Lundgrenfournier3506

The light extraction efficiency (LEE) of GaN-based vertical blue micron-scale light-emitting diode (μ-LED) structures was investigated numerically using three-dimensional finite-difference timedomain (FDTD) methods. The entire μ-LED chip was included in the FDTD computational domain to determine the LEE accurately. As the lateral dimensions of μ-LEDs increased from 5 to 30 μm, the LEE decreased gradually because of the increased portion of light trapped inside the LED chip and the increased light absorption in the GaN layers with increasing chip size. The LEE varied strongly with the p-GaN thickness for the μ-LED with a flattop surface, which could be explained by the strong dependence of the spatial distribution of the emission patterns on the p-GaN thickness. This dependence on the p-GaN thickness decreased when the surface of the μ-LED chip was patterned. A high LEE of >80% could be achieved in LEDs with properly chosen parameters. The FDTD simulation results presented in this study are expected to be employed advantageously in designing μ-LED structures with a high LEE.In this study, we implemented reversible current switching (RCS) of 100 mA in a two-terminal device based on a vanadium dioxide (VO₂) thin film, which could be controlled by far-infrared (FIR) laser pulses. The VO₂ thin films used for fabrication of two-terminal devices were grown on sapphire (Al₂O₃) substrates using a pulsed laser deposition method. An optimal deposition condition was determined by analyzing the resistance-temperature curves of deposited VO₂ thin films and the current-voltage characteristics of two-terminal devices based on these films, which were suggested in our previous works. The film surface of the VO₂-based device was directly irradiated using focused CO₂ laser pulses, and the insulator-metal transition or metal-insulator transition of the VO₂ thin film could be triggered depending on laser irradiation. Consequently, RCS of up to 100 mA could be accomplished. This on-state current is close to the upper limit of the current flowing through our VO₂ device. The switching contrast, defined as the ratio between on-state and off-state currents, was evaluated and found to be ˜11,962. The average rising and falling times of the switched current were found to be ˜29.2 and ˜71.7 ms, respectively. In comparison with our previous work, the improved heat dissipation structure and the high-quality thin film could maintain the switching contrast at a similar level, although the on-state current was increased by about two times.There are many challenges in the hardware implementation of a neural network using nanoscale memristor crossbar arrays where the use of analog cells is concerned. Multi-state or analog cells introduce more stringent noise margins, which are difficult to adhere to in light of variability. We propose a potential solution using a 1-bit memristor that stores binary values "0" or "1" with their memristive states, denoted as a high-resistance state (HRS) and a low-resistance state (LRS). In addition, we propose a new architecture consisting of 4-parallel 1-bit memristors at each crosspoint on the array. The four 1-bit memristors connected in parallel represent 5 decimal values according to the number of activated memristors. This is then mapped to a synaptic weight, which corresponds to the state of an artificial neuron in a neural network. We implement a convolutional neural network (CNN) model on a framework (tensorflow) using an equivalent quantized weight mapping model that demonstrates learning results almost identical to a high-precision CNN model. This radix-5 CNN is mapped to hardware on the proposed parallel-connected memristor crossbar array. Also, we propose a method for negative weight representation on a memristor crossbar array. Then, we verify the CNN hardware on an edge-AI (e-AI) platform, developed on a field-programmable gate array (FPGA). In this e-AI platform, we represent five weights per crosspoint using CLB logics. We test the learning results of the CNN hardware using an e-AI platform with a dataset consisting of 4×4 images in three classes. We verify the functionality of our radix-5 CNN implementation showing comparable classification accuracy to high-precision use cases, with reduction of the area of the memristor crossbar array by half, all verified on a FPGA. Implementing the CNN model on the FPGA board can contribute to the practical use of edge-AI.Thermally and chemically stable, sulfonyl imide-based polymer blends have been prepared from sulfonimide poly(arylene ether sulfone) (SI-PAES) and sulfonimide Parmax-1200 (SI-Parmax-1200) using the solvent casting method. Initially, sulfonimide poly(arylene ether sulfone) (SI-PAES) polymers have typically been synthesized via direct polymerization of bis(4-chlorophenyl) sulfonyl imide (SI-DCDPS) and bis(4-fluorophenyl) sulfone (DFDPS) with bisphenol A (BPA). Subsequently, SI-Parmax-1200 has been synthesized via post-modification of the existing Parmax-1200 polymer followed by sulfonation and imidization. The SI-PAES/SI-Parmax-1200 blend membranes show high ion exchange capacity ranging from 1.65 to 1.97 meq/g, water uptake ranging from 22.8 to 65.4% and proton conductivity from 25.9 to 78.5 mS/cm. Protoporphyrin IX order Markedly, the SI-PAES-40/SI-Parmax-1200 membrane (blended-40) exhibits the highest proton conductivity (78.5 mS/cm), which is almost similar to Nafion 117® (84.73 mS/cm). The thermogravimetric analysis (TGA) and Fenton's test confirm the excellent thermal and chemical stability of the synthetic polymer blends. Furthermore, the scanning electron microscopy (SEM) study shows a distinct phase separation at the hydrophobic/hydrophilic segments, which facilitate proton conduction throughout the ionic channel of the blend polymers. Therefore, the synthetic polymer blends represent an alternative to Nafion 117® as proton exchangers for fuel cells.Nano memristor crossbar arrays, which can represent analog signals with smaller silicon areas, are popularly used to describe the node weights of the neural networks. The crossbar arrays provide high computational efficiency, as they can perform additions and multiplications at the same time at a cross-point. In this study, we propose a new approach for the memristor crossbar array architecture consisting of multi-weight nano memristors on each cross-point. As the proposed architecture can represent multiple integer-valued weights, it can enhance the precision of the weight coefficients in comparison with the existing memristor-based neural networks. This study presents a Radix-11 nano memristor crossbar array with weighted memristors; it validates the operations of the circuits, which use the arrays through circuit-level simulation. With the proposed Radix-11 approach, it is possible to represent eleven integer-valued weights. In addition, this study presents a neural network designed using the proposed Radix-11 weights, as an example of high-performance AI applications.