Monitoring MBE substrate deoxidation via RHEED image-sequence analysis by deep learning

Reflection high-energy electron diffraction (RHEED) is a powerful tool in molecular beam epitaxy (MBE), but RHEED images are often difficult to interpret, requiring experienced operators. We present an approach for automated surveillance of GaAs substrate deoxidation in MBE using deep learning based RHEED image-sequence classification. Our approach consists of an non-supervised auto-encoder (AE) for feature extraction, combined with a supervised convolutional classifier network. We demonstrate that our lightweight network model can accurately identify the exact deoxidation moment. Furthermore we show that the approach is very robust and allows accurate deoxidation detection during months without requiring re-training. The main advantage of the approach is that it can be applied to raw RHEED images without requiring further information such as the rotation angle, temperature, etc.Comment: 6 pages, 6 figure

Towards high mobility InSb nanowire devices

We study the low-temperature electron mobility of InSb nanowires. We extract the mobility at 4.2 Kelvin by means of field effect transport measurements using a model consisting of a nanowire-transistor with contact resistances. This model enables an accurate extraction of device parameters, thereby allowing for a systematic study of the nanowire mobility. We identify factors affecting the mobility, and after optimization obtain a field effect mobility of $\sim2.5\mathbin{\times}10^4$ cm$^2$/Vs. We further demonstrate the reproducibility of these mobility values which are among the highest reported for nanowires. Our investigations indicate that the mobility is currently limited by adsorption of molecules to the nanowire surface and/or the substrate.Comment: 13 pages, 5 figures (main text); 7 pages, 2 figures, 2 tables (supplementary text

Quantum computing based on semiconductor nanowires

A quantum computer will have computational power beyond that of conventional computers, which can be exploited for solving important and complex problems, such as predicting the conformations of large biological molecules. Materials play a major role in this emerging technology, as they can enable sophisticated operations, such as control over single degrees of freedom and their quantum states, as well as preservation and coherent transfer of these states between distant nodes. Here we assess the potential of semiconductor nanowires grown from the bottom-up as a materials platform for a quantum computer. We review recent experiments in which small bandgap nanowires are used to manipulate single spins in quantum dots and experiments on Majorana fermions, which are quasiparticles relevant for topological quantum computing

From InSb Nanowires to Nanocubes: Looking for the Sweet Spot

High aspect ratios are highly desired to fully exploit the one-dimensional properties of indium antimonide nanowires. Here we systematically investigate the growth mechanisms and find parameters leading to long and thin nanowires. Variation of the V/III ratio and the nanowire density are found to have the same influence on the “local” growth conditions and can control the InSb shape from thin nanowires to nanocubes. We propose that the V/III ratio controls the droplet composition and the radial growth rate and these parameters determine the nanowire shape. A sweet spot is found for nanowire interdistances around 500 nm leading to aspect ratios up to 35. High electron mobilities up to 3.5 × 10^4 cm^2 V^(–1) s^(–1) enable the realization of complex spintronic and topological devices

Monitoring MBE substrate deoxidation via RHEED image-sequence analysis by deep learning

6 pages, 6 figuresReflection high-energy electron diffraction (RHEED) is a powerful tool in molecular beam epitaxy (MBE), but RHEED images are often difficult to interpret, requiring experienced operators. We present an approach for automated surveillance of GaAs substrate deoxidation in MBE using deep learning based RHEED image-sequence classification. Our approach consists of an non-supervised auto-encoder (AE) for feature extraction, combined with a supervised convolutional classifier network. We demonstrate that our lightweight network model can accurately identify the exact deoxidation moment. Furthermore we show that the approach is very robust and allows accurate deoxidation detection during months without requiring re-training. The main advantage of the approach is that it can be applied to raw RHEED images without requiring further information such as the rotation angle, temperature, etc

Monitoring MBE substrate deoxidation via RHEED image-sequence analysis by deep learning

6 pages, 6 figuresReflection high-energy electron diffraction (RHEED) is a powerful tool in molecular beam epitaxy (MBE), but RHEED images are often difficult to interpret, requiring experienced operators. We present an approach for automated surveillance of GaAs substrate deoxidation in MBE using deep learning based RHEED image-sequence classification. Our approach consists of an non-supervised auto-encoder (AE) for feature extraction, combined with a supervised convolutional classifier network. We demonstrate that our lightweight network model can accurately identify the exact deoxidation moment. Furthermore we show that the approach is very robust and allows accurate deoxidation detection during months without requiring re-training. The main advantage of the approach is that it can be applied to raw RHEED images without requiring further information such as the rotation angle, temperature, etc

Twin-induced InSb nanosails : a convenient high mobility quantum system

Ultra narrow bandgap III-V semiconductor nanomaterials provide a unique platform for realizing advanced nanoelectronics, thermoelectrics, infrared photodetection, and quantum transport physics. In this work we employ molecular beam epitaxy to synthesize novel nanosheet-like InSb nanostructures exhibiting superior electronic performance. Through careful morphological and crystallographic characterization we show how this unique geometry is the result of a single twinning event in an otherwise pure zinc blende structure. Four-terminal electrical measurements performed in both the Hall and van der Pauw configurations reveal a room temperature electron mobility greater than 12 000 cm²·V⁻¹·s⁻¹. Quantized conductance in a quantum point contact processed with a split-gate configuration is also demonstrated. We thus introduce InSb "nanosails" as a versatile and convenient platform for realizing new device and physics experiments with a strong interplay between electronic and spin degrees of freedom

Experimental phase diagram of zero-bias conductance peaks in superconductor/semiconductor nanowire devices

Topological superconductivity is an exotic state of matter characterized by spinless p-wave Cooper pairing of electrons and by Majorana zero modes at the edges. The first signature of topological superconductivity is a robust zero-bias peak in tunneling conductance. We perform tunneling experiments on semiconductor nanowires (InSb) coupled to superconductors (NbTiN) and establish the zero-bias peak phase in the space of gate voltage and external magnetic field. Our findings are consistent with calculations for a finite-length topological nanowire and provide means for Majorana manipulation as required for braiding and topological quantum bits.QN/Bakkers LabQuTec

Observation of Conductance Quantization in InSb Nanowire Networks

International audienceMajorana zero modes (MZMs) are prime candidates for robust topological quantum bits, holding a great promise for quantum computing. Semiconducting nanowires with strong spin orbit coupling offer a promising platform to harness one-dimensional electron transport for Majorana physics. Demonstrating the topological nature of MZMs relies on braiding, accomplished by moving MZMs around each other in a certain sequence. Most of the proposed Majorana braiding circuits require nanowire networks with minimal disorder. Here, the electronic transport across a junction between two merged InSb nanowires is studied to investigate how disordered these nanowire networks are. Conductance quantization plateaus are observed in most of the contact pairs of the epitaxial InSb nanowire networks: the hallmark of ballistic transport behavior