Erscheinungsjahr 2022

The prediction of turbulent flow by the application of machine learning (ML) algorithms to big data is a concept currently in its infancy which requires further development. It is of special importance if the aim is a prediction that is good in a statistical sense or if the vector fields should be predicted as good as possible. For this purpose, the statistical and deterministic prediction of the unsteady but periodic flow of the von Kármán Vortex Street (KVS) was examined using an Echo State Network (ESN) which is well suited for learning from time series due to its recurrent connections. The experimental data of the velocity field of the KVS were collected by Particle Image Velocimetry (PIV). Then, the data were reduced by Proper Orthogonal Decomposition (POD) and the flow was reconstructed by the first hundred most energetic modes. An ESN with 3000 neurons was optimized with respect to its three main hyperparameters to predict the time coefficients of the POD modes. For the deterministic prediction, the aim was to maximize the correct direction of the vertical velocities. The results indicate that the ESN can mimic the periodicity and the unsteadiness of the flow. It is also able to predict the sequence of the upward and downward directed velocities for longer time spans. For the statistical prediction, the similarity of the probability density functions of the vertical velocity fields between the predicted and actual flow was achieved. The leaking rate of the ESN played a key role in the transition from deterministic to statistical predictions.

In recent years, various depth sensors that are small enough to be used with mobile hardware have been introduced. They provide important information for use cases like 3D reconstruction or in the context of augmented reality where tracking and camera data alone would be insufficient. However, depth sensors may not always be available due to hardware limitations or when simulating augmented reality applications for prototyping purposes. In these cases, different approaches like stereo matching or depth estimation using neural networks may provide a viable alternative. In this paper, we therefore explore the imitation of depth sensors using deep neural networks. For this, we use a state-of-the-art network for depth estimation and adapt it in order to mimic a Structure Sensor as well as an iPad LiDAR sensor. We evaluate the network which was pre-trained on NYU V2 directly as well as several variations where transfer learning is applied in order to adapt the network to different depth sensors while using various data preprocessing and augmentation techniques. We show that a transfer learning approach together with appropriate data processing can enable an accurate modeling of the respective depth sensors.

Redox-based memristive devices have shown great potential for application in neuromorphic computing systems. However, the demands on the device characteristics depend on the implemented computational scheme and unifying the desired properties in one stable device is still challenging. Understanding how and to what extend the device characteristics can be tuned and stabilized is crucial for developing application specific designs. Here, we present memristive devices with a functional trilayer of HfOx/Al2O3/TiO2 tailored by the stoichiometry of HfOx (x = 1.8, 2) and the operating conditions. The device properties are experimentally analyzed, and a physics-based device model is developed to provide a microscopic interpretation and explain the role of the Al2O3 layer for a stable performance. Our results demonstrate that the resistive switching mechanism can be tuned from area type to filament type in the same device, which is well explained by the model: the Al2O3 layer stabilizes the area-type switching mechanism by controlling the formation of oxygen vacancies at the Al2O3/HfOx interface with an estimated formation energy of ≈ 1.65 ± 0.05 eV. Such stabilized area-type devices combine multi-level analog switching, linear resistance change, and long retention times (≈ 107-108 s) without external current compliance and initial electroforming cycles. This combination is a significant improvement compared to previous bilayer devices and makes the devices potentially interesting for future integration into memristive circuits for neuromorphic applications.

Well-defined hetero-interfaces with controlled properties are crucial for any high-performance, semiconductor-based, (opto-)electronic device. They are particularly important for device structures on the nanoscale with increased interfacial areas. Utilizing a ultrahigh-vacuum based multi-tip scanning tunneling microscope, this work reveals inadvertent conductivity channels between the nanowire (NW) base and the substrate, when measuring individual vertical core-shell III-V-semiconductor NWs. For that, four-terminal probing is applied on freestanding, epitaxially grown coaxial p-GaAs/i-GaInP/n-GaInP NWs without the need of nanoscale lithography or deposition of electrical contacts. This advanced analysis, carried out after composition-selective wet chemical etching, reveals a substantially degraded electrical performance of the freestanding NWs compared to detached ones. In an electron beam induced current mode of the nanosensor, charge separation at the substrate-to-NW base junction is demonstrated. An energy dispersive X-ray spectroscopic linescan shows an unintended compositional change of the epitaxially grown NW toward the planar layers caused by different incorporation mechanisms of Ga and In at the NW base. This approach provides direct insight into the NW-substrate transition area and leads to a model of the conductivity channels at the NW base, which should, in principle, be considered in the fabrication of all NW heterostructures grown bottom-up on heterogeneous substrate materials.

Range-resolved interferometry (RRI) allows the simultaneous demodulation of multiple interferometric signal sources and provides a tomographic view of all constituent interferometers that may be present in a setup. Through comparison with a reference distance of known length, absolute distance measurements can be performed. RRI is tailored to the use of laser frequency modulation through injection-current modulation of regular, monolithic laser diodes that are both cost-effective and highly coherent and therefore this approach promises broad applicability. In this paper, two methods for absolute distance measurement, one based on the direct evaluation of the signal peak positions and one based on the phase demodulation of an additional lock-in modulation signal, are experimentally demonstrated. Using an external verification displacement interferometer, both techniques are shown to achieve in-situ absolute distance measurements with systematic errors below over a 50 mm travel range. The aim of this paper is to establish the general suitability of RRI for absolute distance measurements and in-situ tomographic interferometer characterisation for precision engineering. In future, this approach could be used to diagnose interferometric setups for parasitic signal contributions, multiple reflections or to determine the dead path length for accurate environmental compensation, either for use during initial setup of, or for continuous operation alongside, a regular displacement measuring interferometer.