搜索结果：Parasitic

A parasitic reconfigurable antenna array is a low-power approach for beamforming using passive tunable elements. Prior work on reconfigurable antennas in communication theory is based on ideal radiation pattern abstractions. It does not address the problem of physical realizability. Beamforming with parasitic elements is inherently difficult because mutual coupling creates non-linearity in the beamforming gain objective. We develop a multi-port circuit-theoretic model of the hybrid array with parasitic elements and antennas with active RF chain validated through electromagnetic simulations with a dipole array. We then derive the beamforming weight of the parasitic element using the theoretical beam pattern expression for the case of a single active antenna and multiple parasitic elements. We show that the parasitic beamforming is challenging because the weights are subject to coupled magnitude and phase constraints. We simplify the beamforming optimization problem using a shift-of-origin transformation to the typical unit-modulus beamforming weight. With this transformation, we derive a closed-form solution for the reconfigurable parasitic reactance. We generalize this solution to

As chip manufacturing processes advance to deep submicron nodes, parasitic interconnect effects increasingly dominate the performance of analog and mixed-signal (AMS) circuits and often lead to costly layout iterations. This makes early-stage estimation of parasitic capacitance and resistance important for parasitic-aware design exploration before full physical implementation. However, progress on GNN-based parasitic modeling has been hindered by the lack of public, high-fidelity RC benchmarks that support reproducible evaluation. To address this gap, we introduce ParasGB, the first open-source benchmark suite for pre-layout parasitic parameter prediction on circuit graphs. ParasGB provides large-scale, heterogeneous RC networks extracted with commercial EDA tools from tape-out-proven designs, together with a unified evaluation protocol covering node-level ground capacitance, edge-level resistance, and edge-level coupling capacitance. Within this framework, we benchmark diverse GNN architectures using a standardized training pipeline and expose challenges such as extreme label imbalance, long-tailed parasitic distributions, and strong structural heterogeneity. By establishing a phy

Adaptive andcooperative adaptive cruise control (ACC and CACC) and next generation CACC (CACC+) systems usually employ a constant time headway policy (CTHP) for platooning of connected and autonomous vehicles (CAVs). In ACC, the ego vehicle uses onboard sensors to measure the position and velocity of the predecessor vehicle to maintain a desired spacing. The CACC and CACC+systems use additional information, such as acceleration(s) communicated through vehicle-to-vehicle (V2V) communication of the predecessor vehicle(s); these systems have been shown to result in improved spacing performance, throughput, and safety over ACC. Parasitic dynamics are generally difficult to model and the parasitic parameters (delay, lag, etc.) are difficult to obtain. Parasitic actuation delays can have deleterious effects and impose limits on the mobility and safety of CAVs. It is reasonable to assume that the bounds on parasitic actuation delays are known a priori. For CAVs, we need to address both internal stability and string stability in the presence of parasitic actuation delays. This requires robustness of string and internal stability for all values of parasitic actuation delays that are within

To achieve higher system energy efficiency, SRAM in SoCs is often customized. The parasitic effects cause notable discrepancies between pre-layout and post-layout circuit simulations, leading to difficulty in converging design parameters and excessive design iterations. Is it possible to well predict the parasitics based on the pre-layout circuit, so as to perform parasitic-aware pre-layout simulation? In this work, we propose a deep-learning-based 2-stage model to accurately predict these parasitics in pre-layout stages. The model combines a Graph Neural Network (GNN) classifier and Multi-Layer Perceptron (MLP) regressors, effectively managing class imbalance of the net parasitics in SRAM circuits. We also employ Focal Loss to mitigate the impact of abundant internal net samples and integrate subcircuit information into the graph to abstract the hierarchical structure of schematics. Experiments on 4 real SRAM designs show that our approach not only surpasses the state-of-the-art model in parasitic prediction by a maximum of 19X reduction of error but also significantly boosts the simulation process by up to 598X speedup.

