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Influence of Growth Polarity Switching on the Optical and Electrical Properties of GaN/AlGaN Nanowire LEDs
For the development and application of GaN-based nanowire structures, it is crucial to understand their fundamental properties. In this work, we provide the nano-scale correlation of the morphological, electrical, and optical properties of GaN/AlGaN nanowire light emitting diodes (LEDs), observed using a combination of spatially and spectrally resolved cathodoluminescence spectroscopy and imaging, electron beam-induced current microscopy, the nano-probe technique, and scanning electron microscopy. To complement the results, the photo- and electro-luminescence were also studied. The interpretation of the experimental data was supported by the results of numerical simulations of the electronic band structure. We characterized two types of nanowire LEDs grown in one process, which exhibit top facets of different shapes and, as we proved, have opposite growth polarities. We show that switching the polarity of nanowires (NWs) from the N- to Ga-face has a significant impact on their optical and electrical properties. In particular, cathodoluminescence studies revealed quantum wells emissions at about 3.5 eV, which were much brighter in Ga-polar NWs than in N-polar NWs. Moreover, the electron beam-induced current mapping proved that the p–n junctions were not active in N-polar NWs. Our results clearly indicate that intentional polarity inversion between the n- and p-type parts of NWs is a potential path towards the development of efficient nanoLED NW structures.
Nanograting-Enhanced Optical Fibers for Visible and Infrared Light Collection at Large Input Angles
The efficient incoupling of light into particular fibers at large angles is essential for a multitude of applications; however, this is difficult to achieve with commonly used fibers due to low numerical aperture. Here, we demonstrate that commonly used optical fibers functionalized with arrays of metallic nanodots show substantially improved large-angle light-collection performances at multiple wavelengths. In particular, we show that at visible wavelengths, higher diffraction orders contribute significantly to the light-coupling efficiency, independent of the incident polarization, with a dominant excitation of the fundamental mode. The experimental observation is confirmed by an analytical model, which directly suggests further improvement in incoupling efficiency through the use of powerful nanostructures such as metasurface or dielectric gratings. Therefore, our concept paves the way for high-performance fiber-based optical devices and is particularly relevant within the context of endoscopic-type applications in life science and light collection within quantum technology.
Effects of Plasma-Chemical Composition on AISI 316L Surface Modification by Active Screen Nitrocarburizing Using Gaseous and Solid Carbon Precursors
Low-temperature plasma nitrocarburizing treatments are applied to improve the surface properties of austenitic stainless steels by forming an expanded austenite layer without impairing the excellent corrosion resistance of the steel. Here, low-temperature active screen plasma nitrocarburizing (ASPNC) was investigated in an industrial-scale cold-wall reactor to compare the effects of two active screen materials: (i) a steel active screen with the addition of methane as a gaseous carbon-containing precursor and (ii) an active screen made of carbon-fibre-reinforced carbon (CFC) as a solid carbon precursor. By using both active screen materials, ASPNC treatments at variable plasma conditions were conducted using AISI 316L. Moreover, insight into the plasma-chemical composition of the H2-N2 plasma for both active screen materials was gained by laser absorption spectroscopy (LAS) combined with optical emission spectroscopy (OES). It was found that, in the case of a CFC active screen in a biased condition, the thickness of the nitrogen-expanded austenite layer increased, while the thickness of the carbon-expanded austenite layer decreased compared to the non-biased condition, in which the nitrogen- and carbon-expanded austenite layers had comparable thicknesses. Furthermore, the crucial role of biasing the workload to produce a thick and homogeneous expanded austenite layer by using a steel active screen was validated.
Prediction of Short Fiber Composite Properties by an Artificial Neural Network Trained on an RVE Database
In this study, an artificial neural network is designed and trained to predict the elastic properties of short fiber reinforced plastics. The results of finite element simulations of three-dimensional representative volume elements are used as a data basis for the neural network. The fiber volume fraction, fiber length, matrix-phase properties, and fiber orientation are varied so that the neural network can be used within a very wide range of parameters. A comparison of the predictions of the neural network with additional finite element simulations shows that the stiffnesses of short fiber reinforced plastics can be predicted very well by the neural network. The average prediction accuracy is equal or better than by a two-step homogenization using the classical method of Mori and Tanaka. Moreover, it is shown that the training of the neural network on an extended data set works well and that particularly calculation-intensive data points can be avoided without loss of prediction quality.
Optimization of Multi-Level Operation in RRAM Arrays for In-Memory Computing
Accomplishing multi-level programming in resistive random access memory (RRAM) arrays with truly discrete and linearly spaced conductive levels is crucial in order to implement synaptic weights in hardware-based neuromorphic systems. In this paper, we implemented this feature on 4-kbit 1T1R RRAM arrays by tuning the programming parameters of the multi-level incremental step pulse with verify algorithm (M-ISPVA). The optimized set of parameters was assessed by comparing its results with a non-optimized one. The optimized set of parameters proved to be an effective way to define non-overlapped conductive levels due to the strong reduction of the device-to-device variability as well as of the cycle-to-cycle variability, assessed by inter-levels switching tests and during 1 k reset-set cycles. In order to evaluate this improvement in real scenarios, the experimental characteristics of the RRAM devices were captured by means of a behavioral model, which was used to simulate two different neuromorphic systems: an 8 × 8 vector-matrix-multiplication (VMM) accelerator and a 4-layer feedforward neural network for MNIST database recognition. The results clearly showed that the optimization of the programming parameters improved both the precision of VMM results as well as the recognition accuracy of the neural network in about 6% compared with the use of non-optimized parameters.
