Circuit Elements Research Articles

Rapid time-domain simulation of fractional capacitors with SPICE

Fractional capacitors, commonly called constant-phase elements or CPEs, are used in modeling and control applications, for example, for rechargeable batteries. Unfortunately, they are not natively supported in the well-used circuit simulator SPICE. This manuscript presents and demonstrates a modeling approach that allows users to incorporate these elements in circuits and model the response in the time domain. The novelty is that we implement for the first time a particular configuration of RC elements in parallel in a Foster-type network with SPICE in order to simulate a constant-phase element across a defined frequency range. We demonstrate that the circuit produces the required impedance spectrum in the frequency domain, and shows a power-law voltage response to a step change in current in the time domain, consistent with theory, and is able to reproduce the experimental voltage response to a complicated current profile in the time domain. The error depends on the chosen frequency limits and the number of RC branches, in addition to very small SPICE numerical errors. We are able to define an optimum circuit description that minimizes error while maintaining a short computation time. The scientific value is that the work permits rapid and accurate evaluation of the response of CPEs in the time domain, faster than other methods, using open source tools.

NbN-based half-flux-quantum element for integration with superconducting qubits

Half-flux-quantum (HFQ) logic is a superconductor logic family comprising conventional Josephson junctions (0-JJs) and π Josephson junctions (π-JJs). The energy scale of HFQ logic can be flexibly reduced by phase shifts owing to π-JJs; thus, HFQ circuits are a promising building block for qubit interface circuits. In this study, we demonstrate an NbN-based HFQ circuit element compatible with NbN-based superconducting qubits for all-NbN monolithic integration of qubits and HFQ circuits. The use of NbN is beneficial for both qubits and HFQ circuits owing to its high-quality junctions and high kinetic inductance. First, we developed a prototype of an NbN-based 0-JJ/π-JJ hybrid fabrication process for designing HFQ circuits. We evaluated 0-JJs and π-JJs fabricated through the hybrid process, which demonstrated that the 0-JJs had a small deviation with regard to critical current density and the π-JJs had a sufficiently high critical current density to work as π phase shifters. Furthermore, we fabricated an HFQ superconducting quantum interference device, which is one of the most fundamental elements in HFQ circuits, and observed clear HFQ-period modulation in the magnetic flux dependence of the maximum current at 4.2 K.

Modular horizontal network within mouse primary visual cortex.

Interactions between feedback connections from higher cortical areas and local horizontal connections within primary visual cortex (V1) were shown to play a role in contextual processing in different behavioral states. Layer 1 (L1) is an important part of the underlying network. This cell-sparse layer is a target of feedback and local inputs, and nexus for contacts onto apical dendrites of projection neurons in the layers below. Importantly, L1 is a site for coupling inputs from the outside world with internal information. To determine whether all of these circuit elements overlap in L1, we labeled the horizontal network within mouse V1 with anterograde and retrograde viral tracers. We found two types of local horizontal connections: short ones that were tangentially limited to the representation of the point image, and long ones which reached beyond the receptive field center, deep into its surround. The long connections were patchy and terminated preferentially in M2 muscarinic acetylcholine receptor-negative (M2-) interpatches. Anterogradely labeled inputs overlapped in M2-interpatches with apical dendrites of retrogradely labeled L2/3 and L5 cells, forming module-selective loops between topographically distant locations. Previous work showed that L1 of M2-interpatches receive inputs from the lateral posterior thalamic nucleus (LP) and from a feedback network from areas of the medial dorsal stream, including the secondary motor cortex. Together, these findings suggest that interactions in M2-interpatches play a role in processing visual inputs produced by object-and self-motion.

Fast joint parity measurement via collective interactions induced by stimulated emission

Parity detection is essential in quantum error correction. Error syndromes coded in parity are detected routinely by sequential CNOT gates. Here, different from the standard CNOT-gate based scheme, we propose a reliable joint parity measurement (JPM) scheme inspired by stimulated emission. By controlling the collective behavior between data qubits and syndrome qubit, we realize the parity detection and experimentally implement the weight-2 and weight-4 JPM scheme in a tunable coupling superconducting circuit, which shows comparable performance to the CNOT scheme. Moreover, with the aid of the coupling tunability in quantum system, this scheme can be further utilized for specific joint entangling state preparation (JEP) with high fidelity, such as multiqubit entangled state preparation for non-adjacent qubits. This strategy, combined with the superconducting qubit system with tunable couplers, reveals tremendous potential and applications in the surface code architecture without adding extra circuit elements. Besides, the method we develop here can readily be applied in large-scale quantum computation and quantum simulation.

