Nonlinear Electronic Devices Research Articles

Fabricating 3D Si nanostructure via a novel Ga+ implantation enabled maskless dry etching process

Three-dimensional (3D) nanostructure is the key to the miniaturization of nonlinear photonic and electronic devices. Because of the great demand on the ultra-small nanostructures, the novel nanofabrication technologies with flexible 3D silicon (Si) fabrication capability are of great importance. Herein, by combining Ga+ FIB implantation and ICP dry etching processes, we proposed a novel Ga+ FIB implantation assisted maskless dry etching technology for direct fabrication of 3D Si nanostructures. We found that the implanted Ga+ induced amorphization layer in Si substrate acts as the ‘quasi-mask’ during the ICP dry etching process. The increase of Ga+ concentration in amorphization layer of Si substrate improves the etching resistance of the area. Moreover, enabled by high resolution and flexibility of FIB, the proposed technology is capable of directly fabricating various 3D Si nanostructures in a simple way, such as 3D nanoscale artificial bowtie arrays with sub-10 nm gaps, multi-scale 3D Si nanostructures with arbitrary patterns. More importantly, the proposed technology is compatible to current semiconductor manufacturing process. Benefiting by the advantages of simplicity and high efficiency, the proposed maskless dry etching process promises great application potential in the fields of nonlinear photonics and micro-electronics.

A comparative study of different deep learning methods for time-series probabilistic residential load power forecasting

The widespread adoption of nonlinear power electronic devices in residential settings has significantly increased the stochasticity and uncertainty of power systems. The original load power data, characterized by numerous irregular, random, and probabilistic components, adversely impacts the predictive performance of deep learning techniques, particularly neural networks. To address this challenge, this paper proposes a time-series probabilistic load power prediction technique based on the mature neural network point prediction technique, i.e., decomposing the load power data into deterministic and stochastic components. The deterministic component is predicted using deep learning neural network technology, the stochastic component is fitted with Gaussian mixture distribution model and the parameters are fitted using great expectation algorithm, after which the stochastic component prediction data is obtained using the stochastic component generation method. Using a mature neural network point prediction technique, the study evaluates six different deep learning methods to forecast residential load power. By comparing the prediction errors of these methods, the optimal model is identified, leading to a substantial improvement in prediction accuracy.

Nonlinear electronic devices on single-layer CVD graphene for thermistors

In this article, we present simple, cost-effective, passive (non-gated) electronic devices based on single-layer (SL) chemical vapor deposited (CVD) graphene that show nonlinear and asymmetric current-voltage characteristics (CVCs) at ambient temperatures. Al2O3-Ti-Au contacts to graphene results in a nonlinear resistance to achieve nonlinearity in the CVC. Upon transfer to polyethylene terephthalate, the CVD-grown SL graphene shows mobility of 6200 cm2V-1S-1. We have observed both thermoelectric effect and thermoresistive sensing in the fabricated devices such as voltage and temperature concerning change in electronic power and resistance through asymmetric and nonlinear CVC. The device is stable both at low and high voltages (±200 mV to ±4 V) and temperatures (4 K - 300 K). Graphene-based thermosensing devices can be ultra-thin, cost-effective, non-toxic/organic, flexible, and high-speed for integration into future complementary metal-oxide semiconductor (CMOS) interface, and wearable self-power electronics. A strong negative temeperature coefficent of resistance is demonstrated in the realized nonlinear graphene-integrated resistors for its application in NTC thermistors.

Modeling defect-level switching for nonlinear and hysteretic electronic devices

Previously, we demonstrated hysteretic and persistent changes of resistivity in two-terminal electronic devices based on charge trapping and detrapping at immobile metastable defects [Yin et al., Phys. Rev. Appl. 15, 014014 (2021)]; we termed these defect-level switching (DLS) devices. DLS devices feature all-electronic resistive switching and thus are volatile because of the “voltage-time” dilemma. However, the dynamics of volatile resistive switches may be valuable for emerging applications such as selectors in crosspoint memory and neuromorphic computing concepts. To design circuits using these volatile resistive switches, accurate modeling is essential. In this work, we develop an accurate and analytical model to describe the switching in DLS devices, based on the established theories of point defect metastability in Cu(In,Ga)Se2 (CIGS) and II–VI semiconductors. The analytical nature of our model allows for time-efficient simulations of dynamical behavior of DLS devices. We model the time durations of SET and RESET programming pulses, which can be exponentially shortened with respect to the pulse amplitude. We also demonstrate the concept of inverse design: given desired resistance states, the width and amplitude of the programming signal can be chosen accordingly.

