Junction Model Research Articles

Nonreciprocal microwave responses in Nb/Au/NbSe2 Josephson junctions

The nonreciprocal Josephson junction, the Josephson diode, opens a door for superconducting circuit technology. Here, we report on nonreciprocal behaviors in Nb/Au/NbSe2 Josephson junctions. The strong asymmetry could be seen in the current–voltage characteristics, which most likely arises from time symmetry breaking due to Abrikosov vortices, indicated by an asymmetric Fraunhofer pattern. The nonreciprocal microwave responses are systematically investigated compared to a numerical simulation based on a resistively and capacitively shunted junction model, which could well capture the asymmetric behaviors and rule out some artifacts such as other parasitic Josephson-like structures. Based on the nonreciprocal microwave responses, we put forward a proposal for a switchable Josephson diode controlled by an external microwave, which might provide an opportunity for next-generation microwave-related superconducting circuits.

Poster Session: Deep Active Learning Using Retinal Image Embedding Vectors to Optimize Training Data Selections.

In recent years, deep learning (DL) has made breakthroughs in many fields. However, the success of DL requires constant re-training with large amounts of data, which is often costly, causing data paucity problems in the medical space. Active learning (AL), on the other hand, aims to reduce the amount of training data while still retaining similar performance through the exploration of the added value of data. In this study, we designed an AL system for training Artificial Intelligence (AI) models to explore faster learning curves while minimizing the need for new data. The model was designed for vessel object detection, using a Deep Neural Network (DNN), for scanning laser ophthalmoscopy (SLO) retinal images. Image embedding vectors were used to proactively select the most informative data. We established a baseline of k-fold cross validation frameworks to measure model performance, where 809 annotated images were randomly selected from our database and divided into multiple regions of interest with minimal selection bias. Using this validation framework, we observed the detection model for vessel junctions could achieve a mean average precision (mAP) of 0.648, precision of 0.645, and recall of 0.703; tested on 135 images. We then tested our active learning system by dividing the dataset into two clusters using image embedding vectors and machine learning clustering algorithms. We observed one cluster converged faster to the desired mAP performance than the other by 2.3x.

Bending of PN junctions in flexoelectric semiconductors

In this paper, electromechanical fields and carrier distributions in a PN junction with coupling to bending deformation are investigated. Based on the macroscopic theory of flexoelectricity and the semiconductor drift-diffusion theory, a one-dimensional model of the PN junction is developed. The effects of doping level, flexoelectric coefficient and material constants on electrons and holes at the equilibrium state are analyzed. The I-V characteristic of the PN junction under the bias voltage is also obtained. The flexoelectric field is demonstrated to affect the built-in electric field of the PN junction and the carrier distributions in the p-doped and n-doped regions, suggesting an avenue for performance tuning of PN junctions.

Weak Radio Frequency Signal Reception Sensitivity Based on DC Superconducting Quantum Interference Devices

Superconducting quantum interference devices (SQUIDs) are promising for detection of extremely weak power regimes for the Internet of Things (IoT) and long-distance satellite networks. In low-frequency magnetic signal measurements, it has demonstrated superior performance over traditional systems. We review the resistively-and-capacitively-shunted junction (RCSJ) model of DC SQUIDs and investigate its transfer function to magnetic flux signals while taking thermal fluctuation into account. We numerically evaluate the influence of bias current and bias flux signal to determine the operating parameters of the device. The performances related to SQUID ring inductance operating at 4.2 K and 77 K are demonstrated. Combined with common radio frequency (RF) front-end modules, including low-noise amplifier (LNA) and low-pass filter, we evaluate the bit-error-rate (BER) performance of the system under different inductance values and inferred that the sensitivity of the system can reach -99 dBm at 100 MHz bandwidth.

Bioprinting 3D muscle tissue supplemented with endothelial-spheroids for neuromuscular junction model

The neuromuscular junction (NMJ) plays a critical role in muscle contraction, and its dysfunction can result in various neuromuscular disorders. In vitro models for studying NMJ are essential for understanding their functions and pathology. However, the engineering of muscle tissue presents challenges for the organization of myofiber-like oriented muscle bundles as well as the induction of vessel formation and innervation. To address these challenges, we fabricated a hybrid muscle construct comprising uniaxially aligned muscle struts and endothelial cell spheroids using a combination of in situ electric field-assisted bioprinting (E-printing) and microdroplet-based spheroid-forming bioprinting (MDS-printing) techniques. This resulted in self-aggregation of human umbilical vein endothelial cells (HUVECs) into spheroids without attachment to the structures. We tested various fabrication parameters, such as electric field and cross-linking conditions, for E-printing and the deposited cell density of MDS-printing, to stabilize the alignment of the human muscle progenitor cells (hMPCs) and HUVEC spheroids, respectively. The stimulated hMPCs efficiently formed fully aligned myofibers, and the incorporation of HUVEC spheroids induced highly upregulated crosstalk between different cell types compared to a simple E-printed hMPC/HUVEC mixture-loaded construct. This improved myogenesis and vessel formation in vitro. In addition, when co-cultured with a motor neuron-like cell (NSC-34) spheroid separated by a channel, we observed considerably improved neuromuscular junction formation compared to those formed with the normally mixed cell-bearing structures. Our findings suggest that this hybrid muscle construct has the potential to enhance muscle tissue engineering by improving biological activities through the incorporation of HUVEC-spheroids and facilitating neuromuscular junction formation through co-culture with NSC-34 spheroids.

