Gating Parameters Research Articles

Technical note: Characterization of a multi-point scintillation dosimetry research platform for a low-field MR-Linac.

MRI-guided radiation therapy (MRgRT) requires unique quality assurance equipment to address MR-compatibility needs, minimize electron return effect, handle complex dose distributions, and evaluate real-time dosimetry for gating. Plastic scintillation detectors (PSDs) are an attractive option to address these needs. To perform a comprehensive characterization of a multi-probe PSD system in a low-field 0.35T MR-linac, including detector response assessment and gating performance. A four-channel PSD system (HYPERSCINT RP-200) was assembled. A single channel was used to evaluate repeatability, percent depth dose (PDD), detector response as a function of orientation with respect to the magnetic field, and intersession variability. All four channels were used to evaluate repeatability, linearity, and output factors. The four PSDs were integrated into an MR-compatible motion phantom at isocenter and in gradient regions. Experiments were conducted to evaluate gating performance and tracking efficacy. For repeatability, the maximum standard deviation of repeated measurements was 0.13% (single PSD). Comparing the PSD to reference data, PDD had a maximum difference of 1.12% (10cm depth, 6.64×6.64 cm2). Percent differences for rotated detector setups were negligible (<0.3%). All four PSDs demonstrated linear response over 10-1000 MU delivered and the maximum percent difference between the baseline and measured output factors was 0.78% (2.49×2.49 cm2). Gating experiments had 400 cGy delivered to isocenter with<0.8 cGy variation for central axis measures and<0.7 cGy for the gradient sampled region. Real-time dosimetry measurements captured spurious beam-on incidents that correlated to tracking algorithm inaccuracies and highlighted gating parameter impact on delivery efficiency. Characterization of the multi-point PSD dosimetry system in a 0.35T MR-linac demonstrated reliability in a low-field MR-Linac setting, with high repeatability, linearity, small intersession variability, and similarity to baseline data for PDD and output factors. Time-resolved, multi-point dosimetry also showed considerable promise for gated MR-Linac applications.

Mitigation of microwave crosstalk with parameterized single-qubit gate in superconducting quantum circuits

In superconducting qubit systems, microwave crosstalk among the qubit control lines is a prominent source of errors for gate operations, particularly when implemented simultaneously in a multiqubit system. In this work, we present an experimental study of crosstalk mitigation for the case of single-qubit gate operation, which involves the universal U3 gate decomposition into two Xπ/2 gates and three virtual Z gates. We demonstrate that by optimizing the virtual Z gate parameters, the crosstalk can be effectively mitigated, with the single-qubit gate fidelity recovered to the level comparable to that in the absence of crosstalk.

Effect of temperature modulation on the performance of top contact bottom gate organic thin film transistor

This research article investigates the influence of temperature variations on the performance parameters of Top Contact Bottom Gate (TCBG) Organic Thin Film Transistors (OTFTs). Employing the Arrhenius equation and Schottky emission model, the study explores temperature-dependent charge carrier mobility and injection mechanisms. Analyzing a wide range of parameters, the research unveils a complex interplay between temperature and OTFT behavior. The linear and saturation mobilities exhibit a decline due to enhanced carrier-phonon interactions and lattice vibrations. Transconductance and ION/IOFF ratio display a concomitant decrease at higher temperatures, indicative of the heightened susceptibility of OTFT performance to temperature fluctuations. The depreciation in subthreshold slope and threshold voltage with rising temperatures underscores the influence of temperature on trap states. The research emphasizes the importance of considering temperature’s impact during OTFT design for diverse applications, enhancing the reliability and functionality of organic electronic devices.