Coupled Memristor Oscillators for Neuromorphic Locomotion Control: Modeling and Analysis.

The recent surge of interest in brain-inspired architectures along with the development of nonlinear dynamical electronic devices and circuits has enabled energy-efficient hardware realizations of several important neurobiological systems and features. Central pattern generator (CPG) is one such neural system underlying the control of various rhythmic motor behaviors in animals. A CPG can produce spontaneous coordinated rhythmic output signals without any feedback mechanism, ideally realizable by a system of coupled oscillators. Bio-inspired robotics aims to use this approach to control the limb movement for synchronized locomotion. Hence, devising a compact and energy-efficient hardware platform to implement neuromorphic CPGs would be of great benefit for bio-inspired robotics. In this work, we demonstrate that four capacitively coupled vanadium dioxide (VO2) memristor-based oscillators can produce spatiotemporal patterns corresponding to the primary quadruped gaits. The phase relationships underlying the gait patterns are governed by four tunable bias voltages (or four coupling strengths) making the network programmable, reducing the complex problem of gait selection and dynamic interleg coordination to the choice of four control parameters. To this end, we first introduce a dynamical model for the VO2 memristive nanodevice, then perform analytical and bifurcation analysis of a single oscillator, and finally demonstrate the dynamics of coupled oscillators through extensive numerical simulations. We also show that adopting the presented model for a VO2 memristor reveals a striking resemblance between VO2 memristor oscillators and conductance-based biological neuron models such as the Morris-Lecar (ML) model. This can inspire and guide further research on implementation of neuromorphic memristor circuits that emulate neurobiological phenomena.

A novel PQ improvement in multi-parallel feeder distribution system using multi-convertible UPQC device

In the current situation, the use of non-linear power electronic devices in automated industries, arc-furnaces, and adjustable speed drives. The nature of large-sized non-linear loads causes harmonic pollution, voltage interruptions, and voltage sag/swell issues, which are the main issues in modern multi-parallel feeder distribution network. Several mitigation techniques among the above, the multi-convertible universal power-quality conditioner has reliable performance, robust operation and also mitigation of both current-voltage allied power quality (PQ) issues accordingly. The multi-convertible universal power-quality conditioner (Mcon-UPQC) device mitigates power-quality issues by extracting deviated supply voltage and distorted load current by using feasible control algorithm. But, the computational delay, complex mathematical transformation and response delay are the major problem identified in conventional control algorithms. In this work, a novel generalized vector reference control algorithm has been proposed for extraction of fundamental reference voltage-current signals. The performance and effectiveness of proposed control algorithm of Mcon-UPQC device is validated by using MATLAB-Simulink computing tool, simulated outcomes are illustrated with valid comparisons.

Broad Learning System Using Rectified Adaptive Moment Estimation for Harmonic Detection and Analysis

The extensive application of nonlinear components and power electronic devices in power systems has led to increasingly deteriorating harmonic pollution. The rapid and high-precision harmonic detection and analysis technology will set the prerequisite for mitigating the harmonic crisis. This paper presents a robust, stable and real-time approach based on broad learning system (BLS) for detecting and analyzing the amplitudes of fundamental and harmonic components, which only requires half or even quarter cycle input. Additionally, a fast-converging adaptive parameter learning strategy using rectified adaptive moment estimation (RAdam) for BLS is proposed. To obtain the actual distorted signals, the signal acquisition system is built on the basis of the designed multi-pulse rectifier power supply (6/18 pulse). With the fundamental frequency deviation, interharmonics and noise, etc., the simulated signals synthesized by MATLAB tool and the actual signals acquired from the built system are attained to verify the performance of methods. The proposed approach differentiates from other existing methods not only due to its superior precision and robustness in the presence of multitudinous interference factors but also its real-time performance because of low average computing time.