Variation Aware Optimization vs Classical Margin Optimization of Superconducting Electronics

We have developed a Superconducting Electronics Circuit Design Optimizer in conjunction with a Statistical Process Variation Josephson Junction Model and formal method of Logic Verification as a means to optimize circuits and mitigate their susceptibility to factors of yield loss. In this paper we will contrast the Yield improvement provided by direct yield optimization against classical methods of margin centering.

Observations in Use of a Tunnel Junction Model in Simulations of Josephson Digital Circuits

Observations in Use of a Tunnel Junction Model in Simulations of Josephson Digital Circuits

Numerical Study of SQUID Array Responses Due to Asymmetric Junction Parameters

Superconducting quantum interference device arrays have been extensively studied for their high magnetic field sensitivity. The performance of these devices strongly depends on the characteristic parameters of their Josephson junctions, i.e. their critical currents and shunt resistances. Using a resistively shunted junction model and including thermal noise, we perform a numerical investigation of the effects of asymmetric Josephson junctions by independently studying variations in the critical currents and junction resistances. We compare the voltage response of a dc-SQUID with a 1D parallel SQUID array and study the maximum transfer function dependence on the number of junctions in parallel, the screening parameter and thermal noise strength. Our results show that the maximum transfer function and linearity increase with the number of junctions in parallel for arrays with different junction resistances, in contrast to SQUID arrays with identical junctions or with spreads in the critical currents.

Advances in In Vitro Models of Neuromuscular Junction: Focusing on Organ-on-a-Chip, Organoids, and Biohybrid Robotics.

The neuromuscular junction (NMJ) is a peripheral synaptic connection between presynaptic motor neurons and postsynaptic skeletal muscle fibers that enables muscle contraction and voluntary motor movement. Many traumatic, neurodegenerative, and neuroimmunological diseases are classically believed to mainly affect either the neuronal or the muscle side of the NMJ, and treatment options are lacking. Recent advances in novel techniques have helped develop in vitro physiological and pathophysiological models of the NMJ as well as enable precise control and evaluation of its functions. This paper reviews the recent developments in in vitro NMJ models with 2D or 3D cultures, from organ-on-a-chip and organoids to biohybrid robotics. Related derivative techniques are introduced for functional analysis of the NMJ, such as the patch-clamp technique, microelectrode arrays, calcium imaging, and stimulus methods, particularly optogenetic-mediated light stimulation, microelectrode-mediated electrical stimulation, and biochemical stimulation. Finally, the applications of the in vitro NMJ models as disease models or for drug screening related to suitable neuromuscular diseases are summarized and their future development trends and challenges are discussed.

Exploratory study of betavoltaic nuclear battery using AlN P[sbnd]N junction

In most of the betavoltaic nuclear batteries, wide band semiconductors such as GaN and SiC have been used as an energy converter. But, the growth of these semiconductors are relatively expensive. In the present work, a new study was conducted on betavoltaic nuclear batteries. In this study, AlN betavoltaic nuclear battery driven by an 90Sr + 90Y radioisotope source was investigated. AlN is wide band semiconductor and economically viable. Then, by Monte Carlo simulation the effects of factors such as type of electrode, dimensions, thickness, and geometric model of PN junction on the battery efficiency were investigated. Finally, it was found that the cubical model has the highest amount of short circuit current, open circuit voltage, and battery efficiency. The value of short circuit current, open circuit voltage, and battery efficiency in this model are 406.05 nA, 5.43 V, and 5.16 %, respectively. In order to compare the efficiency of AlN betavoltaic nuclear battery with the efficiency of GaN and SiC betavoltaic nuclear batteries, first the optimal dimensions were investigated for each of GaN and SiC betavoltaic nuclear batteries and then based on the optimal dimensions obtained for GaN and SiC, the cubical model was investigated for them. The results of the simulations show that, the efficiency of AlN betavoltaic nuclear battery is 24 % more than GaN betavoltaic nuclear battery efficiency and 975 % more than SiC betavoltaic nuclear battery efficiency. Also, the efficiency of the AlN betavoltaic nuclear battery (simulated in this work) is about 130 % more than the efficiency of the GaN betavoltaic nuclear battery calculated by others.