Performances of gate stacked heterojunction SELBOX and SOI tunnel FETs including interface trap charges: A simulation study

In this work, the influence of interface trap charges (ITCs) on electrical parameters of gate stacked heterojunction silicon on insulator Tunnel FET (GSHJ-SOITFET) and GSHJ-TFET on SELBOX substrate (GSHJ-STFET) is investigated using TCAD simulator. Here, two types of trap distributions like Uniform and Gaussian are considered at Silicon/gate oxide interface. The influence of ITCs on transfer characteristic, electron density, energy band diagram (ON, OFF and Ambipolar states), transconductance (gm), and cut-off frequency (ft) are highlighted for these two structures. Moreover, the linearity parameters are highlighted in the presence of ITCs of these structures. It is seen that DC, RF/analog, and linearity characteristics of GSHJ-STFET are immune to ITCs compared to GSHJ-SOITFET. Also, GSHJ-STFET has improved electrical behavior than GSHJ-SOITFET. The proposed fabrication processing steps of GSHJ-STFET is also highlighted in this work. Finally, a comparative study of electrical parameters for GSHJ-STFET with the existing literature is highlighted.

Encrypt ECG signals based on the optimized 4-bit absolute value comparator

As people become more conscious of private information security and advancements in Electrocardiogram (ECG) identity identification technology, the need to encrypt private data intensifies. New wearable medical devices continuously gather vast amounts of personal health data, underscoring the urgency of safeguarding privacy. This paper focuses on encrypting and decrypting rich characteristic signals like ECG. It delves into the core aspects, including the logic circuit diagram of a four-bit digit absolute comparator using Complementary Metal Oxide Semiconductor (CMOS) technology and optimizing delay through logic effort to reduce power consumption. XOR gates and other gate circuits, as well as complement subtraction principles, are employed in building logic gates. Calculating delay involves selecting the longest critical path and determining the specific parameters (g, h, and p) for each logic gate. Subsequently, energy consumption is computed using a formula, revealing a trade-off between energy and delay. After optimization, the circuit's energy consumption is 184.832, and the delay is 34.13. In summary, the 4-bit absolute value detector plays a crucial role in encryption, addressing the growing concern for privacy and data security.

DG-ALETSK: A High-Dimensional Fuzzy Approach With Simultaneous Feature Selection and Rule Extraction

Fuzzy or neuro-fuzzy systems have been successfully employed in many areas, but their limitation in solving high-dimensional problems remains a challenging task. On the other hand, fuzzy model based feature selection (FS) approaches have been well studied but, because of the above limitation, are rarely applied to high-dimensional data, which exactly needs feature reduction. In this paper, we design a novel adaptive Ln-Exp softmin operator as the approximator of the minimum T-norm, which is equipped in Takagi-Sugeno-Kang (TSK) fuzzy systems to make them capable of dealing with high-dimensional problems. The adaptive Ln-Exp softmin based TSK (ALETSK) model is developed. Then, we improve the gate function by introducing an enhanced scheme and propose a double groups of gates based ALETSK (DG-ALETSK) fuzzy approach to simultaneously conduct FS and rule extraction (RE), where the gate function is a function measuring the importance of the features or rules and it acts like a gate. More specifically, a group of feature gates are embedded in the antecedents of ALETSK for FS. Meanwhile, another group of rule gates are embedded in the consequents of ALETSK for RE. In one training phase, the gate parameters are trained along with the system parameters, so that the FS and RE are done together. Our proposed DG-ALETSK is effective and time-saving for FS and RE in the high-dimensional tasks, which is verified by the numerical experiments.

A comparative study of noise performance for InP HEMT

A comparative study to determine the scaling rules of noise model parameters for InP HEMT devices between 90 nm and 70 nm process is proposed in this paper. The expressions of noise model parameters in terms of four noise parameters are also derived. Both gate length and gate width scalable rules of the gate and drain noise model parameters (P and R) are discussed between 90 nm and 70 nm devices. Good agreement is obtained based on the comparison of measured and modeled noise parameters up to 45 GHz.

Chiral‐Quantum‐Optics‐Based Supervised Learning

AbstractBased on the excellent control of single photons realized by atom‐photon‐chiral couplings,a novel quantum‐optics scheme for supervised learning is proposed. The single‐photon rotating and phase‐shift operations, which can be controlled by another single photon, are realized by proper atom‐photon‐chiral couplings. Then, an algorithm to perform the supervised learning tasks, composed by integrating the realized gates, and implemented by the tunable gate parameters, is realized.