Comparison of the effectiveness of stabilizing quasi-resonant and inertial influences on the parameters of a controlled Lorentz system with dynamic chaos

Problem statement. The article is devoted to determining the comparative energy efficiency of stabilizing quasi-resonant and inertial effects on the parameters of a controlled discrete-nonlinear Lorentz system with dynamic chaos. Currently, information transmission systems based on the use of chaotic dynamics effects are developing extremely intensively. The need to ensure reproducibility of parameters and statistical characteristics of pseudorandom signals generated on the basis of nonlinear systems with dynamic chaos makes the task of stabilizing chaotic modes of nonlinear devices and systems urgent. Goal. Determination of the comparative energy efficiency of stabilizing quasi-resonant and inertial effects on the parameters of a controlled discrete-nonlinear Lorentz system with dynamic chaos. Results. It is shown that fluctuations causing deviations of the discrete-nonlinear Lorentz system from equilibrium states lead to changes in the quasi-resonant frequency and the effectiveness of inertial stabilizing effects. It is established that the effectiveness of stabilizing inertial effects on parameter b of a controlled discrete-nonlinear Lorentz system increases with an increase in the fluctuation deviation of the system from equilibrium states. The conditions under which stabilizing inertial effects become energetically more advantageous than quasi-stationary effects on the parameters of a discrete-nonlinear system are determined. Practical significance. Ensuring the required regular behavior with the transfer of nonlinear electronic and quantum devices and systems from stochastic to regular mode.

A generalized admittance criterion for dominant harmonic source determination without synchronous phasor measurements

The widespread integration of various nonlinear power electronic devices in modern power systems has made harmonic distortion more serious than ever before. Given the urgent need to improve power quality, the harmonic contribution evaluation (HCE) is of great importance since it can provide a strong basis for harmonic mitigation measures. However, the conventional HCE methods are not effective due to the lack of synchronous voltage and current phasor information in real-field practice. To overcome the phasor limitation, an HCE method based on a generalized admittance criterion (GAC) is proposed in this paper. The proposed GAC aims to determine the dominant harmonic source at the point of common coupling (PCC) from the utility side and customer side. Compared to the conventional method, the developed method only needs the RMS values of the PCC voltage, current, and phase angle difference. Therefore, the synchronous phasor information of voltage and current is no longer required. Meanwhile, a threshold to determine the dominant harmonic source is established by using the equivalent admittance ratio on both sides of PCC. The evaluation process of the proposed method is significantly simplified by comparing the generalized admittance limit with the proposed threshold, which also greatly improves the method evaluation accuracy. Moreover, the interference of background harmonic fluctuation is excluded by using the linear calibration-based harmonic admittance estimation method. Finally, multiple scenarios with various admittance characteristics were simulated to verify the performance of the proposed criterion, and the real-field measurement is also used to demonstrate the accuracy and validity of the proposed method.

Design of Total Harmonic Distortion Reduction Using Quantum Coyote Optimization Algorithm for Hybrid Power Generation Systems

The usage of various inverters in various industries has gained considerable attention in the power electronics industry in recent years. The harmonic distortion that is induced by various renewable energy sources, along with the growing use of nonlinear loads and power electronic devices, has a significant impact on the distribution system. This is because of the rise in penetration of various renewable energy sources and the usage of nonlinear loads and power electronic devices. In addition, distributed generation (DG) units with inverters (such as solar (PV) and wind turbines) that are integrated into distribution networks are viewed as significant harmonic producers that have substantial adverse effects on power quality. The power quality in the system suffers because of these generators. This study proposes a method of harmonic mitigation for addressing power quality problems that are present in distribution systems as a solution to the challenges that have been found. These concerns have been brought to light as a result of previous research. The standard two-level and three-level inverters were the basis for the development of the multilevel inverter (MLI), which was meant to address their shortcomings. One of the effective technologies that can maintain constant performance is the hybrid power generating system. A hybrid energy system has several benefits, including high dependability, cheap cost and minimal emissions. When used in a hybrid power production system, the reduction of total harmonic distortion (THD) becomes absolutely necessary (HPGS). In this context, the research provides a novel approach for high-performance quantum computing called quantum coyote optimization algorithm-based THD reduction (QCOA-THDR). The QCOA-THDR approach that has been presented has been tested with several converters, including SEPIC, buck–boost and Cuk converters. Additionally, the proportional as well as the integral gain variables of the proportional integral (PI) controller are set in order to achieve decreased total harmonic distortion (THD). In addition, the QCOA system is formed by combining the ideas of quantum computing (QC) with the traditional COA. This is how the QCOA system comes to exist. A limited number of simulations were run, and the results are being analyzed from a variety of perspectives in order to investigate the improved effects that the QCOA-THDR approach has on data. The QCOA-THDR method was shown to have superior results than the more modern techniques, as shown by the comparison research.