In 6G, MIMO dimensions continue to scale, yet the increased cost, power consumption, and hardware complexity associated with growing RF chains limit practical deployment. Parasitic antennas offer a promising alternative that can add spatial degrees of freedom and array gain without a proportional increase in RF chains. From a communication perspective, prior work on parasitic antennas has primarily focused on adjusting continuous reactance values using varactors, but such varactor-based tuning has increased cost and complexity in the analog control and practical RF circuit design. This paper proposes a multi-active multi-parasitic antenna (MAMP) architecture with binary controllers, where each parasitic element operates in one of two discrete reactance states. To validate the practicality of the system, we experimentally identify array geometries that best match the actual radiation patterns with those of the mathematical model through HFSS simulations. We express the induced current vector as a quadratic function of the binary state vector, and propose a pair of discrete reactance values that minimize the relative error of the proposed model while being implementable with off-the-

In this article, we study the impact of self-heating effects (SHEs) and middle of line (MOL) and back-end of line (BEOL) induced parasitics on multi-tier CFET design, where multiple nanosheet devices are vertically stacked. We analyze and compare the 4-tier CFET design with the conventional 2-tier CFET, using TCAD models calibrated to experimental measurements. Additionally, TCAD simulations are used to model and analyze SHE-induced heat distribution and temperature profiles and to extract the detailed parasitic RC network from 3D models of CMOS inverters designed with full MOL and BEOL interconnects. At the device level, the maximum temperature rise (TMAX) caused by SHE in nFET and pFET devices of the 2-tier CFET architecture is 62 K and 74 K, respectively. Due to the increased distance from the substrate heat sink, the upper-tier nFET and pFET devices in the 4-tier design show higher TMAX of 83.5 K and 98.5 K and more heat trapping in the stacked layers. Furthermore, in the 4-tier CFET-based CMOS inverters, the BEOL-induced parasitic RCs are, respectively, 10 and 6.5 times higher in the top-tier than in the 2-tier CFET-based inverters. In the bottom tier, the corresponding parasi

Microring resonators enable the enhancement of nonlinear frequency mixing processes, generating output fields at frequencies that widely differ from the inputs, in some cases by more than an octave. The efficiency of such devices depends on effective in- and out-coupling between access waveguides and the microrings at these widely separated frequencies. One successful approach is to separate the coupling task across multiple waveguides, with a cutoff waveguide (a waveguide that does not support guided modes above a certain wavelength) being judiciously used to prevent unwanted excessive overcoupling at low frequencies. Here, we examine how such a cutoff waveguide can still induce parasitic loss in the coupling region of a microring resonator, thereby impacting nonlinear device performance. We verified this parasitic loss channel through both experiment and simulation, showing that a waveguide optimized for 532 nm (visible) and 780 nm (near-infrared), while nominally cut off at 1550 nm, can still introduce significant parasitic loss at telecom wavelengths. This is studied in the context of visible-telecom optical parametric oscillation, where the excess parasitic loss can be strong

This letter presents a generalized technique for cancelling the parasitic inductance of capacitors using a lattice network. The Z-matrix-based equivalent transformation shows that the conventional vertically symmetric inductance condition is only a sufficient condition and can be relaxed. A vertically asymmetric lattice network is then proposed, enabling the parasitic inductance of capacitors to be cancelled by adjusting only one inductance. The method is further extended to common-mode noise reduction using a common-mode choke, and both concepts are experimentally verified.

Superconducting qubits show great promise to realize practical quantum computers from micro-fabricated integrated circuits. However, their solid-state architecture bears the burden of parasitic modes in qubit materials and the control circuitry which cause decoherence and interfere with qubits. Here, we present evidence that wirebonds, which are used to contact the micro-circuits and to realize chip-to-chip airbridges, may contain parasitic Josephson junctions. In our experiment, such a junction was enclosed in a superconducting loop and so gave rise to the formation an RF-SQUID which interfered with a nearby flux-tunable transmon qubit. Periodic signatures observed in magnetic field sweeps revealed a strong AC-dispersive coupling of the parasitic RF-SQUID to both the qubit and its readout resonator, in addition to the DC-inductive coupling between RF-SQUID and qubit. Our finding sheds light on a previously unknown origin of decoherence due to parasitic Josephson junctions in superconducing circuits.