GSQAS: Graph Self-supervised Quantum Architecture Search

Quantum Architecture Search (QAS) is a promising approach for designing quantum circuits specifically tailored for variational quantum algorithms (VQAs). However, existing QAS algorithms require calculating the ground-truth performances of a substantial number of quantum circuits during the search process, rendering them computationally demanding and limiting their applicability to large-scale quantum circuits. Recently, a predictor-based QAS has been proposed to address this challenge by estimating circuit performance directly based on their structures using a predictor trained on a set of labeled quantum circuits. However, the predictor is trained by purely supervised learning, which suffers from poor generalization ability when labeled training circuits are scarce. It is highly time-consuming to obtain a substantial number of labeled quantum circuits because the gate parameters of quantum circuits need to be optimized until convergence to obtain their ground-truth performances. To overcome these limitations, we propose GSQAS, a graph self-supervised QAS that trains a predictor by self-supervised learning. Specifically, we first pre-train a graph encoder using a well-designed pretext task on a large number of unlabeled quantum circuits, aiming to generate meaningful representations of quantum circuits. Subsequently, the downstream predictor is trained on a small set of quantum circuits’ representations and their corresponding labels. Once the encoder is trained, it becomes applicable to various downstream tasks. To effectively encode spatial topology information and avoid huge dimensions of feature vectors for large-scale quantum circuits, we propose a graph-based encoding scheme for quantum circuits. Simulation results on QAS for variational quantum eigensolver and quantum state classification demonstrate that GSQAS outperforms the state-of-the-art predictor-based QAS, yielding superior performance with fewer labeled circuits.

Modeling and analysis of voltage gating of gap junction channels at a single-channel level

The advancement of single-channel-level recording via the patch-clamp technique has provided a powerful means of assessing the detailed behaviors of various types of ion channels in native and exogenously expressed cellular environments. However, such recordings of gap junction (GJ) channels are hampered by unique challenges that are related to their unusual intercellular configuration and natural clustering into densely packed plaques. Thus, the methods for reliable cross-correlation of data recorded at macroscopic and single-channel levels are lacking in studies of GJs. To address this issue, we combined our previously published four-state model (4SM) of GJ channel gating by voltage with maximum likelihood estimation (MLE)-based analyses of electrophysiological recordings of GJ channel currents. First, we consider evaluation of single-channel characteristics and the methods for efficient stochastic simulation of single GJ channels from the kinetic scheme described by 4SM using data obtained from macroscopic recordings. We then present an MLE-based methodology for extraction of information about transition rates for GJ channels and, ultimately, gating parameters defined in 4SM from recordings with visible unitary events. The validity of the proposed methodology is illustrated using stochastic simulations of single GJ channels and is extended to electrophysiological data recorded in cells expressing connexin 43 tagged with enhanced green fluorescent protein.

An integrated approach to evaluate the functional effects of disease-associated NMDA receptor variants

The NMDA receptor (NMDAR) is a ubiquitously expressed glutamate-gated ion channel that plays key roles in brain development and function. Not surprisingly, a variety of disease-associated variants have been identified in genes encoding NMDAR subunits. A critical first step to assess whether these variants contribute to their associated disorder is to characterize their effect on receptor function. However, the complexity of NMDAR function makes this challenging, with many variants typically altering multiple functional properties. At synapses, NMDARs encode pre- and postsynaptic activity to carry a charge transfer that alters membrane excitability and a Ca2+ influx that has both short- and long-term signaling actions. Here, we characterized epilepsy-associated variants in GluN1 and GluN2A subunits with various phenotypic severity in HEK293 cells. To capture the complexity of NMDAR gating, we applied 10 glutamate pulses at 10 Hz to derive a charge integral. This assay is advantageous since it incorporates multiple gating parameters – activation, deactivation, and desensitization – into a single value. We then integrated this gating parameter with Mg2+ block and Ca2+ influx using fractional Ca2+ currents to generate indices of charge transfer and Ca2+ transfer over wide voltage ranges. This approach yields consolidated parameters that can be used as a reference to normalize channel block and allosteric modulation to better define potential patient treatment. This is especially true for variants in the transmembrane domain that affect not only receptor gating but also often Mg2+ block and Ca2+ permeation.

Rate enhancement of gated drift-diffusion process by optimal resetting.

