Hasil untuk "Electronics"

Jae Hwan Kim, Kwang Seop Son, Jae Gu Song et al.

The increasing digitalization of Instrumentation and Control (I&C) systems in Nuclear Power Plants (NPPs) has improved operational efficiency while introducing cybersecurity vulnerabilities. Conventional network-based intrusion detection systems (IDS) face limitations in detecting sophisticated cyber threats targeting safety-critical controllers. To address these challenges, this study proposes a process information-driven cyber threat detection methodology based on real-time process data analysis and control logic consistency, enabling non-intrusive threat identification. The proposed methodology was examined through simulation and experimental testing using an APR-1400 Reactor Protection System (RPS) testbed. A cyber attack scenario targeting the High Pressurizer Pressure (HPP) Trip function was designed to assess the effectiveness of the detection mechanism. Simulation results demonstrated the detection algorithm's ability to identify unauthorized modifications to the trip setpoint, indicating the potential to detect cyber threats affecting reactor trip logic. Furthermore, experimental testing using the Safety Data Acquisition & Detection System (SDDS) demonstrated real-time anomaly detection while maintaining system integrity. These findings suggest that the proposed process-driven detection technique can enhance the cybersecurity resilience of NPPs without disrupting operational stability.

Andreas Gsponer, Sebastian Onder, Stefan Gundacker et al.

Silicon carbide (SiC) has been widely adopted in the semiconductor industry, particularly in power electronics, because of its high temperature stability, high breakdown field, and fast switching speeds. Its wide band gap makes it an interesting candidate for radiation-hard particle detectors in high-energy physics and medical applications. Furthermore, the high electron and hole drift velocities in 4H-SiC enable devices suitable for ultra-fast particle detection and timing applications. However, currently, the front-end readout electronics used for 4H-SiC detectors constitute a bottleneck in investigations of the charge carrier drift. To address these limitations, a high-frequency readout board with an intrinsic bandwidth of 10 GHz was developed. With this readout, the transient current signals of a 4H-SiC diode with a diameter of 141 $\mathrm{μm}$ and a thickness of 50 $\mathrm{μm}$ upon UV-laser, alpha particle, and high-energy proton beam excitation were recorded. In all three cases, the electron and hole drift can clearly be separated, which enables the extraction of the charge carrier drift velocities as a function of the electric field. These velocities, for the first time directly measured, provide a valuable comparison to Monte-Carlo simulated literature values and constitute an essential input for TCAD simulations. Finally, a complete simulation environment combining TCAD, the Allpix$^2$ framework, and SPICE simulations is presented, in good agreement with the measured data.

Jizhao Zang, Travis C. Briles, Jesse S. Morgan et al.

Access to electrical signals across the millimeter-wave (mmW) and terahertz (THz) bands offers breakthroughs for high-performance applications. Despite generations of revolutionary development, integrated electronics are challenging to operate beyond 100 GHz. Therefore, new technologies that generate wideband and tunable electronic signals would advance wireless communication, high-resolution imaging and scanning, spectroscopy, and network formation. Photonic approaches have been demonstrated for electronic signal generation, but at the cost of increased size and power consumption. Here, we describe a chip-scale, universal mmW frequency synthesizer, which uses integrated nonlinear photonics and high-speed photodetection to exploit the nearly limitless bandwidth of light. We use a photonic-integrated circuit to generate dual, microresonator-soliton frequency combs whose interferogram is fundamentally composed of harmonic signals spanning the mmW and THz bands. By phase coherence of the dual comb, we precisely stabilize and synthesize the interferogram to generate any output frequency from DC to >1000 GHz. Across this entire range, the synthesizer exhibits exceptional absolute fractional frequency accuracy and precision, characterized by an Allan deviation of 3*10^(-12) in 1 s measurements. We use a modified uni-traveling-carrier (MUTC) photodiode with an operating frequency range to 500 GHz to convert the interferogram to an electrical signal, generating continuously tunable tones across the entire mmW band. The synthesizer phase noise at a reference frequency of 150 GHz is -83 dBc/Hz at 100 kHz offset, which exceeds the intrinsic performance of state-of-the-art CMOS electronics. Our work harnesses the coherence, bandwidth, and integration of photonics to universally extend the frequency range of current, advanced-node CMOS microwave electronics to the mmW and THz bands.

Hugo de Souza Oliveira, Niloofar Saeedzadeh Khaanghah, Martijn Oetelmans et al.