Generation of Multiple Vector Optical Bottle Beams
We propose binary diffractive optical elements, combining several axicons of different types (axis-symmetrical and spiral), for the generation of a 3D intensity distribution in the form of multiple vector optical ‘bottle’ beams, which can be tailored by a change in the polarization state of the illumination radiation. The spatial dynamics of the obtained intensity distribution with different polarization states (circular and cylindrical of various orders) were investigated in paraxial mode numerically and experimentally. The designed binary axicons were manufactured using the e-beam lithography technique. The proposed combinations of optical elements can be used for the generation of vector optical traps in the field of laser trapping and manipulation, as well as for performing the spatial transformation of the polarization state of laser radiation, which is crucial in the field of laser-matter interaction for the generation of special morphologies of laser-induced periodic surface structures.
Effect of Build Orientation on the Microstructure, Mechanical and Corrosion Properties of a Biodegradable High Manganese Steel Processed by Laser Powder Bed Fusion
In the last decade, additive manufacturing technologies like laser powder bed fusion (LPBF) have emerged strongly. However, the process characteristics involving layer-wise build-up of the part and the occurring high, directional thermal gradient result in significant changes of the microstructure and the related properties compared to traditionally fabricated materials. This study presents the influence of the build direction (BD) on the microstructure and resulting properties of a novel austenitic Fe-30Mn-1C-0.02S alloy processed via LPBF. The fabricated samples display a {011} texture in BD which was detected by electron backscatter diffraction. Furthermore, isolated binding defects could be observed between the layers. Quasi-static tensile and compression tests displayed that the yield, ultimate tensile as well as the compressive yield strength are significantly higher for samples which were built with their longitudinal axis perpendicular to BD compared to their parallel counterparts. This was predominantly ascribed to the less severe effects of the sharp-edged binding defects loaded perpendicular to BD. Additionally, a change of the Young’s modulus in dependence of BD could be demonstrated, which is explained by the respective texture. Potentiodynamic polarization tests conducted in a simulated body fluid revealed only slight differences of the corrosion properties in dependence of the build design.
An Assessment of Deep Learning Models and Word Embeddings for Toxicity Detection within Online Textual Comments
Today, increasing numbers of people are interacting online and a lot of textual comments are being produced due to the explosion of online communication. However, a paramount inconvenience within online environments is that comments that are shared within digital platforms can hide hazards, such as fake news, insults, harassment, and, more in general, comments that may hurt someone’s feelings. In this scenario, the detection of this kind of toxicity has an important role to moderate online communication. Deep learning technologies have recently delivered impressive performance within Natural Language Processing applications encompassing Sentiment Analysis and emotion detection across numerous datasets. Such models do not need any pre-defined hand-picked features, but they learn sophisticated features from the input datasets by themselves. In such a domain, word embeddings have been widely used as a way of representing words in Sentiment Analysis tasks, proving to be very effective. Therefore, in this paper, we investigated the use of deep learning and word embeddings to detect six different types of toxicity within online comments. In doing so, the most suitable deep learning layers and state-of-the-art word embeddings for identifying toxicity are evaluated. The results suggest that Long-Short Term Memory layers in combination with mimicked word embeddings are a good choice for this task.
Quantum-Optical Spectrometry in Relativistic Laser-Plasma Interactions Using the High-Harmonic Generation Process: A Proposal
Quantum-optical spectrometry is a recently developed shot-to-shot photon correlation-based method, namely using a quantum spectrometer (QS), that has been used to reveal the quantum optical nature of intense laser–matter interactions and connect the research domains of quantum optics (QO) and strong laser-field physics (SLFP). The method provides the probability of absorbing photons from a driving laser field towards the generation of a strong laser–field interaction product, such as high-order harmonics. In this case, the harmonic spectrum is reflected in the photon number distribution of the infrared (IR) driving field after its interaction with the high harmonic generation medium. The method was implemented in non-relativistic interactions using high harmonics produced by the interaction of strong laser pulses with atoms and semiconductors. Very recently, it was used for the generation of non-classical light states in intense laser–atom interaction, building the basis for studies of quantum electrodynamics in strong laser-field physics and the development of a new class of non-classical light sources for applications in quantum technology. Here, after a brief introduction of the QS method, we will discuss how the QS can be applied in relativistic laser–plasma interactions and become the driving factor for initiating investigations on relativistic quantum electrodynamics.
16.3 w peak‐power pulsed all‐diode laser based multi‐wavelength master‐oscillator power‐amplifier system at 964 nm
An all-diode laser-based master oscillator power amplifier (MOPA) configuration for the generation of ns-pulses with high peak power, stable wavelength and small spectral line width is presented. The MOPA emits alternating at two wavelengths in the spectral range between 964 nm and 968 nm, suitable for the detection of water vapor by absorption spectroscopy. The monolithic master oscillator (MO) consists of two slightly detuned distributed feedback laser branches, whose emission is combined in a Y-coupler. The two emission wavelengths can be adjusted by varying the current or temperature to an absorption line and to a non-absorbing region. The power amplifier (PA) consists of a ridge-waveguide (RW) section and a tapered section, monolithically integrated within one chip. The RW section of the PA acts as an optical gate and converts the continuous wave input beam emitted by the MO into a sequence of short optical pulses, which are subsequently amplified by the tapered section to boost the output power. For a pulse width of 8 ns, a peak power of 16.3 W and a side mode suppression ratio of more than 37 dB are achieved at a repetition rate of 25 kHz. The measured spectral width of 10 pm, i.e., 0.1 cm−1, is limited by the resolution of the optical spectrum analyzer. The generated pulses emitting alternating at two wavelengths can be utilized in a differential absorption light detection and ranging system.