Hybrid cooling system with phase change material and liquid microchannels to prevent thermal runaway propagation within lithium-ion battery packs

Thermal runaway propagation in battery pack can cause catastrophic hazards without timely countermeasures. This paper presents the establishment of lumped thermal resistance model for battery pack and introduces an innovative hybrid thermal management system designed to suppress thermal runaway propagation. First, a heat generation model describing the reaction dynamics of cell thermal runaway is developed and validated by experiment. Subsequently, a lumped thermal resistance network is formulated for the large-scale battery pack using electrical circuit elements and simulation studies for the thermal runaway propagation are carried out. The results reveal that the thermal runaway propagation progress triggered by four cells can accelerate by nearly 40 % compared to that initiated by a single cell. Consequently, a hybrid thermal management incorporating phase change material and liquid microchannels is designed. Comparative analysis with individual cooling systems demonstrates the superior effectiveness of the proposed system in suppressing the first round of thermal runaway propagation. The temperatures of neighbor cells only have a marginal climb to about 80 ℃ in the initial 600 s, which is well below the thermal runaway triggering temperature. This work can provide valuable insights into the structural design of thermal management systems for battery packs in practical applications.

Reconfigurable Resistive Switching in VO2/La0.7Sr0.3MnO3/Al2O3 (0001) Memristive Devices for Neuromorphic Computing.

The coexistence of nonvolatile and volatile switching modes in a single memristive device provides flexibility to emulate both neuronal and synaptic functions in the brain. Furthermore, such a device structure may eliminate the need for additional circuit elements such as transistor-based selectors, enabling low-power consumption and high-density device integration in fully memristive spiking neural networks. In this work, we report dual resistive switching (RS) modes in VO2/La0.7Sr0.3MnO3 (LSMO) bilayer memristive devices. Specifically, the nonvolatile RS is driven by the movement of oxygen vacancies (Vo) at the VO2/LSMO interface and requires a higher biasing voltage, whereas the volatile RS is controlled by the metal-insulator transition (MIT) of VO2 under a lower biasing voltage. The simple device structure is electrically driven between the two RS modes and thus can operate as a one selector-one resistor (1S1R) cell, which is a desirable feature in memristive crossbar arrays to avoid the sneak-path current issue. The RS modes are found to be stable and repeatable and can be reconfigured by exploiting the interfacial and phase transition properties, and thus, they hold great promise for applications in memristive neural networks and neuromorphic computing.

Low-Cost Small Scale Autonomous Vehicle

A low-cost small-scale autonomous vehicle refers to a self-driving vehicle that is affordable and designed for smaller applications or purposes. The firefly algorithm was used to solve obstacle-avoiding problems due to dynamic or static uncertain positioned obstacles. The autonomous vehicle reached the determined position, which could be considered accurate. Regardless of the starting point, it arrived at the defined destination in an area with a diameter of 5 meters. Considering the costs of the sensors, simple algorithm, not very powerful processor and circuit elements, sufficient success has been achieved.

EXPERIMENTAL ANALYSIS OF POWER SEMICONDUCTOR ELEMENTS USED IN FLYBACK CONVERTERS

Switch-mode power supplies (SMPS) have become very popular lately due to their superior efficiency and compact dimensions as compared to conventional counterparts which contain low-frequency transformers. This efficiency and miniaturization are connected to several factors, including high operating frequencies typically in the kHz to MHz range, the utilization of ferromagnetic materials for inductive coupling, and the incorporation of active switching components to mitigate energy loss via the Joule-Lenz effect. Enhancing SMPS efficiency implies active circuit elements (diodes, transistors, etc.) sizing and arrangements. This paper presents a practical approach by addressing a series of experiments to analyze and compare the performance of different active components. It starts with the analysis of the PWM generator module from the primary and continues with the field-effect transistors and rectifier diodes in the secondary. The asynchronous flyback topology has been chosen to carry out the experiments. By using the same experimental platform, based on the obtained results, the analysis of behavioral differences between the different components has been performed.

Climbing fibers provide essential instructive signals for associative learning

Supervised learning depends on instructive signals that shape the output of neural circuits to support learned changes in behavior. Climbing fiber (CF) inputs to the cerebellar cortex represent one of the strongest candidates in the vertebrate brain for conveying neural instructive signals. However, recent studies have shown that Purkinje cell stimulation can also drive cerebellar learning and the relative importance of these two neuron types in providing instructive signals for cerebellum-dependent behaviors remains unresolved. In the present study we used cell-type-specific perturbations of various cerebellar circuit elements to systematically evaluate their contributions to delay eyeblink conditioning in mice. Our findings reveal that, although optogenetic stimulation of either CFs or Purkinje cells can drive learning under some conditions, even subtle reductions in CF signaling completely block learning to natural stimuli. We conclude that CFs and corresponding Purkinje cell complex spike events provide essential instructive signals for associative cerebellar learning.

