11 results
Search Results
Now showing 1 - 10 of 11
- ItemAdvanced GeSn/SiGeSn Group IV Heterostructure Lasers(Weinheim : Wiley-VCH, 2018) von den Driesch, Nils; Stange, Daniela; Rainko, Denis; Povstugar, Ivan; Zaumseil, Peter; Capellini, Giovanni; Schröder, Thomas; Denneulin, Thibaud; Ikonic, Zoran; Hartmann, Jean-Michel; Sigg, Hans; Mantl, Siegfried; Grützmacher, Detlev; Buca, DanGrowth and characterization of advanced group IV semiconductor materials with CMOS-compatible applications are demonstrated, both in photonics. The investigated GeSn/SiGeSn heterostructures combine direct bandgap GeSn active layers with indirect gap ternary SiGeSn claddings, a design proven its worth already decades ago in the III–V material system. Different types of double heterostructures and multi-quantum wells (MQWs) are epitaxially grown with varying well thicknesses and barriers. The retaining high material quality of those complex structures is probed by advanced characterization methods, such as atom probe tomography and dark-field electron holography to extract composition parameters and strain, used further for band structure calculations. Special emphasis is put on the impact of carrier confinement and quantization effects, evaluated by photoluminescence and validated by theoretical calculations. As shown, particularly MQW heterostructures promise the highest potential for efficient next generation complementary metal-oxide-semiconductor (CMOS)-compatible group IV lasers.
- ItemGate-controlled quantum dots and superconductivity in planar germanium([London] : Nature Publishing Group UK, 2018) Hendrickx, N.W.; Franke, D.P.; Sammak, A.; Kouwenhoven, M.; Sabbagh, D.; Yeoh, L.; Li, R.; Tagliaferri, M.L.V.; Virgilio, M.; Capellini, G.; Scappucci, G.; Veldhorst, M.Superconductors and semiconductors are crucial platforms in the field of quantum computing. They can be combined to hybrids, bringing together physical properties that enable the discovery of new emergent phenomena and provide novel strategies for quantum control. The involved semiconductor materials, however, suffer from disorder, hyperfine interactions or lack of planar technology. Here we realise an approach that overcomes these issues altogether and integrate gate-defined quantum dots and superconductivity into germanium heterostructures. In our system, heavy holes with mobilities exceeding 500,000 cm2 (Vs)−1 are confined in shallow quantum wells that are directly contacted by annealed aluminium leads. We observe proximity-induced superconductivity in the quantum well and demonstrate electric gate-control of the supercurrent. Germanium therefore has great promise for fast and coherent quantum hardware and, being compatible with standard manufacturing, could become a leading material for quantum information processing.
- 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.
- ItemInfluence of plasma treatment on SiO2/Si and Si3N4/Si substrates for large-scale transfer of graphene([London] : Macmillan Publishers Limited, part of Springer Nature, 2021) Lukose, R.; Lisker, M.; Akhtar, F.; Fraschke, M.; Grabolla, T.; Mai, A.; Lukosius, M.One of the limiting factors of graphene integration into electronic, photonic, or sensing devices is the unavailability of large-scale graphene directly grown on the isolators. Therefore, it is necessary to transfer graphene from the donor growth wafers onto the isolating target wafers. In the present research, graphene was transferred from the chemical vapor deposited 200 mm Germanium/Silicon (Ge/Si) wafers onto isolating (SiO2/Si and Si3N4/Si) wafers by electrochemical delamination procedure, employing poly(methylmethacrylate) as an intermediate support layer. In order to influence the adhesion properties of graphene, the wettability properties of the target substrates were investigated in this study. To increase the adhesion of the graphene on the isolating surfaces, they were pre-treated with oxygen plasma prior the transfer process of graphene. The wetting contact angle measurements revealed the increase of the hydrophilicity after surface interaction with oxygen plasma, leading to improved adhesion of the graphene on 200 mm target wafers and possible proof-of-concept development of graphene-based devices in standard Si technologies.
- 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).
- ItemAuthor Correction: Influence of plasma treatment on SiO2/Si and Si3N4/Si substrates for large-scale transfer of graphene([London] : Macmillan Publishers Limited, part of Springer Nature, 2021) Lukose, R.; Lisker, M.; Akhtar, F.; Fraschke, M.; Grabolla, T.; Mai, A.; Lukosius, M.The original version of this Article omitted an affiliation for M. Lisker. The correct affiliations for M. Lisker are listed below: IHP- Leibniz Institut für innovative Mikroelektronik, Im Technologiepark 25, 15236, Frankfurt (Oder), Germany Technical University of Applied Science Wildau, Hochschulring 1, 15745, Wildau, Germany The original Article and accompanying Supplementary Information file have been corrected.
- ItemEthical Artificial Intelligence in Chemical Research and Development: A Dual Advantage for Sustainability(Dordrecht : Springer Netherlands, 2021) Hermann, Erik; Hermann, Gunter; Tremblay, Jean-ChristopheArtificial intelligence can be a game changer to address the global challenge of humanity-threatening climate change by fostering sustainable development. Since chemical research and development lay the foundation for innovative products and solutions, this study presents a novel chemical research and development process backed with artificial intelligence and guiding ethical principles to account for both process- and outcome-related sustainability. Particularly in ethically salient contexts, ethical principles have to accompany research and development powered by artificial intelligence to promote social and environmental good and sustainability (beneficence) while preventing any harm (non-maleficence) for all stakeholders (i.e., companies, individuals, society at large) affected.
- 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).