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Advanced GeSn/SiGeSn Group IV Heterostructure Lasers
Growth 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.
Advanced numerical investigation of the heat flux in an array of microbolometers
The 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.
Analogue pattern recognition with stochastic switching binary CMOS-integrated memristive devices
Biological 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).
Influence of plasma treatment on SiO2/Si and Si3N4/Si substrates for large-scale transfer of graphene
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.
Neuromorphic on-chip recognition of saliva samples of COPD and healthy controls using memristive devices
Chronic 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).
Restoring a nearly free-standing character of graphene on Ru(0001) by oxygen intercalation
Realization 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.
Structural and electronic properties of epitaxial multilayer h-BN on Ni(111) for spintronics applications
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.
Author Correction: Influence of plasma treatment on SiO2/Si and Si3N4/Si substrates for large-scale transfer of graphene
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.