The technological transition from soft machines to soft robots necessarily passes through the integration of soft electronics and sensors. This allows for the establishment of feedback control systems while preserving the softness of the robot embodiment. Multistable mechanical metamaterials are excellent building blocks of soft machines, as their nonlinear response can be tuned by design to accomplish several functions. In this work, we present the integration of soft capacitive sensors in a multistable mechanical metamaterial, to enable proprioceptive sensing of state changes. The metamaterial is a periodic arrangement of 4 bistable unit cells. Each unit cell has an integrated capacitive sensor. Both the metastructure and the sensors are made of soft materials (TPU) and are 3D printed. Our preliminary results show that the capacitance variation of the sensors can be linked to state transitions of the metamaterial, by capturing the nonlinear deformation.

Santu Mondal, Sneha Ray, Aritra Acharyya et al.

This work investigates the application of artificial neural network (ANN)-based regression models to predict the static and dynamic characteristics of GaN impact avalanche transit time (IMPATT) sources in the terahertz (THz) frequency regime. A comprehensive dataset, derived from self-consistent quantum drift-diffusion (SCQDD) simulations of GaN IMPATT structures designed for a wide frequency range from the microwave frequency bands, up to 5 THz, is used to train the ANN models. The models effectively capture the impact of variations in structural, doping, and biasing parameters on device performance. The proposed ANN approach significantly reduces computational time for predicting breakdown characteristics, power output, and conversion efficiency properties of IMPATT sources, achieving similar accuracy to traditional SCQDD simulations while requiring only 7.8&#x2013;20.1% of the computational time. Mean square errors are observed to be on the order of <inline-formula> <tex-math notation="LaTeX">$10^{-4}$ </tex-math></inline-formula>&#x2013;<inline-formula> <tex-math notation="LaTeX">$10^{-6}$ </tex-math></inline-formula>, demonstrating the models&#x2019; high accuracy. Experimental validation shows strong agreement in terms of breakdown voltage, power output, and efficiency, supporting the potential of machine learning to streamline the design and optimization of high-frequency semiconductor devices.

Saeedeh Lotfi, Martin Janda, Jan Reboun et al.

Abstract Printed electronics (PE) present a promising alternative to conventional photolithography by enabling rapid prototyping with reduced costs, material waste, and enhanced design flexibility and advantages, particularly relevant for high-frequency microwave applications. This study presents the design, fabrication, and evaluation of two microstrip low-pass filters (LPFs) with cutoff frequencies of 2.60 GHz and 3.55 GHz serving as representative components for microwave circuits, using three additive manufacturing techniques: Direct-Write (DW), Screen Printing (SP), and Aerosol Jet Printing (AJP). Over 60 filter samples were fabricated and measured to systematically assess performance across different printing methods. The LPFs were designed and analyzed through electromagnetic simulations, complemented by an LC equivalent circuit model based on actual device dimensions to better understand their behavior. Measured frequency responses showed strong agreement with simulations, validating the effectiveness of all three printing methods. Each technique demonstrated unique trade-offs between resolution, fabrication complexity, and electrical performance, emphasizing the need to tailor method selection to specific application requirements. This paper offers valuable insights into the design, analysis, and fabrication of RF and microwave circuits using printed electronics, highlighting the strengths and limitations of each technique. It serves as a practical guide for researchers in selecting suitable methods for high-frequency applications.

Pragati Shuddhodhan Meshram, Swetha Karthikeyan, Bhavya Bhavya et al.

Multi-modal Large Language Models (MLLMs) are gaining significant attention for their ability to process multi-modal data, providing enhanced contextual understanding of complex problems. MLLMs have demonstrated exceptional capabilities in tasks such as Visual Question Answering (VQA); however, they often struggle with fundamental engineering problems, and there is a scarcity of specialized datasets for training on topics like digital electronics. To address this gap, we propose a benchmark dataset called ElectroVizQA specifically designed to evaluate MLLMs' performance on digital electronic circuit problems commonly found in undergraduate curricula. This dataset, the first of its kind tailored for the VQA task in digital electronics, comprises approximately 626 visual questions, offering a comprehensive overview of digital electronics topics. This paper rigorously assesses the extent to which MLLMs can understand and solve digital electronic circuit questions, providing insights into their capabilities and limitations within this specialized domain. By introducing this benchmark dataset, we aim to motivate further research and development in the application of MLLMs to engineering education, ultimately bridging the performance gap and enhancing the efficacy of these models in technical fields.