Soil-transmitted helminth (STH) infections continuously affect a large proportion of the global population, particularly in tropical and sub-tropical regions, where access to specialized diagnostic expertise is limited. Although manual microscopic diagnosis of parasitic eggs remains the diagnostic gold standard, the approach can be labour-intensive, time-consuming, and prone to human error. This paper aims to utilize a vision language model (VLM) such as Microsoft Florence that was fine-tuned to localize all parasitic eggs within microscopic images. The preliminary results show that our localization VLM performs comparatively better than the other object detection methods, such as EfficientDet, with an mIOU of 0.94. This finding demonstrates the potential of the proposed VLM to serve as a core component of an automated framework, offering a scalable engineering solution for intelligent parasitological diagnosis.

The magnetorotational instability (MRI) is considered a leading mechanism for driving angular momentum transport in differentially rotating astrophysical flows, including accretion disks and protoneutron stars. This process is mediated by the exponential amplification of the magnetic field whose final amplitude is envisioned to be limited by secondary (parasitic) instabilities. In this paper, we investigated the saturation of the MRI via parasitic modes relaxing previous approximations. We carried out the first systematic analysis of the evolution of parasitic modes as they feed off the exponentially growing MRI while being advected by the background shear flow. We provide the most accurate calculation of the amplification factor to which the MRI can grow before the fastest parasitic modes reach a comparable amplitude. We find that this amplification factor is remarkably robust, depending only logarithmically on the initial amplitude of the parasitic modes, in reasonable agreement with numerical simulations. Based on these insights, and guided by numerical simulations, we provide a simple analytical expression for the amplification of magnetic fields responsible for MRI-driven angu

Diffusion-based microfluidic gradient generators (DMGGs) are essential for various in-vitro studies due to their ability to provide a convection-free concentration gradient. However, these systems, often referred to as membrane-based DMGGs, exhibit delayed gradient formation due to the incorporated flow-resistant membrane. This limitation substantially hinders their application in dynamic and time-sensitive studies. Here, we accelerate the gradient response in DMGGs by removing the membrane and implementing new geometrical configurations to compensate for the membrane's role in suppressing parasitic flows. We introduce these novel configurations into two microfluidic designs: the H-junction and the Y-junction. In the H-junction design, parasitic flow is redirected through a bypass channel following the gradient region. The Y-junction design features a shared discharge channel that allows converging discharge flow streams, preventing the buildup of parasitic pressure downstream of the gradient region. Using hydraulic circuit analysis and fluid dynamics simulations, we demonstrate the effectiveness of the H-junction and Y-junction designs in suppressing parasitic pressure flows. Thes

This study investigates the stiffness characteristics of the Sprint Z3 head, also known as 3-PRS Parallel Kinematics Machines, which are among the most extensively researched and viably successful manipulators for precision machining applications. Despite the wealth of research on these robotic manipulators, no previous work has demonstrated their stiffness performance within the parasitic motion space. Such an undesired motion influences their stiffness properties, as stiffness is configuration-dependent. Addressing this gap, this paper develops a stiffness model that accounts for both the velocity-level parasitic motion space and the regular workspace. Numerical simulations are provided to illustrate the stiffness characteristics of the manipulator across all considered spaces. The results indicate that the stiffness profile within the parasitic motion space is both shallower and the values are smaller when compared to the stiffness distribution across the orientation workspace. This implies that evaluating a manipulator's performance adequately requires assessing its ability to resist external loads during parasitic motion. Therefore, comprehending this aspect is crucial for red

This work extends recent developments in studying human--machine interaction by scaling from individual game-theoretic models to a societal-level model. We adopt a Graphon Mean-Field Game (GMFG) that models the interaction among four groups of internally-homogeneous but externally-heterogeneous agents in a shared environment. Our results show that parasitism can masquerade as productive learning, with knowledge distribution and actions appearing healthy while being driven by machine coupling rather than independent investigation. To detect this, we measure the direction of information flow and belief entropy of the environment, revealing that human to machine channel dominates across all scenarios, with the asymmetry intensifying under parasitism. We further demonstrate that the system exhibits coexisting mutualistic and parasitic equilibria, where environmental noise can induce a tipping point that shifts agents past the cognitive cost barrier. These emergent phenomena are not designed into any individual agent but arise from the collective interaction structure, underscoring the need to study the sociology of humans and machines holistically as a complex system.