Ferromagnetic materials for Josephsonπjunctions

The past two decades have seen an explosion of work on Josephson junctions containing ferromagnetic materials. Such junctions are under consideration for applications in digital superconducting logic and memory. In the presence of the exchange field, spin–singlet Cooper pairs from conventional superconductors undergo rapid phase oscillations as they propagate through a ferromagnetic material. As a result, the ground-state phase difference across a ferromagnetic Josephson junction oscillates between 0 and π as a function of the thickness of the ferromagnetic material. π-junctions have been proposed as circuit elements in superconducting digital logic and in certain qubit designs for quantum computing. If a junction contains two or more ferromagnetic layers whose relative magnetization directions can be controlled by a small applied magnetic field, then the junction can serve as the foundation for a memory cell. Success in all of those applications requires careful choices of ferromagnetic materials. Often, materials that optimize magnetic properties do not optimize supercurrent propagation, and vice versa. In this review, we discuss the significant progress that has been made in identifying and testing a wide range of ferromagnetic materials in Josephson junctions over the past two decades. The review concentrates on ferromagnetic metals, partly because eventual industrial applications of ferromagnetic Josephson junctions will most likely start with metallic ferromagnets (either in all metal junctions or junctions containing an insulating layer). We will briefly mention work on non-metallic barriers, including ferromagnetic insulators, and some of the exciting work on spin–triplet supercurrent in junctions containing non-collinear magnetic inhomogeneity.

Equivalent circuit for assessing pore size distribution in porous electrodes by EIS

The size, geometry and arrangement of pores can significantly affect both the activity of electrocatalysts and the performance of supercapacitors. Electrochemical impedance spectroscopy (EIS) is a powerful, non-invasive diagnostic tool for characterizing porous conductive media. To date, the impedance of porous electrodes characterized by the distribution of pore sizes has been described only on the basis of numerical models. Here, for the first time, the impedance of such systems is provided in the form of analytical solutions valid in high and low frequency range. The model enables traditional equivalent circuit fitting to impedance of electrodes characterized by log–normal distribution of pore sizes. The analytical expressions for the circuit elements give access to the electrode idealized geometry in terms of log-normal distribution. The model was applied for the analysis of the electrochemical impedance of activated carbon electrode in a sulphuric acid solution. The width of pore size distribution, mean pore radius, pore length and the number of pores can be determined from a single impedance spectrum.

478 Single-Neuron and Columnar Computations in the Human Prefrontal Cortex Underlying Transformations From Conscious Visual Perception to Speech Production

INTRODUCTION: The basic functional unit of the mammalian neocortex is the cortical column. It consists of a vast diversity of circuit elements and interconnected layers supporting a range of computations for cognitive functions. While much progress has been made in our understanding of its functional architecture in animal models, its role in complex, uniquely human cognitive processes remain unknown. METHODS: We utilized a new columnar recording approach in humans participating of cognitive tasks, while undergoing awake neurosurgical procedures. The novelty of this approach hinged on utilizing state-of-the-art Neuropixels probes for our recordings, a silicon-based electrophysiology recording electrodes with high channel count and recording site density. This technology allows us to measure neural activity with single cell resolution throughout the neocortical column and its layers, and at a scale that is far beyond the capabilities of current clinically-approved devices. The participants performed a naturalistic visual priming-based sentence production task that provided them with pictorial representations of events that had to be articulated in specific form and order. RESULTS: We observed distinct spatiotemporal activity dynamics throughout the cortical column in the language-dominant prefrontal cortex that encoded a preparatory mode for speech generation, as well as visual perception and sentence construction processing stages during natural speech. Furthermore, we observed the emergence of this columnar activity pattern with optimal visual inspection of presented images (by high resolution eye and pupil tracking) and associated improved performance during the task. CONCLUSIONS: These results highlight a functional organization of the cortical column in the human prefrontal cortex subserving sensory-motor transformations for visual conscious perception and speech production, with profound implications for future brain machine interfaces that aim to restore complex human cogntive functions.

