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

EPICENTRE : innovations technologiques en épitaxie par jets moléculaires

National audienc

Dopage P de CdxHg(1-x)Te par épitaxie par jets moléculaires

Nous avons étudié le dopage extrinsèque de type P du CdHgTe réalisé par épitaxie par jets moléculaires et avons montré que l'impureté arsenic était le meilleur candidat pour ce dopage. L'incorporation du dopant P lors de la croissance cristalline du matériau a été obtenue grâce à trois sources différentes: une cellule à effusion, une cellule cracker et une cellule plasma. Après un recuit d'activation, les mesures électriques de ces échantillons ont montré un dopage P de quelques 1016 à quelques 1018 porteurs par centimètre cube. En comparant ces mesures et les taux d'arsenic incorporés lors de la croissance il est apparu qu'une grande partie des atomes arsenic n'étaient pas électriquement actifs après recuit. Afin de mieux comprendre les phénomènes mis en jeu lors de la croissance cristalline et lors du recuit d'activation, une campagne de mesures EXAFS a été conduite à l'European Synchrotron Radiation Facility. Nous avons alors montré qu'après croissance l'environnement cristallin des atomes d'arsenic était cristallin alors qu'après recuit cet environnement devenait amorphe.Nous avons enfin réalisé des dispositifs électriques et mis en évidence les premières diodes dopées par ajout d'arsenic.We have studied the extrinsic p-type doping of HgCdTe grown by molecular beam epitaxy and we have demonstrated that arsenic is the best candidate for that kind of doping. The incorporation of arsenic during the crystal growth was achieved thanks to three different sources: an effusion cell, a cracker cell and a plasma cell. After the thermal annealing, we have measured p-type doping levels ranging from 1016 to 1Q18cm-3. Then we have compared these Hall effect measurements with arsenic concentration in the HgCdTe layers, and it appears that most of the arsenic is not electronically active. To understand the origin of this phenomenon, an EXAFS study has been carried out at the European Synchrotron Radiation Facility. This study leads us to consider that after crystal growth,the structure around the arsenic atoms is weil crystallized whereas after thermal annealing the structure becomes amorphous with the predominance of arsenic clusters. We finally obtained our firt PIN diodes extrinsically doped with arsenic.GRENOBLE1-BU Sciences (384212103) / SudocSudocFranceF

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

Effects of crystal phase mixing on the electrical properties of InAs nanowires

We report a systematic study of the relationship between crystal quality and electrical properties of InAs nanowires grown by MOVPE and MBE, with crystal structure varying from wurtzite to zinc blende. We find that mixtures of these phases can exhibit up to 2 orders of magnitude higher resistivity than single-phase nanowires, with a temperature-activated transport mechanism. However, it is also found that defects in the form of stacking faults and twin planes do not significantly affect the resistivity. These findings are important for nanowire-based devices, where uncontrolled formation of particular polytype mixtures may lead to unacceptable device variability