T. Muscheid, R. Gartmann, N. Karcher et al.

Recent advances in the development of cryogenic particle detectors such as magnetic microcalorimeters (MMCs) allow the fabrication of sensor arrays with an increasing number of pixels. Since these detectors must be operated at the lowest temperatures, the readout of large detector arrays is still quite challenging. This is especially true for the ECHo experiment, which presently aims to simultaneously run 6,000 two-pixel detectors to investigate the electron neutrino mass. For this reason, we developed a readout system based on a microwave SQUID multiplexer ($μ$MUX) that is operated by a custom software-defined radio (SDR) at room-temperature. The SDR readout electronics consist of three distinct hardware units: a data processing board with a Xilinx ZynqUS+ MPSoC; a converter board that features DACs, ADCs, and a coherent clock distribution network; and a radio frequency front-end board to translate the signals between the baseband and the microwave domains. Here, we describe the characteristics of the full-scale SDR system. First, the generated frequency comb for driving the $μ$MUX was evaluated. Subsequently, by operating the SDR in direct loopback, the crosstalk of the individual channels after frequency demultiplexing was investigated. Finally, the system was used with a 16-channel $μ$MUX to evaluate the linearity of the SDR, and the noise contributed to the overall readout setup.

Jianguo Liu, Yu Wang, Changqing Feng et al.

Many small and medium-sized physics experiments are being conducted worldwide. These experiments have similar requirements for readout electronics, especially the back-end electronics. Some experiments need a trigger logic unit(TLU) to provide timing and synchronous control signals. This paper introduces a back-end electronics design for small and medium-sized physics experiments; it adopts a daughter-motherboard structure integrated TLU function to provide greater flexibility. Different interfaces and protocols can be flexibly selected according to data bandwidth requirements. It supports 32 optical fiber interfaces based on a field-programmable gate array (FPGA) of normal IOs with 400Mbps of data bandwidth for each channel. At the same time, it supports 16 high-speed communication interfaces based on GTX port with several Gbps data bandwidth of each channel. For the TLU function, this design has 8 HDMI interfaces and one RJ45 interface to provide synchronous triggers and other control signals, and it has six analog LEMOs and four digital LEMOs to accept asynchronous signals from an external source. These design specifications can meet the needs of most small and medium-sized experiments. This set of back-end electronics has been successfully used in experiments such as PandaX-III, VLAST, and moungraphy. Moreover, it has successfully conducted beam tests with a readout of the data of VLAST detectors at CERN.

Mauro Femminella, Martina Palmucci, Gianluca Reali et al.

In modern cloud computing, the need for flexible and scalable orchestration of services, combined with robust security measures, is paramount. In this paper, we propose an innovative approach for managing secure cloud bursting in Kubernetes, combining Attribute-Based Encryption (ABE) with Kubernetes labeling. Our model addresses the challenges of complexity, cost, and data protection compliance by leveraging both Kubernetes and ABE. We introduce an attribute-based bursting component that uses Kubernetes labels for orchestration, and an encryption component that employs ABE for data protection. This unified management model ensures data confidentiality while enabling efficient cloud bursting. Our approach combines the strengths of label-based orchestration with fine-grained encryption, providing a technologically advanced yet user-friendly solution for secure cloud bursting. We present a proof-of-concept implementation that demonstrates the feasibility and effectiveness of our model. Our approach offers a unified solution that complies with security and privacy laws while meeting the needs of contemporary cloud-based systems.

Ji-Hoon Kim, Seunghee Han, Kwanghyun Park et al.

The ability to perform machine learning (ML) tasks in a database management system (DBMS) is a new paradigm for conventional database systems as it enables advanced data analytics on top of well-established capabilities of DBMSs. However, the integration of ML in DBMSs introduces new challenges in traditional CPU-based systems because of its higher computational demands and bigger data bandwidth requirements. To address this, hardware acceleration has become even more important in database systems, and the computational storage device (CSD) placing an accelerator near storage is considered as an effective solution due to its high processing power with no extra data movement cost. In this paper, we propose Trinity, an end-to-end database system that enables in-database, in-storage platform that accelerates advanced analytics queries invoking trained ML models along with complex data operations. By designing a full stack from DBMS&#x2019;s internal software components to hardware accelerator, Trinity enables in-database ML pipelines on the CSD. On the software side, we extend the internals of conventional DBMSs to utilize the accelerator in the SmartSSD. Our extended analyzer evaluates the compatibility of the current query with our hardware accelerator and compresses compatible queries into a 24-byte numeric format for efficient hardware processing. Furthermore, the predictor is extended to integrate our performance cost models to always offload queries into the optimal hardware backend. The proposed SmartSSD cost model mathematically models our hardware, including host operations, data transfers, FPGA kernel execution time, and the CPU cost model uses polynomial regression ML models to predict complex CPU latency. On the hardware side, we introduce the in-database processing accelerator (i-DPA), a custom FPGA-based accelerator. i-DPA includes database page decoder to fully exploit the bandwidth benefit of near-storage processing. It also employs dynamic tuple binding to enhance the overall parallelism and hardware utilization. i-DPA;s architecture having heterogeneous computing units with a reconfigurable on-chip interconnect also allows seamless data streaming, enabling task-level pipeline across different computing units. Finally, our evaluation shows that Trinity improves the end-to-end performance of analytics queries by <inline-formula> <tex-math notation="LaTeX">$15.21\times $ </tex-math></inline-formula> on average and up to <inline-formula> <tex-math notation="LaTeX">$57.18\times $ </tex-math></inline-formula> compared to the conventional CPU-based DBMS platform. We also show that the Trinity&#x2019;s performance can linearly scale up with multiple SmartSSDs, achieving nearly up to <inline-formula> <tex-math notation="LaTeX">$200\times $ </tex-math></inline-formula> speedup over the baseline with four SmartSSDs.