Formation of various cell-aggregated structures in the core of hydrogel filament using a microfluidic device and its application as an in vitro neuromuscular junction model

Microfluidic systems have been widely used in various in vitro tissue engineering models. In this paper, we present a new microfluidic system for manufacturing cell filaments laden with cell aggregates of various shapes (beads, rosaries, and filaments) in the core region. The structures were obtained using the Rayleigh-Plateau instability of the two-phase flow of the injected bioinks (continuous phase of collagen/poly[ethylene glycol] diacrylate [PEGDA] and a dispersed phase of methacrylated gelatin (GelMA)). To demonstrate the potential of the system at the neuromuscular junction in vitro model, we injected a C2C12-laden collagen/PEGDA bioink into the sheath region and GelMA mixed with motor neuron cells (NSC-34) into the core region of the fabricated cell aggregates. The rosary-shaped NSC-34 aggregate in the core exhibited higher cell viability and more developed neurogenic gene expression than the cell aggregates of beads and filaments in the core region, owing to much greater mechanotransduction. Furthermore, when comparing the cell-laden struts (C2C12 in the sheath region and NSC34 in the rosary core region) with normally bioprinted struts with the same cell density, the expressions of myogenesis-, neuromuscular junction (NMJ)-, and neurogenesis-related genes in struts laden with rosary-shaped cell aggregates were significantly upregulated. The results of these experiments demonstrate the potential for the fabrication of new cell-laden core-sheath structures, which could be used for designing various in vitro models.

Mechanics of cell-cell junctions

Tissue cells in epithelial or endothelial monolayers are connected through cell-cell junctions, which are stabilized by transmembrane E-cadherin bonds and intracellular actin filaments. These bonds and junctions play a crucial role in maintaining the barrier function of epithelia and endothelia and are believed to transmit forces between cells. Additionally, E-cadherin bonds can impact the shape of cell-cell junctions. In this study, we develop a continuum mechanical model of the cell-cell junction by explicitly incorporating the cell membrane, distributions of E-cadherin bonds, cytoplasmic fluid pressure, and F-actin dynamics. The static force-balanced version of the model is able to analyze the influences of cell cortical tension, actin dynamics, and cytoplasmic pressure on the junction shape and E-cadherin bonds. Furthermore, an extended model that incorporates fluid flow, across the cell boundary as well as around the cell, is also examined. This model can couple cell-shape changes with cell cortical tension and fluid flow, and predicts the additional effect of fluid motion on cell-cell junction mechanics. Taken together, our models serve as an intermediate link between molecular-scale models of cell-junction molecules and cell-scale models of tissue and epithelia.

Equivalent imperfect interface model of PN junction of piezoelectric semiconductor for the multi-field coupled waves propagation

Equivalent imperfect interface model of PN junction of piezoelectric semiconductor for the multi-field coupled waves propagation

Design of behavior prediction model of molybdenum disulfide magnetic tunnel junctions using deep networks

Magnetic tunnel junctions (MTJ) are widely used in spintronics development owing to their high scalability and minimal power consumption. However, analyzing the electrical and magnetic behaviors of MTJ in real-time applications is challenging. In this study, an MTJ based on molybdenum disulfide (MoS2) is designed, and a novel deep Elman neural behavior prediction model is developed to analyze its behavior. MoS2 acts as a tunnel barrier in the proposed model, whereas iron oxide (Fe3O4) acts as a ferromagnetic electrode. The interface between Fe3O4 and MoS2 in the MTJ improves the spin polarization and tunnel magnetoresistance ratio. Herein, the performance parameters of the MTJ are used as inputs for the developed prediction model, which analyzes the magnetic and electrical properties of the MTJ using prediction parameters. The spin currents in the parallel and antiparallel configurations are also determined. The designed model is implemented using MATLAB and validated by comparing simulation and experimental results. Moreover, a maximum resistivity of 91 Ω is attained at a temperature of 300 K for the proposed model. At 120 K, under a positive bias, the proposed model achieves a TMR ratio of 0.936. Under negative bias, the maximum TMR ratio attained by the proposed model is 0.817.