Resistance transient dynamics in switchable perovskite memristors

Memristor devices have been investigated for their properties of resistive modulation that can be used in data storage and brain-like computation elements as artificial synapses and neurons. Memristors are characterized by an onset of high current values under applied voltage that produces a transition to a low resistance state or successively to different stable states of increasing conductivity that implement synaptic weights. Here, we develop a nonlinear model to explain the variation with time of the voltage and the resistance and compare it to experimental results on ionic–electronic halide perovskite memristors. We find separate experimental signatures of the capacitive discharge and inductive current increase. We show that the capacitor produces an increase step of the resistance due to the influence of the series resistance. In contrast, the inductor feature associated with inverted hysteresis causes a decrease of the resistance, as observed experimentally. The chemical inductor feature dominates the potentiation effect in which the conductivity increases with the voltage stimulus. Our results enable a quantitative characterization of highly nonlinear electronic devices using a combination of techniques such as time transient decays and impedance spectroscopy.

Noise–dissipation relation for nonlinear electronic circuits

An extension of fluctuation–dissipation theorem is used to derive a “speed limit” theorem for nonlinear electronic devices. This speed limit provides a lower bound on the dissipation that is incurred when transferring a given amount of electric charge in a certain amount of time with a certain noise level (average variance of the current). This bound, which implies a high energy dissipation for fast, low-noise operations (such as switching a bit in a digital memory), brings together recent results of stochastic thermodynamics into a form that is usable for practical nonlinear electronic circuits, as we illustrate on a switching circuit made of an nMOS pass gate in a state-of-the-art industrial technology.

A SVD-Based Dynamic Harmonic Phasor Estimator With Improved Suppression of Out-of-Band Interference

The diffusion of nonlinear loads and power electronic devices in power systems deteriorates the signal environment, and increases the difficulty of measuring harmonic phasors. Considering accurate harmonic phasor information is necessary to deal with harmonic related issues, this paper focuses on realizing accurate dynamic harmonic phasor estimation when the signal is contaminated by certain interharmonic tones, i.e. the out-of-band interference (OBI). Specifically, this work introduces the singular value decomposition into the Taylor-Fourier transform algorithm, deriving a general decomposition form for harmonic phasor filters, and yielding a set of adjustable parameters. Then these parameters are configured to minimize the negative influence of OBI on the dynamic harmonic phasor filters. Based on the recommended parameter values, the optimized harmonic estimator is obtained, and then tested under signals distorted by OBI, harmonic, noise, and in worse cases, frequency deviation, and some dynamic conditions. Test results show the proposal achieves good harmonic phasor estimation even under multiple interferences and dynamic scenarios, and has a much weaker dependence on OBI tones compared to conventional approaches.

Detection and Classification of Power Quality Disturbances Using Stock Well Transform and Improved Grey Wolf Optimization-Based Kernel Extreme Learning Machine

Power quality disturbances (PQDs) in modern electrical power systems, caused by the integration of nonlinear power electronic devices and erratic distributed generation (DG), lead to interruptions and significant energy losses for end users. However, conventional methods for PQDs classification face challenges in dealing with noise interference and feature selection. To address these challenges, this research paper proposes a novel approach that combines the stockwell transform (ST) with an improved grey wolf optimization-based kernel extreme learning machine to enhance classification accuracy. The stockwell transform is utilized to extract meaningful features from the power quality (PQ) signals, which are subsequently input into the kernel extreme learning machine (KELM). Furthermore, the parameters of the KELM model are optimized using the improved grey wolf optimization (IGWO) approach to improve the accuracy of classification. To evaluate the performance of the proposed method, real-time implementation is considered by incorporating PQDs data with signal-to-noise ratios (SNR) of 20 dB, 30 dB, and 40 dB into the original synthetic signals. Multiple noise conditions are simulated to assess the proposed model ability to identify and classify disturbance signals. The results demonstrate a detection accuracy of 99.76% under noiseless conditions, indicating the model’s high accuracy. Moreover, the proposed method exhibits robustness to noise, achieving accuracies of 98.86%, 98.32%, and 97.3% at SNR levels of 40 dB, 30 dB, and 20 dB, respectively. In the end, this work has performed a comparative study with other previously published work. Compared with other classification methods, the algorithm proposed in this paper has higher accuracy, and it is an efficient and feasible classification method.