Vivekananda Pattanaik, Binaya Kumar Malika, Subhasis Panda et al.

Monitoring, detection, and measurement are vital in the energy system to facilitate better wide-ranging protection, control, and operation. In this regard, to offer a better operational and dynamic performance, Phasor Measurement Units (PMUs) are the prominent components and most desirable choices for transforming the conventional power system into a smart grid and micro-grid-based system. PMUs provide synchronized phasor measurements of electrical parameters such as current, voltage, and other information related to the system's status. However, one of the significant issues related to PMU placement is to place such that the system is fully observable and achieves better state estimation that has a crucial impact on planning, protection, control, and other factors related to the system's overall performance. This motivates the authors to extensively review the innovative approaches suggested by various authors and present their ideas, advantages, limitations, and untouched research gaps. Secondly, this review elaborates on these techniques’ concepts and mathematical details. Thirdly, various techniques are discussed categorically, starting from classical optimization techniques, heuristic and meta-heuristic-based approaches, hybrid techniques, and advanced methods. Also, the problem formulation for the PMUs placement is expressed, giving importance to the objective function, constraints, variables, and assessment index reflecting the optimality in placement. In addition, the future scope is presented to enlighten the researcher on critical discussions regarding the research gaps and fundamental changes to system topology and operation that must be focused on. In this review article, even though the methodologies are similar in approach, a micro-PMU (µPMU) placement is discussed as more important than the PMUs, looking at the present scenario and application in the distribution sector. This article also focuses on various PMU-related standards and real-time applications.

Marie Froidevaux, Ludovic Douillard, Willem Boutu et al.

Strong-field quantum electronics is emerging as a potential candidate in information processing but still coherence vs decoherence is a primary concern of the concept. Strong-field coherent processes in band gap materials have led during the last decade to the emergence of high harmonic generation in semiconductors, petahertz electronics, or strong-field quantum states. However, the coherent behavior of the sub-optical cycle-driven electrons has never been directly observed. We report here on the experimental evidence of coherent ultrashort emission of hot electrons from a nanostructured semiconductor. Our method uses sub-wavelength electric field enhancement to localize the electron emission within a nanometer-scale spot. We found similarities with the electron emission from metallic nanotips in the strong-field regime, a topic that has opened a vast domain of applications during the last decade. The electron spectra display both odd and even harmonic orders of the driving femtosecond laser frequency, a signature of the coherent nature of the electron emission and their attosecond timing. Our findings complete our knowledge of phenomena governing coherent strong-field processes in semiconductors and open perspectives for the generation of future quantum devices operating in the strong-field regime.

Philip Dienstbier, Lennart Seiffert, Timo Paschen et al.

Solids exposed to intense electric fields release electrons through tunnelling. This fundamental quantum process lies at the heart of various applications, ranging from high brightness electron sources in DC operation to petahertz vacuum electronics in laser-driven operation. In the latter process, the electron wavepacket undergoes semiclassical dynamics in the strong oscillating laser field, similar to strong-field and attosecond physics in the gas phase. There, the sub-cycle electron dynamics has been determined with a stunning precision of tens of attoseconds, but at solids the quantum dynamics including the emission time window has so far not been measured. Here we show that two-colour modulation spectroscopy of backscattering electrons uncovers the sub-optical-cycle strong-field emission dynamics from nanostructures, with attosecond precision. In our experiment, photoelectron spectra of electrons emitted from a sharp metallic tip are measured as function of the relative phase between the two colours. Projecting the solution of the time-dependent Schrödinger equation onto classical trajectories relates phase-dependent signatures in the spectra to the emission dynamics and yield an emission duration of $710\pm30$ attoseconds by matching the quantum model to the experiment. Our results open the door to the quantitative timing and precise active control of strong-field photoemission in solid state and other systems and have direct ramifications for diverse fields such as ultrafast electron sources, quantum degeneracy studies and sub-Poissonian electron beams, nanoplasmonics and petahertz electronics.