An approach for an extension of the deflagration model in containment code system COCOSYS to separate burned and unburned atmosphere via junctions

Abstract In case of a postulated severe accident in a water-cooled nuclear power plant significant amounts of hydrogen (H2) and carbon monoxide (CO) can be generated and released into the containment or reactor building where it might form a combustible mixture with air assuming passive autocatalytic recombiners are not available. In case of ignition, pressure peaks might occur, that are relevant for the integrity of safety relevant equipment and the containment or reactor building. It is therefore important for safety analysis to be able to correctly predict combustion phenomena that might occur. The accident analysis code AC2 2021.0 which is developed by Gesellschaft für Anlagen- und Reaktorsicherheit (GRS) includes the Containment Code System (COCOSYS version 3.1) for the simulation of containment phenomena. COCOSYS contains the model FRONT for the simulation of premixed deflagration of H2 and CO. Recent code validation using H2 deflagration tests conducted in the multi-compartment THAI+ test facility shows that the flame propagation stops prematurely in simulations of some tests. This is partly attributed to the missing separation of burned and unburned atmosphere which leads to a reduction in fuel concentration in not yet burning zones connected to a burning zone. Model improvement potential was identified which is addressed in this paper. A model extension to separate burned and unburned atmosphere via a junction model is proposed and implemented into a development version of COCOSYS 3.1. First validation results using the THAI test HD-39 are discussed that show improved prediction capability by the extended model.

Temperature dependence of avalanche breakdown in 4H-SiC devices

We report new data on the temperature coefficient of the avalanche breakdown voltage (BV) in 4H-SiC devices. We compare avalanche in different device types (MOSFETs/Schottky diodes), different measurement types [Id(Vd)-sweeps and unclamped inductive switching stress], and avalanche directions (vertical and lateral) for a wide range of avalanche voltages. We fit the measured BV(T)-curves to the one-sided abrupt junction model and extract values for the temperature coefficients of impact ionization coefficients in their Chynoweth and Thornber forms for both holes and electrons.

Performance Analysis of Variable Threshold Voltage (ΔVth) Model of Junction less FinTFET

The work presented in this paper is a variable threshold voltage (ΔVth) model of junction less fin gate tunnel FET (JL FinTFET) in which there is a shift in threshold voltage. As a result, to improve drive current and subthreshold slope among other devices. At the same time, gradually decrease the random dopant fluctuations (RDF) effects on Vth, ambipolar leakage current by using this design. The threshold voltage in the junction less fin gate TFET may be modified using 2D numerical simulations by supplying a voltage to the variable gate. The effects of the threshold voltage change on the device's overall performance investigate. A GaSb junction less fin gate TFET and AlGaSb junction less fin gate TFETs with variable threshold voltage characteristics compare. The ON state current is 1.5x10-3 A/m, the SS is 17.1 mV/decade, and the Iamb is 3.314x10-17 A/m.

Sensitivity of a DC SQUID with a non-sinusoidal current-phase relation in its junctions

In ballistic superconductor–normal metal–superconductor Josephson junctions, such as those made from graphene or high mobility semiconductors, the current-phase relation may not have the common, sinusoidal form but can be skewed to have a peak supercurrent at a phase difference greater than π/2. Here, we use a numerical simulation that includes thermal noise to investigate the sensitivity of a DC superconducting quantum interference device (SQUID) with such junctions. The simulation uses a resistively and capacitively shunted junction model where the current-phase relation of each junction can be defined as an arbitrary function. The modulation, transfer function, noise, and sensitivity of a SQUID are calculated for different types of current-phase relation. For the examples considered here, we find that the flux sensitivity of the SQUID is always degraded by forward skewing of the current-phase relation, even in cases where the transfer function of the SQUID has been improved.

Effect of complement C3 inhibition with pegcetacoplan in an iPSC-derived ALS neuromuscular junction model of neuroinflammation (P13-4.004)

Effect of complement C3 inhibition with pegcetacoplan in an iPSC-derived ALS neuromuscular junction model of neuroinflammation (P13-4.004)

Medical image encryption based on RNG with an autonomous piecewise damping Josephson junction jerk oscillator embedded in FPGA

This paper explore the dynamics, Field Programmable Gate Array (FPGA) validation of an autonomous piecewise damping Josephson junction jerk oscillator (APDJJJO) and uses it to protect medical images based on random number generator (RNG). APDJJJO is derived from a piecewise damping Josephson junction (JJ) model and has either no equilibrium points or two unstable equilibrium points. One-scroll chaotic hidden attractor, one-scroll sovereign complex attractor, periodic and one-scroll complex self-driven attractors coexisting, and bistable limit cycles are found in APDJJJO during the investigation based on numerical simulations. Additionally, the FPGA validation of the APDJJJO shows similar complex characteristics to those obtained during the investigation via numerical simulations. Lastly, the chaotic characteristics depicted by the APDJJJO are used to design a RNG for the encryption of medical images. The generated random bits are validated successfully by standard statistical tool set by the National Institute of Standards and Technology (NIST-800-22). Encryption algorithm is developed to secure a medical image by exploring the unpredicted bits generated. The safety and performances analysis are done to prove the robustness and efficiency of the image encryption algorithm.