Maiden Application of a Modified HBA-Optimized Cascaded (2DOF + FOPIDN)-PD Controller for Load Frequency Control of Diverse Source Power System

Electricity has become one of the most essential components of establishing a quality standard of living in any country. Consequently, considerable work has been focused on designing a sophisticated load frequency control (LFC) system. However, in light of limited resources and real-world challenges, computationally based control algorithms that are more effective and less expensive remain critically needed. Thus, this paper employs a modified honey badger algorithm (HBA) in conjunction with the concepts of Lévy flight and inertia weight to optimize the parameters of a new cascaded two-degree-of-freedom fractional-PID structure coupled with a proportional derivative (2DOF + FOPIDN)-PD controller to solve LFC problems in an interconnected power system (IPS) comprising conventional and renewable energy sources (RES). The proposed control technique is applied to a two-area IPS under diverse load conditions and in the presence of nonlinear elements and electronic devices. The proposed method is evaluated with respect to a range of performance metrics, such as settling time, undershoots, and error criteria values. The collective performance of the established control scheme indicated that the suggested control approach provides excellent reliability under various load condition scenarios, sensitivity tests, and perturbations, proving the system’s efficacy and dependability.

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Modelling and Analysis on AC Loss of HTS Tapes With Magnetic Substrate Under Harmonic Current in Power Systems

Due to the wide application of large-scale non-linear loads and switching power electronic devices, current in the power grid is often mixed with many harmonic components. This paper numerically analyzed the influences of current harmonics on the AC loss of HTS tapes with magnetic substrate, considering the impacts of different harmonic orders and different harmonic distortion rates. The AC loss of the HTS tape with the magnetic substrate is analyzed under two conditions: Case 1 transport current; Case 2 external magnetic field. The study involves the influence of current harmonics on the AC loss of HTS tapes, which can lay theoretical foundations for practical industrial applications with superconducting devices in power systems.

Harmonic source identification method based on sinusoidal approximation

The proportion of nonlinear electronic devices such as electric arc furnace is increasing day by day, and the proportion of harmonic content in power grid is also more and more cannot be ignored. Therefore, the identification of harmonic source has received the attention of experts and scholars in the industry at present. Therefore, how to accurately locate and reduce harmonic content from power system source becomes the first step to improve power quality. This paper firstly analyzes the causes of harmonics and points out that nonlinear load is the fundamental cause of harmonics in power system. Secondly, the load equivalent model, the composition of the model and the method to determine the model parameters are established. Meanwhile, the current distortion coefficient is introduced to determine the harmonic source. Finally, an example is designed to verify the feasibility and rationality of the proposed method. Compared with the existing analytical methods at present, the proposed method has significantly improved the accuracy of harmonic source identification.

Dynamic harmonic domain approach to assess performance of three-phase fixed capacitor thyristor controlled reactor (FC-TCR)

Harmonics have become an important issue as the number of non-linear elements and electronic devices coupled to electrical power systems increases by the day. A Fixed Capacitor Thyristor Controlled Reactor (FC-TCR) is widely used in power systems to continuously control reactive power. This paper presents the harmonic analysis of a three-phase FC-TCR under different operating conditions using the Dynamic Harmonic Domain (DHD) technique. The existing Harmonic Domain (HD) method is widely used to obtain the steady-state harmonics in the power grid. It is not possible to calculate transient harmonics using the harmonic domain method. The dynamic harmonic domain technique also provides a solution for the transient condition. The three-phase FC-TCR is simulated in a MATLAB environment and calculates transient harmonic quantities such as apparent power S(t), reactive power Q(t), real power P(t), and distortion power D(t), RMS values, and distortion factors of the three-phase FC-TCR when subjected to voltage disturbance.

A Linear Model for Classification of Electronic Devices Using Harmonic Radar

A new approach to exploit nonlinear reradiation of electronic circuits under test (ECUT) for classification of electronic devices using harmonic radar is proposed in this article. Unlike prior approaches, we develop a linear model to relate the measurements to the unknown parameters representing the nonlinear characteristics of the ECUT. Each nonlinear circuit under test has a distinguishable response to a power-varying single-tone or two-tone incident wave. As a result, each ECUT possesses a unique unknown deterministic vector of parameters derived from the proposed linear model. We use maximum likelihood estimator in the presence of complex white Gaussian noise based on the newly constructed linear model. The statistical features of the normalized estimated vectors of parameters are employed as distinguishing factors in classification of different nonlinear electronic devices using K-nearest neighbors classification method. Simulation results show that the proposed method of linear model with power-swept signals and estimated vectors of parameters successfully classifies nonlinear devices.