Michael Wiebusch, Henning Heggen, Michael Heil

This contribution presents a pragmatic approach to read-out electronics for drift chambers used in particle physics experiments, specifically for the R3B experiment at GSI. The design uses discrete miniature SMD components and LVDS inputs of a low-cost FPGA to achieve a performance similar to classic ASIC solutions to the problem. The circuit comprises a high gain, low noise amplifier, a custom signal shaper, tailored to the specifics of proportional counter signals, and a leading-edge discriminator with programmable threshold. The presented approach offers an attractive solution for small to medium sized detector systems that require specialized read-out electronics but cannot afford the high cost and development effort associated with ASICs.

Othman Abdullah Almatroud, Viet-Thanh Pham, Giuseppe Grassi et al.

The use of the advancements in memristor technology to construct chaotic maps has garnered significant research attention in recent years. The combination of memristors and nonlinear terms provides an effective approach to proposing novel maps. In this study, we have leveraged memristors and sine terms to develop three-dimensional maps, capable of processing special fixed points. Additionally, we have conducted an in depth study of a specific example (TDMM<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mn>1</mn></msub></semantics></math></inline-formula> map) to demonstrate its dynamics, feasibility, and application for lightweight encryption. Notably, our general approach could be extended to develop higher-dimensional maps, including four- and five-dimensional ones, thereby opening up the possibility to create numerous higher-dimensional maps.

Jason Ceci, Jonah Stegman, Hassan Khan

Electronics repair and service providers offer a range of services to computing device owners across North America -- from software installation to hardware repair. Device owners obtain these services and leave their device along with their access credentials at the mercy of technicians, which leads to privacy concerns for owners' personal data. We conduct a comprehensive four-part study to measure the state of privacy in the electronics repair industry. First, through a field study with 18 service providers, we uncover that most service providers do not have any privacy policy or controls to safeguard device owners' personal data from snooping by technicians. Second, we drop rigged devices for repair at 16 service providers and collect data on widespread privacy violations by technicians, including snooping on personal data, copying data off the device, and removing tracks of snooping activities. Third, we conduct an online survey (n=112) to collect data on customers' experiences when getting devices repaired. Fourth, we invite a subset of survey respondents (n=30) for semi-structured interviews to establish a deeper understanding of their experiences and identify potential solutions to curtail privacy violations by technicians. We apply our findings to discuss possible controls and actions different stakeholders and regulatory agencies should take to improve the state of privacy in the repair industry.

Younsang Cho, Jaeoh Kim, Donghyeon Yu

A general-purpose GPU (GPGPU) is employed in a variety of domains, including accelerating the spread of deep natural network models; however, further research into its effective implementation is needed. When using the compute unified device architecture (CUDA), which has recently gained popularity, the situation is analogous to use the GPUs and its memory space. This is due to the lack of a gold standard for selecting the most efficient approach for CUDA GPU parallel computation. Contrarily, as solving the least absolute shrinkage and selection operator (LASSO) regression fully consists of the basic linear algebra operations, the computation using GPGPU is more effective than other models. Additionally, its optimization problem often requires fast and efficient calculations. The purpose of this study is to provide brief introductions to the implementation approaches and numerically compare the computational efficiency of GPU parallel computation with that of the fast iterative shrinkage-thresholding algorithm for LASSO. This study contributes to providing gold standards for the CUDA GPU parallel computation, considering both computational efficiency and ease of implementation. Based on our comparison results, we recommend implementing the CUDA GPU parallel computation using Python, with either a dynamic-link library or PyTorch for the iterative algorithms.

Jarek Duda

While semiconductor electronics is at heart of modern world, and now uses 5nm or smaller processes of single atoms, it seems there are missing models of actual electron currents in these scales - which could help with more conscious design of future electronics. This article proposes such practical methodology allowing to model approximated electron flows in semiconductor, nonlinear Ohm law in p-n junction, and hopefully more complex systems e.g. built of transistors. It assumes electron hopping between atoms using Maximal Entropy Random Walk based diffusion - chosen accordingly to (Jaynes) maximal entropy principle, this way leading to the same stationary probability density as quantum models. Due to Anderson-like localization in nonhomogeneous lattice of semiconductor, electrons are imprisoned in entopic wells, e.g. requiring to exceed a potential barrier for conductance.

Mahesh V. Sonth, G. Srikanth, Pankaj Agrawal et al.