"Gating" is a widely observed phenomenon in biochemistry that describes the transition between the activated (or open) and deactivated (or closed) states of an ion-channel, which makes transport through that channel highly selective. In general, gating is a mechanism that imposes an additional restriction on a transport, as the process ends only when the "gate" is open and continues otherwise. When diffusion occurs in the presence of a constant bias to a gated target, i.e., to a target that switches between an open and a closed state, the dynamics essentially slow down compared to ungated drift-diffusion, resulting in an increase in the mean completion time, ⟨TG⟩ > ⟨T⟩, where T denotes the random time of transport and G indicates gating. In this work, we utilize stochastic resetting as an external protocol to counterbalance the delay due to gating. We consider a particle in the positive semi-infinite space that undergoes drift-diffusion in the presence of a stochastically gated target at the origin and is moreover subjected to rate-limiting resetting dynamics. Calculating the minimal mean completion time ⟨Tr⋆G⟩ rendered by an optimal resetting rate r⋆ for this exactly solvable system, we construct a phase diagram that owns three distinct phases: (i) where resetting can make gated drift-diffusion faster even compared to the original ungated process, ⟨Tr⋆G⟩<⟨T⟩<⟨TG⟩, (ii) where resetting still expedites gated drift-diffusion but not beyond the original ungated process, ⟨T⟩≤⟨Tr⋆G⟩<⟨TG⟩, and (iii) where resetting fails to expedite gated drift-diffusion, ⟨T⟩<⟨TG⟩≤⟨Tr⋆G⟩. We also highlight various non-trivial behaviors of the completion time as the resetting rate, gating parameters, and geometry of the set-up are carefully ramified. Gated drift-diffusion aptly models various stochastic processes such as chemical reactions that exclusively take place in certain activated states of the reactants. Our work predicts the conditions under which stochastic resetting can act as a useful strategy to enhance the rate of such processes without compromising their selectivity.

Profiling of Natural Killer Interactions With Cancer Cells Using Mass Cytometry

We developed a comprehensive method for functional assessment of the changes in immune populations and killing activity of peripheral blood mononuclear cells after cocultures with cancer cells using mass cytometry. In this study, a 43-marker mass cytometry panel was applied to a coculture of immune cells from healthy donors’ peripheral blood mononuclear cells with diverse cancer cell lines. DNA content combined with classical CD45 surface staining was used as gating parameters for cocultures of immune cells (CD45high/DNAlow) with hematological (CD45low/DNAhigh) and solid cancer cell lines (CD45neg/DNAhigh). This strategy allows for universal discrimination of cancer cells from immune populations without the need for a specific cancer cell marker and simultaneous assessment of phenotypical changes in both populations. The use of mass cytometry allows for simultaneous detection of changes in natural killer, natural killer T cell, and T cell phenotypes and degranulation of immune populations upon target recognition, analysis of target cells for cytotoxic protein granzyme B content, and cancer cell death. These findings have broad applicability in research and clinical settings with the aim to phenotype and assess functional changes following not only NK-cancer cell interactions but also the effect of those interactions on other immune populations.

Temperature-dependent analysis of heterojunction-free GaN FinFET through optimization of controlling gate parameters and dielectric materials

Abstract This work presents the temperature-dependent analysis of heterojunction-free gallium nitride (GaN) FinFET through optimization of controlling gate parameters and dielectric materials. The temperature-dependent performance evaluation presents in terms of the transfer characteristic, transconductance, subthreshold swing (SS), and drain-induced barrier lowering (DIBL). Further, parametric assessment has been performed by gate length (L g) and oxide thickness (t ox) variation for optimization. Moreover, the different gate dielectric materials (Al2O3, ZrO2, and Si3N4) have also been used for different temperatures to optimize suitable gate dielectric material for improved performance of the device. Thus, GAN FinFET can be considered a promising component in high temperatures in IC and RF amplifiers.

