Shape reconstruction from gradient data

We present a novel method for reconstructing the shape of an object from measured gradient data. A certain class of optical sensors does not measure the shape of an object, but its local slope. These sensors display several advantages, including high information efficiency, sensitivity, and robustness. For many applications, however, it is necessary to acquire the shape, which must be calculated from the slopes by numerical integration. Existing integration techniques show drawbacks that render them unusable in many cases. Our method is based on approximation employing radial basis functions. It can be applied to irregularly sampled, noisy, and incomplete data, and it reconstructs surfaces both locally and globally with high accuracy.Comment: 16 pages, 5 figures, zip-file, submitted to Applied Optic

Microdeflectometry - a novel tool to acquire 3D microtopography with nanometer height resolution

We introduce "microdeflectometry", a novel technique for measuring the microtopography of specular surfaces. The primary data is the local slope of the surface under test. Measuring the slope instead of the height implies high information efficiency and extreme sensitivity to local shape irregularities. The lateral resolution can be better than one micron whereas the resulting height resolution is in the range of one nanometer. Microdeflectometry can be supplemented by methods to expand the depth of field, with the potential to provide quantitative 3D imaging with SEM-like features.Comment: 3 pages, 11 figures, latex, zip-file, accepted for publication at Optics Letter

A persistent incremental learning approach for object classification of unseen categories using convolutional neural networks on mobile robots

Neural Networks and especially Convolutional Neural Networks (CNN) show remarkable results in many fields, among others in object classification and recognition. But these networks are limited by the tasks they are trained on, as they are designed to learn all tasks they will need during their lifetime in the beginning and hence are frozen. If now new tasks arrive, the network has to be trained completely new. These networks are therefore usually not able to learn in a continual manner, like humans are capable of. In this work, a novel approach is presented, where a deep neural network is used to continually learn new unseen object categories on images, which can be used in different fields, like mobile robots. First, different architectural strategies are proposed to dynamically adapt the network according to the categories it learns over time. This includes one strategy, where the last layer of our network is adapted and another one where multiple fully-connected layers are created for each new sequence. In order to prevent forgetting, different regularization strategies are shown, including a novel loss function where the classification is replaced by a regression. So, it is ensured that already learned categories are not forgotten by simultaneously enabling the network to learn new categories. Furthermore, the emerging problem of a discrepancy in the output distribution is recognized and different solutions are proposed. This includes a novel regularization strategy, where the outputs are divided by the variance per category. Finally, a novel dataset for continual learning is presented, which is especially suited for object recognition in our mobile robot environment (HOWS-CL-25). It consists of 150,795 synthetic images of 25 different household object categories in a randomly changing environment. Our approach can be classified as online learning, a special variant of incremental learning, where one is limited by the data the network can observe in a specific time step, without the access to previous training examples - also called rehearsal-free. This is a challenging and unsolved problem in comparison to other incremental learning approaches, which also use previous training examples, but as this thesis is focusing on an approach for mobile robots, online learning is more relevant. Our approach is tested on different datasets and compared with other solutions from literature. Additionally, our method was evaluated in the CLVISION workshop at CVPR 2020

Determination of Sapphire Off‐Cut and Its Influence on the Morphology and Local Defect Distribution in Epitaxially Laterally Overgrown AlN for Optically Pumped UVC Lasers

Herein, a systematic study of the morphology and local defect distribution in epitaxially laterally overgrown (ELO) AlN on c‐plane sapphire substrates with different off‐cut angles ranging from 0.08° to 0.23° is presented. Precise measurements of the off‐cut angle α, using a combination of optical alignment and X‐ray diffraction with an accuracy of ±5° for the off‐cut direction and ±0.015° for the off‐cut angle, are carried out. For ELO AlN growth, a transition from step flow growth at α  0.14° is observed. Furthermore, the terraces of the step‐bunched surface exhibit curved steps. An analysis of the local defect distribution by scanning transmission electron microscopy and a comparison with atomic force microscopy reveal a bunching of defects in line with the ELO pattern and a roughening of step edges in highly defective regions. In addition, a reduction in the threshold excitation power density for optically pumped ultraviolet‐C (UVC) lasers with smooth surface morphologies is observed.TU Berlin, Open-Access-Mittel - 201

Cathodoluminescence and TEM investigations of structural and optical properties of AlGaN on epitaxial laterally overgrown AlN/sapphire templates

Surface steps as high as 15 nm on up to 10 μm thick AlN layers grown on patterned AlN/sapphire templates play a major role for the structural and optical properties of AlxGa1−xN layers with x ≥ 0.5 grown subsequently by metalorganic vapour phase epitaxy. The higher the Ga content in these layers is, the stronger is the influence of the surface morphology on their properties. For x = 0.5 not only periodic inhomogeneities in the Al content due to growth of Ga-rich facets are observed by cathodoluminescence, but these facets give rise to additional dislocation formation as discovered by annular dark-field scanning transmission electron microscopy. For AlxGa1−xN layers with x = 0.8 the difference in Al content between facets and surrounding material is much smaller. Therefore, the threading dislocation density (TDD) is only defined by the TDD in the underlying epitaxially laterally overgrown (ELO) AlN layer. This way high quality Al0.8Ga0.2N with a thickness up to 1.5 μm and a TDD ≤ 5x108 cm−2 was obtained.Peer Reviewe