Energy storage application of plasticized biopolymer blend electrolyte doped with ammonium nitrate as H+ provider

Recently, proton-conducting electrolytes for energy storage purposes attracted the focus of many research groups as possible alternatives for Li-based energy storage applications. Natural polymers offer a promising solution to counter the environmental impact of synthetic polymers. In this work proton conducting CS:POZ based natural polymer electrolytes were plasticized with glycerol (GL) to improve the ion conductivity and increase the free ion percentage. The films of CS:POZ:NH4NO3 electrolyte were prepared using the casting technique. The plasticizers were changed from 9 to 36 wt%. The FTIR method revealed ion interaction among polymer, salt, and plasticizer. The deconvolution of FTIR band associated with NO3 anion was used to calculate the ion transport parameters (ITPs). It was found that the free ion contour area was enhanced by increasing plasticizer concentration. The EIS plot fitted with equivalent circuit elements was used to determine the DC conductivity and ITPs. The dominancy of ion species was examined using TNM measurement. The safety region for energy storage device (ESD) application was found from the LSV examination. The activated carbon was used to manufacture the electrodes for ESD application. The cyclic voltammetry (CV) investigation found that the capacitances of the ESD influenced by scan rates.

On the analytical modeling of fractal memelement and inverse memelement

SummaryIn this work, an improved analytical model of fractal memelement in which the pinched point shifting has been considered and the original analytical model of fractal inverse memelement have been proposed. These fractal circuit elements are the memelement, and inverse memelement operates based on the principle of electromagnetic in fractal time/space, which must be applied whenever the current flows through fractal media. These models are important because these memory elements can be realized based on the porous material, which is a fractal media. In addition, they can employ self‐similarity, which is hard to be simulated by using the traditional models. This is because such self‐similarity can be well explained by the fractal set‐based model, yet those traditional models are based on the set of real values. Therefore, for deriving the proposed models, the fractal calculus, which is oriented to the fractal set, has been adopted as the mathematical basis. From the analytical and numerical analyses based on the derived models, it has been found that both memelement and inverse memelement can retain their unique frequency characteristics despite being operated based on the abovementioned principle. In addition, their input–output relationships are mathematically differentiable albeit the inputs and outputs themselves are not.

On Mechanical and Chaotic Problem Modeling and Numerical Simulation Using Electric Networks

After reviewing the use of electrical circuit elements to model dynamic processes or the operation of devices or equipment, both in real laboratory implementations and through ideal circuits implemented in simulation software, a network model design protocol is proposed. This approach, following the basic rules of circuit theory, makes use of controlled generators to implement any type of nonlinearity contained in the governing equations. Such a protocol constitutes an interesting educational tool that makes it possible for nonexpert students in mathematics to design and numerically simulate complex physical processes. Three applications to mechanical and chaotic problems are presented to illustrate the versatility of the proposed protocol.

Basic Approaches for Reducing Power Consumption in Finite State Machine Circuits—A Review

Methods for reducing power consumption in circuits of finite state machines (FSMs) are discussed in this review. The review outlines the main approaches to solving this problem that have been developed over the last 40 years. The main sources of power dissipation in CMOS circuits are shown; the static and dynamic components of this phenomenon are analyzed. The power consumption saving can be achieved by using coarse-grained methods common to all digital systems. These methods are based on voltage or/and clock frequency scaling. The review shows the main structural diagrams generated by the use of these methods when optimizing the power characteristics of FSM circuits. Also, there are various known fine-grained methods taking into account the specifics of both FSMs and logic elements used. Three groups of the fine-grained methods targeting FPGA-based FSM circuits are analyzed. These groups include clock gating, state assignment, and replacing look-up table (LUT) elements by embedded memory blocks (EMBs). The clock gating involves a separate or joint use of such approaches as the (1) decomposition of FSM inputs and (2) disabling FSM inputs. The aim of the power-saving state assignment is to reduce the switching activity of a resulting FSM circuit. The replacement of LUTs by EMBs allows a reduction in the power consumption due to a decrease in the number of FSM circuit elements and their interconnections. We hope that the review will help experts to use known methods and develop new ones for reducing power consumption. We think that a good knowledge and understanding of existing methods of reducing power consumption is a prerequisite for the development of new, more effective methods to solve this very important problem. Although the methods considered are mainly aimed at FPGA-based FSMs, they can be modified, if necessary, and used for the power consumption optimization of FSM circuits implemented with other logic elements.

