h-BN layer at the interface to Ni is metallic. Our investigations demonstrate that MBE is a promising, versatile alternative to both the exfoliation approach and chemical vapour deposition of h-BN.
- ItemAdvanced numerical investigation of the heat flux in an array of microbolometers([London] : Macmillan Publishers Limited, part of Springer Nature, 2019) Stocchi, Matteo; Mencarelli, Davide; Pierantoni, Luca; Göritz, Alexander; Kaynak, Canan Baristiran; Wietstruck, Matthias; Kaynak, MehmetThe investigation of the thermal properties of an array of microbolometers has been carried out by mean of two independent numerical analysis, respectively the Direct-Simulation Monte Carlo (DSMC) and the classic diffusive approach of the Fourier's equation. In particular, the thermal dissipation of a hot membrane placed in a low-pressure cavity has been studied for different values of the temperature of the hot body and for different values of the pressure of the environment. The results for the heat flux derived from the two approaches have then been compared and discussed.
- ItemNeuromorphic on-chip recognition of saliva samples of COPD and healthy controls using memristive devices([London] : Macmillan Publishers Limited, part of Springer Nature, 2020) Zarrin, Pouya Soltani; Zahari, Finn; Mahadevaiah, Mamathamba K.; Perez, Eduardo; Kohlstedt, Hermann; Wenger, ChristianChronic Obstructive Pulmonary Disease (COPD) is a life-threatening lung disease, affecting millions of people worldwide. Implementation of Machine Learning (ML) techniques is crucial for the effective management of COPD in home-care environments. However, shortcomings of cloud-based ML tools in terms of data safety and energy efficiency limit their integration with low-power medical devices. To address this, energy efficient neuromorphic platforms can be used for the hardware-based implementation of ML methods. Therefore, a memristive neuromorphic platform is presented in this paper for the on-chip recognition of saliva samples of COPD patients and healthy controls. Results of its performance evaluations showed that the digital neuromorphic chip is capable of recognizing unseen COPD samples with accuracy and sensitivity values of 89% and 86%, respectively. Integration of this technology into personalized healthcare devices will enable the better management of chronic diseases such as COPD. © 2020, The Author(s).
- ItemAnalogue pattern recognition with stochastic switching binary CMOS-integrated memristive devices([London] : Macmillan Publishers Limited, part of Springer Nature, 2020) Zahari, Finn; Pérez, Eduardo; Mahadevaiah, Mamathamba Kalishettyhalli; Kohlstedt, Hermann; Wenger, Christian; Ziegler, MartinBiological neural networks outperform current computer technology in terms of power consumption and computing speed while performing associative tasks, such as pattern recognition. The analogue and massive parallel in-memory computing in biology differs strongly from conventional transistor electronics that rely on the von Neumann architecture. Therefore, novel bio-inspired computing architectures have been attracting a lot of attention in the field of neuromorphic computing. Here, memristive devices, which serve as non-volatile resistive memory, are employed to emulate the plastic behaviour of biological synapses. In particular, CMOS integrated resistive random access memory (RRAM) devices are promising candidates to extend conventional CMOS technology to neuromorphic systems. However, dealing with the inherent stochasticity of resistive switching can be challenging for network performance. In this work, the probabilistic switching is exploited to emulate stochastic plasticity with fully CMOS integrated binary RRAM devices. Two different RRAM technologies with different device variabilities are investigated in detail, and their potential applications in stochastic artificial neural networks (StochANNs) capable of solving MNIST pattern recognition tasks is examined. A mixed-signal implementation with hardware synapses and software neurons combined with numerical simulations shows that the proposed concept of stochastic computing is able to process analogue data with binary memory cells. © 2020, The Author(s).
- ItemRestoring a nearly free-standing character of graphene on Ru(0001) by oxygen intercalation([London] : Macmillan Publishers Limited, part of Springer Nature, 2016) Voloshina, Elena; Berdunov, Nikolai; Dedkov, YuriyRealization of a free-standing graphene is always a demanding task. Here we use scanning probe microscopy and spectroscopy to study the crystallographic structure and electronic properties of the uniform nearly free-standing graphene layers obtained by intercalation of oxygen monolayer in the “strongly” bonded graphene/Ru(0001) interface. Spectroscopic data show that such graphene layer is heavily p-doped with the Dirac point located at 552 meV above the Fermi level. Experimental data are understood within density-functional theory approach and the observed effects are in good agreement with the theoretical data.