RECALL: Rehearsal-free Continual Learning for Object Classification

Convolutional neural networks show remarkable results in classification but struggle with learning new things on the fly. We present a novel rehearsal-free approach, where a deep neural network is continually learning new unseen object categories without saving any data of prior sequences. Our approach is called RECALL, as the network recalls categories by calculating logits for old categories before training new ones. These are then used during training to avoid changing the old categories. For each new sequence, a new head is added to accommodate the new categories. To mitigate forgetting, we present a regularization strategy where we replace the classification with a regression. Moreover, for the known categories, we propose a Mahalanobis loss that includes the variances to account for the changing densities between known and unknown categories. Finally, we present a novel dataset for continual learning, especially suited for object recognition on a mobile robot (HOWS-CL-25), including 150,795 synthetic images of 25 household object categories. Our approach RECALL outperforms the current state of the art on CORe50 and iCIFAR-100 and reaches the best performance on HOWS-CL-25

The Impact of AlN Templates on Strain Relaxation Mechanisms during the MOVPE Growth of UVB-LED Structures

Strain relaxation mechanisms in AlGaN based light emitting diodes emitting in the ultraviolet B spectral range (UVB-LEDs) grown on different AlN/sapphire templates are analyzed by combining in situ reflectivity and curvature data with transmission electron microscopy. In particular, the impact of dislocation density, surface morphology, and lattice constant of the AlN/sapphire templates is studied. For nonannealed AlN/templates with threading dislocation densities (TDDs) of 4 × 109 and 3 × 109 cm−2 and different surface morphologies strain relaxation takes place mostly by conventional ways, such as inclination of threading dislocation lines and formation of horizontal dislocation bands. In contrast, a TDD reduction down to 1 × 109 cm−2 as well as a reduction of the lattice constant of high temperature annealed AlN template leads to drastic changes in the structure of subsequently grown AlGaN layers, e.g., to transformation to helical dislocations and enhanced surface enlargement by formation of macrofacets. For the growth of strongly compressively strained AlGaN layers for UVB-LEDs the relaxation mechanism is strongly influenced by the absolute values of TDD and the lattice constant of the AlN templates and is less influenced by their surface morphology

Spatial clustering of defect luminescence centers in Si-doped low resistivity Al0.82Ga0.18N

A series of Si-doped AlN-rich AlGaN layers with low resistivities was characterized by a combination of nanoscale imaging techniques. Utilizing the capability of scanning electron microscopy to reliably investigate the same sample area with different techniques, it was possible to determine the effect of doping concentration, defect distribution, and morphology on the luminescence properties of these layers. Cathodoluminescence shows that the dominant defect luminescence depends on the Si-doping concentration. For lower doped samples, the most intense peak was centered between 3.36 eV and 3.39 eV, while an additional, stronger peak appears at 3 eV for the highest doped sample. These peaks were attributed to the (VIII-ON)2− complex and the V3−III vacancy, respectively. Multimode imaging using cathodoluminescence, secondary electrons, electron channeling contrast, and atomic force microscopy demonstrates that the luminescence intensity of these peaks is not homogeneously distributed but shows a strong dependence on the topography and on the distribution of screw dislocations.DFG, 43659573, SFB 787: Halbleiter - Nanophotonik: Materialien, Modelle, BauelementeBMBF, 13N12587, Photonische Plattformtechnologie zur ultrasensitiven und hochspezifischen biochemischen Sensorik auf Basis neuartiger UV-LEDs (UltraSens

Effect of electron blocking layer doping and composition on the performance of 310 nm light emitting diodes

The effects of composition and p-doping profile of the AlGaN:Mg electron blocking layer (EBL) in 310 nm ultraviolet B (UV-B) light emitting diodes (LEDs) have been investigated. The carrier injection and internal quantum efficiency of the LEDs were simulated and compared to electroluminescence measurements. The light output power depends strongly on the temporal biscyclopentadienylmagnesium (Cp 2 Mg) carrier gas flow profile during growth as well as on the aluminum profile of the AlGaN:Mg EBL. The highest emission power has been found for an EBL with the highest Cp 2 Mg carrier gas flow and a gradually decreasing aluminum content in direction to the p-side of the LED. This effect is attributed to an improved carrier injection and confinement that prevents electron leakage into the p-doped region of the LED with a simultaneously enhanced carrier injection into the active region

Comparison of Ultraviolet B Light‐Emitting Diodes with Single or Triple Quantum Wells

Light-emitting diodes (LEDs) with an emission wavelength of 310 nm containing either a single or a triple quantum well are compared regarding their efficiency and long-term stability. In addition, the influence of the thickness of the lower quantum well barrier and the quantum well thickness in single quantum well (SQW) LEDs is investigated. Electroluminescence measurements show a 28% higher initial output power for the SQW LEDs compared with the triple quantum well (TQW) LEDs because of larger spatial overlap of the carriers in the SQW as revealed by electro-optical simulations of the LED heterostructures. However, TQW LEDs show a higher output power than SQW LEDs after 1 h operation under harsh conditions. For SQW LEDs, it is found that for a thicker lower quantum well barrier (65 nm instead of 25 nm) the initial output power decreases by ≈15%. A thicker SQW (3 nm instead of 1.6 nm) reduces the initial output power by even 45% but increases the lifetime by a factor of 6 which is attributed to reduced Auger recombination from an enhanced spatial separation of electrons and holes in the quantum wells due to the quantum-confined Stark effect