Accurate Methods for the Analysis of Strong-Drive Effects in Parametric Gates

The ability to perform fast, high-fidelity entangling gates is a requirement for a viable quantum processor. In practice, achieving fast gates often comes with the penalty of strong-drive effects that are not captured by the rotating-wave approximation. These effects can be analyzed in simulations of the gate protocol, but those are computationally costly and often hide the physics at play. Here, we show how to efficiently extract gate parameters by directly solving a Floquet eigenproblem using exact numerics and a perturbative analytical approach. As an example application of this toolkit, we study the space of parametric gates generated between two fixed-frequency transmon qubits connected by a parametrically driven coupler. Our analytical treatment, based on time-dependent Schrieffer-Wolff perturbation theory, yields closed-form expressions for gate frequencies and spurious interactions, and is valid for strong drives. From these calculations, we identify optimal regimes of operation for different types of gates including $i$swap, controlled-Z, and cnot. These analytical results are supplemented by numerical Floquet computations from which we directly extract drive-dependent gate parameters. This approach has a considerable computational advantage over full simulations of time evolutions. More generally, our combined analytical and numerical strategy allows us to characterize two-qubit gates involving parametrically driven interactions, and can be applied to gate optimization and cross-talk mitigation such as the cancelation of unwanted $ZZ$ interactions in multiqubit architectures.

Gating defects in K1126I, a putative hypokalemic periodic paralysis mutation in hNav1.4.

We investigated the impact of mutation K1126I reported to be associated with hypokalemic periodic paralysis, but not yet characterized. The location of this mutation is the extracellular positive charge in domain III S4, and characterization of artificial mutations at this locus suggest it is outside the electric field. We used cut-open oocyte electrophysiology to characterize the steady-state and kinetic gating parameters in wild type hNaV1.4, K1126I in both the native background and with incorporation of the 1303IFM/QQQ mutation to isolate activation from fast inactivation. K1126I produced an 11 mV hyperpolarizing (left-)shift in the midpoint of the steady-state fast inactivation curve, and decreased slope. Activation midpoint was left-shifted by 4 mV by K1126I with decreased slope. Recovery from fast inactivation was slowed and entry from closed states was accelerated by K1126I, with open-state fast inactivation and deactivation not significantly altered. Simulated action potentials for which steady-state fast inactivation and activation parameters were incorporated showed a 10 mV attenuation of the skeletal muscle fiber action potential. We used the IFM/QQQ background to compare slow inactivation in wild type and K1126I channels. The midpoint of the steady-state slow inactivation was hyperpolarized by 8 mV in K1126I/QQQ compared to hNaV1.4 / QQQ, with onset unaffected and recovery accelerated. This work was supported by an Institutional Development Award (IDeA) from the National Institute of General Medical Sciences of the National Institutes of Health under Grant #P20GM103408.

Optimization of CFTR gating through the evolution of its extracellular loops.

CFTR chloride channel mutations cause the lethal and incurable disease cystic fibrosis (CF). CFTR is activated by phosphorylation, and phosphorylated channels exhibit "bursting" behavior-"bursts" of openings separated by short "flickery" closures and flanked by long "interburst" closures-driven by ATP binding/hydrolysis at two nucleotide-binding domains. The human channel (hCFTR) and the distant zebrafish ortholog (zCFTR) display differences both in their gating properties and structures. In phosphorylated ATP-bound hCFTR, the hR117 side chain, conserved across evolution, forms an H-bond that stabilizes the open state. Lack of that bond in the hR117H mutant causes CF. In the phosphorylated ATP-bound zCFTR structure that H-bond is not observable. Here, we show that the zR118H mutation does not affect the function of zCFTR. Instead, we identify an H-bond between the zS109 and zS120 side chains of phosphorylated ATP-bound, but not of unphosphorylated apo-, zCFTR. We investigate the role of that interaction using thermodynamic mutant cycles built on gating parameters determined in inside-out patch clamp recordings. We find that zS109 indeed forms an H-bond with zN120 in the flickery closed state, but not in the open or interburst closed states. Although in hCFTR an isoleucine (hI119) replaces the asparagine, mutation hS108A produces a strong hR117H-like phenotype. Since the effects of the latter two mutations are not additive, we conclude that in hCFTR these two positions interact, and the hS108-hR117 and hR117-hE1124 H-bonds cooperate to stabilize the open state. These findings highlight an example of how the gating mechanism was optimized during CFTR molecular evolution.