We report the development of a cryogenic powder filter that simultaneously offers high attenuation of radio-frequency (RF) signals in the gigahertz (GHz) range and minimized parasitic capacitance to ground. Conventional powder filters, which consist of a signal line passing through a metal powder-filled housing, attenuate high-frequency signals via the skin effect. However, these designs often suffer from significant parasitic capacitance between the signal line and the grounded chassis, which can compromise the performance of sensitive measurement setups by limiting their frequency bandwidth. In this work, we demonstrate that a multilayer powder filter design effectively achieves both high RF attenuation and reduced parasitic capacitance. This solution suppresses sample heating due to the unintentional intrusion of RF signals through the wiring, without degrading the performance of the measurement setup.

Superconducting qubits are among the most promising candidates for building quantum computers. Despite significant improvements in qubit coherence, achieving a fault-tolerant quantum computer remains a major challenge, largely due to imperfect gate fidelity. A key source of this infidelity is the parasitic interaction between coupled qubits, which this thesis addresses in two- and three-qubit circuits. This parasitic interaction causes a bending between computational and non-computational levels, leading to a parasitic ZZ interaction. The thesis first investigates the possibility of zeroing the ZZ interaction in two qubit combinations: a pair of interacting transmons, and a hybrid pair of a transmon coupled to a capacitively shunted flux qubit (CSFQ). The theory developed is used to accurately simulate experimental results from our collaborators, who measured a CSFQ-transmon pair with and without a cross-resonance (CR) gate. The strong agreement between theory and experiment motivated further study of a CR gate that achieves 99.9% fidelity in the absence of static ZZ interaction. Since the CR pulse adds an additional ZZ component to the static part, a new strategy called dynamical

We propose a novel approach for boosting the realized gain in enhanced directivity arrays with both active and parasitic dipoles as radiating elements. The optimization process involves two main objectives: maximizing the end-fire gain and minimizing the reflection coefficient to ensure high realized gain. In the first step, the current excitation vector of the fully driven array is selected to maximize the end-fire gain. Then, all but one of the dipoles are reactively loaded according to their input impedance. Following that, the optimization focuses on the inter-element distance, computing the one that offers a favorable balance between the gain and the total efficiency. This multi-objective optimization leverages the differential evolution (DE) algorithm and utilizes a simple wire dipole as the unit element. Full-wave simulations further confirm the accuracy of our theoretical results. Our two- and three-element parasitic arrays achieve realized gain comparable to state-of-the-art designs, without relying on intricate unit elements or resource-intensive simulations. Moreover, our four- and five-element parasitic arrays deliver the highest realized gain values reported in the lit

In this work, we develop a linear model ODE to study the parasitic capillary ripples present on steep Stokes waves when a small amount of surface tension is included in the formulation. Our methodology builds upon the exponential asymptotic theory of Shelton & Trinh (J. Fluid Mech., vol. 939, 2022, A17), who demonstrated that these ripples occur beyond-all-orders of a small-surface-tension expansion. Our model equation, a linear ODE forced by solutions of the Stokes wave equation, forms a convenient tool to calculate numerical and asymptotic solutions. We show analytically that the parasitic capillary ripples that emerge in solutions to this linear model have the same asymptotic scaling and functional behaviour as those in the fully nonlinear problem. It is expected that this work will lead to the study of parasitic capillary ripples that occur in more general formulations involving viscosity or time-dependence.

The observable quantities in optical interferometry, which are the modulus and the phase of the complex visibility, may be corrupted by parasitic fringes superimposed on the genuine fringe pattern. These fringes are due to an interference phenomenon occurring from straylight effects inside an interferometric instrument. We developed an analytical approach to better understand this phenomenon when straylight causes crosstalk between beams. We deduced that the parasitic interference significantly affects the interferometric phase and thus the associated observables including the differential phase and the closure phase. The amount of parasitic flux coupled to the piston between beams appears to be very influential in this degradation. For instance, considering a point-like source and a piston ranging from $λ/500$ to $λ/5$ in L band ($λ=3.5\:μ$m), a parasitic flux of about 1\% of the total flux produces a parasitic phase reaching at most one third of the intrinsic phase. The piston, which can have different origins (instrumental stability, atmospheric perturbations, ...), thus amplifies the effect of parasitic interference. According to specifications of piston correction in space or