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- ItemNanoscale Mapping of the 3D Strain Tensor in a Germanium Quantum Well Hosting a Functional Spin Qubit Device(Washington, DC : Soc., 2023) Corley-Wiciak, Cedric; Richter, Carsten; Zoellner, Marvin H.; Zaitsev, Ignatii; Manganelli, Costanza L.; Zatterin, Edoardo; Schülli, Tobias U.; Corley-Wiciak, Agnieszka A.; Katzer, Jens; Reichmann, Felix; Klesse, Wolfgang M.; Hendrickx, Nico W.; Sammak, Amir; Veldhorst, Menno; Scappucci, Giordano; Virgilio, Michele; Capellini, GiovanniA strained Ge quantum well, grown on a SiGe/Si virtual substrate and hosting two electrostatically defined hole spin qubits, is nondestructively investigated by synchrotron-based scanning X-ray diffraction microscopy to determine all its Bravais lattice parameters. This allows rendering the three-dimensional spatial dependence of the six strain tensor components with a lateral resolution of approximately 50 nm. Two different spatial scales governing the strain field fluctuations in proximity of the qubits are observed at <100 nm and >1 μm, respectively. The short-ranged fluctuations have a typical bandwidth of 2 × 10-4 and can be quantitatively linked to the compressive stressing action of the metal electrodes defining the qubits. By finite element mechanical simulations, it is estimated that this strain fluctuation is increased up to 6 × 10-4 at cryogenic temperature. The longer-ranged fluctuations are of the 10-3 order and are associated with misfit dislocations in the plastically relaxed virtual substrate. From this, energy variations of the light and heavy-hole energy maxima of the order of several 100 μeV and 1 meV are calculated for electrodes and dislocations, respectively. These insights over material-related inhomogeneities may feed into further modeling for optimization and design of large-scale quantum processors manufactured using the mainstream Si-based microelectronics technology.
- ItemElectron Transport across Vertical Silicon/MoS2/Graphene Heterostructures: Towards Efficient Emitter Diodes for Graphene Base Hot Electron Transistors(Washington, DC : ACS Publications, 2020) Belete, Melkamu; Engström, Olof; Vaziri, Sam; Lippert, Gunther; Lukosius, Mindaugas; Kataria, Satender; Lemme, Max C.Heterostructures comprising silicon, molybdenum disulfide (MoS2), and graphene are investigated with respect to the vertical current conduction mechanism. The measured current-voltage (I-V) characteristics exhibit temperature-dependent asymmetric current, indicating thermally activated charge carrier transport. The data are compared and fitted to a current transport model that confirms thermionic emission as the responsible transport mechanism across devices. Theoretical calculations in combination with the experimental data suggest that the heterojunction barrier from Si to MoS2 is linearly temperature-dependent for T = 200-300 K with a positive temperature coefficient. The temperature dependence may be attributed to a change in band gap difference between Si and MoS2, strain at the Si/MoS2 interface, or different electron effective masses in Si and MoS2, leading to a possible entropy change stemming from variation in density of states as electrons move from Si to MoS2. The low barrier formed between Si and MoS2 and the resultant thermionic emission demonstrated here make the present devices potential candidates as the emitter diode of graphene base hot electron transistors for future high-speed electronics. Copyright © 2020 American Chemical Society.
- ItemModulation Linearity Characterization of Si Ring Modulators(Washington, DC : OSA, 2021) Jo, Youngkwan; Mai, Christian; Lischke, Stefan; Zimmermann, Lars; Choi, Woo-YoungModulation linearity of Si ring modulators (RMs) is investigated through the numerical simulation based on the coupled-mode theory and experimental verification. Numerical values of the key parameters needed for the simulation are experimentally extracted. Simulation and measurement results agree well. With these, the influence of input optical wavelength and power on the Si RM linearity are characterized.
- ItemSelective electrodeposition of indium microstructures on silicon and their conversion into InAs and InSb semiconductors(New York, NY [u.a.] : Springer, 2023) Hnida-Gut, Katarzyna E.; Sousa, Marilyne; Tiwari, Preksha; Schmid, HeinzAbstract: The idea of benefitting from the properties of III-V semiconductors and silicon on the same substrate has been occupying the minds of scientists for several years. Although the principle of III-V integration on a silicon-based platform is simple, it is often challenging to perform due to demanding requirements for sample preparation rising from a mismatch in physical properties between those semiconductor groups (e.g. different lattice constants and thermal expansion coefficients), high cost of device-grade materials formation and their post-processing. In this paper, we demonstrate the deposition of group-III metal and III-V semiconductors in microfabricated template structures on silicon as a strategy for heterogeneous device integration on Si. The metal (indium) is selectively electrodeposited in a 2-electrode galvanostatic configuration with the working electrode (WE) located in each template, resulting in well-defined In structures of high purity. The semiconductors InAs and InSb are obtained by vapour phase diffusion of the corresponding group-V element (As, Sb) into the liquified In confined in the template. We discuss in detail the morphological and structural characterization of the synthesized In, InAs and InSb crystals as well as chemical analysis through scanning electron microscopy (SEM), scanning transmission electron microscopy (TEM/STEM), and energy-dispersive X-ray spectroscopy (EDX). The proposed integration path combines the advantage of the mature top-down lithography technology to define device geometries and employs economic electrodeposition (ED) and vapour phase processes to directly integrate difficult-to-process materials on a silicon platform. Graphical abstract: [Figure not available: see fulltext.].
- 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.
- 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.
- 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).