Keysight

The accelerated demand for electrochemical energy storage urges the need for new, sustainable, and lightweight materials able to store high energy densities rapidly and efficiently. Development of these functional materials requires specialized techniques that can provide a close insight into the electrochemical properties at the nanoscale. For this reason, the electrochemical scanning microwave microscopy (EC‐SMM) enabling local measurement of electrochemical properties with nanometer spatial resolution and sensitivity down to atto‐Ampere electrochemical currents is introduced. Its power is demonstrated by studying NiCo‐layered double hydroxide flakes, revealing active site locations and providing atomistic insights into the catalytic process. EC‐SMM's spatial resolution of 16 ± 1 nm allows detailed analysis of edge effects in this 2D material, including localized electrochemical impedance spectroscopy and cyclic voltammetry. Coupled with advanced numerical modeling of diffusion and migration dynamics at the material interface, the findings elucidate the previously hypothesized processes responsible for localized enhancements in electrochemical activity, while pinpointing essential parameters for tuning the thermodynamics of ion intercalation and optimizing surface adsorption.

The increase of the data rate beyond 100 Gbps for wired channels such as the Chip-to-Module interfaces requires a very careful evaluation and optimization of the transmitter and receiver properties and equalization capabilities based on the specific passive channel of interest. The Channel Operating Margin Methodology offered in the IEEE Standard for Ethernet 802.3 is developed for such purpose. It is adopted in this paper to demonstrate how it can be used while paving a rigorous step-by-step procedure for the transmitter characterization and the reliable evaluation of the receiver equalization. A wide range of experiments are carried out to demonstrate the effective applicability of the COM method for optimizing the 100 Gbps 4-level Pulse Amplitude Modulation (PAM4) signaling and for pushing the channel design toward the length (and loss) limits.

The advent of generative networks and their adoption in numerous domains and communities have led to a wave of innovation and breakthroughs in artificial intelligence and machine learning. Generative Adversarial Networks (GANs) have expanded the scope of what is possible with machine learning, allowing for new applications in areas such as computer vision, natural language processing, and creative AI. GANs, in particular, have been used for a wide range of tasks, including image and video generation, data augmentation, style transfer, and anomaly detection. They have also been used for medical imaging and drug discovery, where they can generate synthetic data to augment small datasets, reduce the need for expensive experiments, and lower the number of real patients that must be included in medical trials. Given these developments, we propose using the power of generative adversarial networks to create and augment flow-based network traffic datasets. We evaluate a series of GAN architectures, including Wasserstein, conditional, energy-based, gradient penalty, and LSTM GANs. We evaluate their performance on a set of flow-based network traffic data collected from 16 subjects who used their computers for home, work, and study purposes. The performance of these GAN architectures is described according to metrics that involve networking principles, data distribution among a collection of flows, and temporal data distribution. Given the tendency of network intrusion detection datasets to have a very imbalanced data distribution, i.e., a large number of samples in the “normal traffic” category and a comparatively low number of samples assigned to the “intrusion” categories, we test our GANs by augmenting the intrusion data and checking whether this helps intrusion detection neural networks in their task. We publish the resulting UPBFlow dataset and code on GitHub ¹ .

The high-speed switching capabilities of wide bandgap (WBG) power devices have posed challenges in accurately evaluating their dynamic characteristics, primarily due to the increasing influence of parasitic components in switching test circuits. To address this issue, we investigated the impact of parasitics by conducting dynamic tests and schematic-level transient simulation on a half-bridge switching circuit incorporating SiC MOSFETs. This comparative analysis identified specific parasitic components responsible for undesirable behaviors such as spikes and ringing in the switching waveform. Our findings provide insights into which parasitic components in the test circuit are critical for the accurate dynamic characterization of SiC MOSFETs.

III–V/III-nitride p–n junctions were realized via crystal heterogeneous integration, and the resulting diodes were characterized to analyze electrical behavior and junction quality. p-type In0.53Ga0.47As, which is a well-established base layer in InP heterojunction bipolar transistor (HBT) technology, was used in combination with a homoepitaxial n-type GaN. The latter offers low dislocation density, coupled with high critical electric field and saturation velocity, which are attractive for use in future HBT collector layers. Transmission electron microscopy confirms an abrupt interface in the fabricated heterogeneous diodes. Electrical characterization of the diodes reveals a near-unity ideality factor (n ∼ 1.07) up to 145 °C, a high rectification ratio of ∼10⁸, and a low interface trap density of 3.7 × 10¹² cm⁻².

Quality control is highly relevant for safety, sustainability and efficiency of the battery manufacturing process. An early and reliable detection of failures in the production chain is important. Here we present a method for detecting micrometric imperfections and contaminations on the battery separator before filling the battery stack with the electrolyte. We sense these irregularities by measuring an increase of partial discharges when applying between the battery electrodes potentials close, but still well below the breakdown voltage of the separator. We can distinguish different degrees and different types of contamination with a very high confidence. This is enabled by a throughout statistical analysis of the partial discharge events. The overall reliability of detecting a contaminated against the clean separator is 96 %. The technique, as implemented here, uses categorization procedures and machine learning algorithms to automate decision‐making and can accelerate the quality assessment process in pilot lines or small‐ manufacturing. Compared to other methods, like optical detection or full discharge measurements, the here presented approach is very reliable, simple to implement and virtually noninvasive.

Partial shading within arrays diminishes power output, induces hotspots, and compromises module integrity, thereby impacting system performance. The presence of bypass diodes further exacerbates these issues by introducing non‐convexities in power curves, leading to additional power losses. To solve this problem, a new reconfiguration technique named Fibonacci Random Number Generator is proposed in this work which minimizes the effects of shading on the panels. The proposed methodology swiftly reduces current discrepancies between PV array rows by reshuffles the panels in an array to disperse the shade better using a mathematical formula resulting in increased power output and smoother power curves during partial shading events. The effectiveness of the proposed method is measured in terms of GMPP, row current calculations, power loss (PL), mismatch losses (ML), execution ratio (ER), fill factor (FF), and capacity factor (CF) for four distinctive shading conditions. Validation of results in software and hardware platforms showcase the applicability of proposed approach in real‐time environments. Results indicate significant average power improvements of 25.49%, 15.47%, and 9.29% compared to existing popular reconfigurations like Skyscraper, Ken‐Ken, and Chaotic baker map. The proposed method stands out as a potent tool for optimizing PV arrays within real‐world systems grappling with partial shading issues.

We report the direct observation of radio-frequency negative differential resistance, via on-wafer S-parameter measurements, in GaN-based impact ionization avalanche transit time (IMPATT) diodes. Clear signatures of reflection gain are observed from 18.7 to 30.6 GHz. These observations have been made possible by suppressing the reverse leakage current (and thereby parasitic shunt conductance) by optimization of the fabrication process, in conjunction with the use of pulsed measurements to suppress device self-heating. Consistent with avalanche-dominated behavior, the measured DC reverse bias current–voltage measurements show a positive temperature coefficient of breakdown. For the high-frequency on-wafer characterization, pulsed-bias S-parameter measurements with low (0.0067%) duty cycle were used to mitigate thermal effects. The measured avalanche frequency aligns closely with theoretical predictions based on Gilden and Hines' small signal model [Gilden and Hines, IEEE Trans. Electron Devices ED-13(1), 169–175 (1966)], measured impact ionization coefficients [Cao et al., Appl. Phys. Lett. 112(26), 262103 (2018)], and experimental saturation velocity measurements [Bajaj et al., Appl. Phys. Lett. 107(15), 153504 (2015)]; this excellent agreement confirms IMPATT operation and provides insights needed to further optimize device performance.