Synaptic Modulation of Conductivity and Magnetism in a CoPt‐Based Electrochemical Transistor

Among various types of neuromorphic devices toward artificial intelligence, the electrochemical synaptic transistor emerges, in which the channel conductance is modulated by the insertion of ions according to the history of gate voltage across the electrolyte. Despite the striking progress in exploring novel channel materials, few studies report on the ferromagnetic metal‐based synaptic transistors, limiting the development of spin‐based neuromorphic devices. Herein, synaptic modulation of both conductivity and magnetism based on an electrochemical transistor with a metallic channel of ferromagnetic CoPt alloy is presented. Its essential synaptic functionalities in the transistor are demonstrated, including depression and potentiation of synaptic weight and paired‐pulse facilitation (PPF). Then, short‐to‐long‐term plasticity transition induced by different gate parameters, such as amplitude, duration, and frequency, is shown. Furthermore, the device presents multilevel and reversible nonvolatile states in both conductivity and coercivity. The results demonstrate simultaneous modulation of conductivity and magnetism, paving the way for building future spin‐based multifunctional synaptic devices.

Ansatz-Independent Variational Quantum Classifiers and the Price of Ansatz

The paradigm of variational quantum classifiers (VQCs) encodes classical information as quantum states, followed by quantum processing and then measurements to generate classical predictions. VQCs are promising candidates for efficient utilizations of noisy intermediate scale quantum (NISQ) devices: classifiers involving M-dimensional datasets can be implemented with only lceil log _2 M rceil qubits by using an amplitude encoding. A general framework for designing and training VQCs, however, is lacking. An encouraging specific embodiment of VQCs, quantum circuit learning (QCL), utilizes an ansatz: a circuit with a predetermined circuit geometry and parametrized gates expressing a time-evolution unitary operator; training involves learning the gate parameters through a gradient-descent algorithm where the gradients themselves can be efficiently estimated by the quantum circuit. The representational power of QCL, however, depends strongly on the choice of the ansatz, as it limits the range of possible unitary operators that a VQC can search over. Equally importantly, the landscape of the optimization problem may have challenging properties such as barren plateaus and the associated gradient-descent algorithm may not find good local minima. Thus, it is critically important to estimate (i) the price of ansatz; that is, the gap between the performance of QCL and the performance of ansatz-independent VQCs, and (ii) the price of using quantum circuits as classical classifiers: that is, the performance gap between VQCs and equivalent classical classifiers. This paper develops a computational framework to address both these open problems. First, it shows that VQCs, including QCL, fit inside the well-known kernel method. Next it introduces a framework for efficiently designing ansatz-independent VQCs, which we call the unitary kernel method (UKM). The UKM framework enables one to estimate the first known computationally-determined bounds on both the price of ansatz and the price of any speedup advantages of VQCs: numerical results with datatsets of various dimensions, ranging from 4 to 256, show that the ansatz-induced gap can vary between 10 and 20%, while the VQC-induced gap (between VQC and kernel method) can vary between 10 and 16%. To further understand the role of ansatz in VQCs, we also propose a method of decomposing a given unitary operator into a quantum circuit, which we call the variational circuit realization (VCR): given any parameterized circuit block (as for example, used in QCL), it finds optimal parameters and the number of layers of the circuit block required to approximate any target unitary operator with a given precision.

Design and performance analysis of GaN vertical JFETs with ion-implanted gates

We present a comprehensive performance analysis of vertical GaN JFETs via TCAD simulation with unique considerations for gates formed by Mg ion implantation into GaN. The dependence of the specific ON-resistance and pinch-off voltage on the gate and channel design parameters is first evaluated for a JFET with abrupt gate-channel junctions. Then, the influence of the gate acceptor concentration and distribution is studied to elucidate the consequences of incomplete acceptor activation or acceptor diffusion resulting from specialized post-implantation annealing techniques necessary for the activation of p-GaN. Examples of normally-ON and normally-OFF designs with 1.7 kV breakdown voltage for 1.2 kV applications are chosen for the activation and diffusion studies to demonstrate how the pinch-off and conduction characteristics are affected for different channel widths and doping concentrations conducive to each type of operation. Record low specific ON-resistance below 1 mΩ cm2 is predicted for both, but gate acceptor diffusion increases the channel resistance, especially for JFETs designed to be normally-OFF.