Assessing Nonlinear Polarization in Electrochemical Cells using AC Impedance Spectroscopy

AC impedance spectroscopy is widely used to evaluate performance limitations in energy storage and conversion devices (e.g., batteries, supercapacitors, and fuel cells). This work shows that integrating the resistive elements in an equivalent circuit as functions of steady-state current enables one to recover overpotentials associated with different processes (e.g., ion migration, charge transfer, and diffusion) in nonlinear electrochemical power supplies. Closed form expressions for diffusion overpotentials are derived using this method for transmissive and reflective boundary conditions and three electrode symmetries (planar, cylindrical, and spherical). Discussion is also extended to macroscopically homogenous porous electrodes which are relevant for most real-world devices. Overall, the approach described herein is a powerful tool to identify rate-limiting steps and guide material/component design.

Anti-ambipolar transport and logic operation in two-dimensional field-effect transistors using in-series integration of GeAs and SnS2

Germanium arsenide (GeAs) from the IV–V semiconductor family has drawn attention for its anisotropic optical and electrical characteristics as a robust p-type semiconductor with high mobility. Despite its potential, the scope of applications for two-dimensional (2D) GeAs remains limited. In this study, we investigate the in-series integration of GeAs and SnS2 for complementary inverter operation, a crucial element in logic circuits. Moreover, the resulting in-series field-effect transistors reveal a previously undocumented anti-ambipolar behavior in this configuration, with a peak-to-valley ratio exceeding 5000 at a drain-source bias of 2 V. This anti-ambipolar characteristic operates regardless of the direction of current flow, which is rarely seen in conventional anti-ambipolar transistors built with hetero-stacked p–n junctions. Moreover, the in-series structure demonstrates stable operation as a complementary inverter, highlighting the versatile potential of p-type 2D GeAs in diverse optoelectronic applications. This work contributes insights into the transport characteristics of GeAs and its interconnected arrangement with SnS2, offering valuable implications for the development of advanced 2D material-based devices.

Generation of an enhancer-driven gene expression viral tool specific to dentate granule cell-types through direct hippocampal injection.

Accurate investigations of neural circuitry require specific genetic access to individual circuit elements, i.e., the myriad neuronal cell-types in the brain. However, native promoters cannot achieve this because while most genes are expressed in the brain, few are expressed in a single neuronal cell-type. We recently used enhancers, the subcomponents of the transcriptional apparatus which tell promoters when and where to express, combined with heterologous minimal promoters to increase specificity of transgene expression, an approach we call Enhancer-Driven Gene Expression (EDGE). As we discuss, EDGE is a marked improvement in specificity over native promoters, but still requires careful anatomical analysis to avoid off-target effects. In this study we present a more complete set of genomic markers from the mouse brain and characterize a novel EDGE viral vector capable of specifically driving expression in distinct subtypes of hippocampal neurons, even though it can express in other cell-types elsewhere. The advent of cell-type specific viral tools in wild-type animals provides a powerful strategy for neural circuit investigation and holds promise for studies using animal models for which transgenic tools are not available.

Quantum error mitigation via quantum-noise-effect circuit groups

Near-term quantum computers have been built as intermediate-scale quantum devices and are fragile against quantum noise effects, namely, NISQ devices. Traditional quantum-error-correcting codes are not implemented on such devices and to perform quantum computation in good accuracy with these machines we need to develop alternative approaches for mitigating quantum computational errors. In this work, we propose quantum error mitigation (QEM) scheme for quantum computational errors which occur due to couplings with environments during gate operations, i.e., decoherence. To establish our QEM scheme, first we estimate the quantum noise effects on single-qubit states and represent them as groups of quantum circuits, namely, quantum-noise-effect circuit groups. Then our QEM scheme is conducted by subtracting expectation values generated by the quantum-noise-effect circuit groups from those obtained by the quantum circuits for the quantum algorithms under consideration. As a result, the quantum noise effects are reduced, and we obtain approximately the ideal expectation values via the quantum-noise-effect circuit groups and the numbers of elementary quantum circuits composing them scale polynomial with respect to the products of the depths of quantum algorithms and the numbers of register bits. To numerically demonstrate the validity of our QEM scheme, we run noisy quantum simulations of qubits under amplitude damping effects for four types of quantum algorithms. Furthermore, we implement our QEM scheme on IBM Q Experience processors and examine its efficacy. Consequently, the validity of our scheme is verified via both the quantum simulations and the quantum computations on the real quantum devices. Our QEM scheme is solely composed of quantum-computational operations (quantum gates and measurements), and thus, it can be conducted by any type of quantum device. In addition, it can be applied to error mitigation for many other types of quantum noise effects as well as noisy quantum computing of long-depth quantum algorithms.