Search Results (27)

This paper presents two classic analog oscillators: a relaxation oscillator and a Wien bridge one, where a memristor replaces a resistor. The circuits are simulated in TopSPICE 7.12 using a memristor emulation circuit and commercially available components to evaluate the memristor’s impact. In the case of the relaxation oscillator, which includes the memristor, a notable increase in oscillation frequency was observed compared to the classical circuit, with a nearly 10-fold increase from 790 Hz to 7.78 kHz while maintaining a constant amplitude. This confirms the influence of the memristor’s dynamic resistance on the circuit time constant. On the other hand, the Wien-bridge oscillator exhibits variations in specific parameters, such as peak voltage, amplitude, and frequency. In this case, the oscillation frequency decreased from 405 Hz to 146 Hz with the addition of the memristor, a characteristic introduced by the proposed memristive element’s nonlinear interactions. Experimental results confirm the feasibility of incorporating memristors into classical oscillator circuits, enabling frequency changes while maintaining stable oscillations, allowing reconfigurable and adaptable analog designs that leverage the properties of memristive devices. Full article

Memristive elements, known as memristors, memcapacitors and meminductors, have become an important topic of research in the electronics world in recent years. As there is still no efficient way to manufacture two-terminal memristive elements, many researchers have focused their efforts on designing emulator circuits that mimic these devices. In this study, a memcapacitor emulator circuit using Current Derivative Buffered Amplifier (CDBA) is proposed, which has significant advantages such as wide dynamic range, differential structure at the input port, high sloping rate and wide bandwidth. The main advantages of the emulator are that it is floating without grounding constraint, it is electronically adjustable, it has charge-controlled incremental and decremental modes and it has a simpler circuit structure since it does not contain a memristor. To ensure the integrity of the circuit theory, the results of the mathematical model and the simulation of the memcapacitor are given together. In addition, the characteristics of the experimentally investigated memcapacitor emulator are in good agreement with the simulation results. To provide an illustration of the performance of the proposed emulator, firstly the second-order active low-pass filter circuit and subsequently the amoeba learning circuit are selected as the working environment. The results show that the filtering performance of the proposed emulator at a value after the cut-off frequency in the filter circuit is $25\%$ more efficient than a standard capacitor and in terms of power consumption, it consumes $27.93\%$ less power than a standard capacitor. Moreover, the emulator successfully accomplishes the learning and data storage tasks in the amoeba learning circuit. Full article

This paper introduces a fully integrated memristive chaotic circuit, which is based on a voltage-controlled oscillator (VCO). The circuit employs a fully integrated architecture that offers reduced power consumption and a smaller footprint compared to the use of discrete components. Specifically, the VCO is utilized to generate the oscillatory signal, whereas the memristor emulator circuit serves as the nonlinear element. The memristor emulator circuit is constructed using a single operational transconductance amplifier (OTA), two transistors, and a grounded capacitor. This straightforward design contributes to diminished power usage within the chip’s area. The VCO incorporates a dual delay unit and implements current compensation to enhance the oscillation frequency and to broaden the VCO’s tunable range. Fabricated using the SMIC 180 nm CMOS process, this chaotic circuit occupies a mere 0.0072 mm² of chip area, demonstrating a design that is both efficient and compact. Simulation outcomes indicate that the proposed memristor emulator is capable of operating at a maximum frequency of 300 MHz. The memristive chaotic circuit is able to produce a chaotic oscillatory signal with an operational frequency ranging from 158 MHz to 286 MHz, powered by a supply of 0.9 V, and with a peak power consumption of 3.5553 mW. The Lyapunov exponent of the time series within the resultant chaotic signal spans from 0.2572 to 0.4341. Full article

Memristors have emerged as prospective two-terminal elements, having applications in memory, neuromorphic systems, and analog circuits. Biological materials such as egg albumin exhibit memristive behavior, displaying a distinctive pinched hysteresis signature in their current-voltage characteristics. However, such memristive behavior must be mathematically modeled to gain insights into the material’s operation and utilize it in various circuit applications. This article proposes a novel SPICE-level framework for fabricated egg albumin memristors using Joglekar’s memristor model. Experimental current-voltage characteristics are used to calibrate the SPICE model, ensuring accurate reproducibility of the experimental results. Additionally, the impact of variations in model-specific parameters on dynamic resistance and device performance is explored. Full article

Directional ionic migration in ultra-thin metal-oxide semiconductors under applied electric fields is a key mechanism for developing various electronic nanodevices. However, understanding charge transfer dynamics is challenging due to rapid ionic migration and uncontrolled charge transfer, which can reduce the functionality of microelectronic devices. This research investigates the supercapacitive-coupled memristive characteristics of ultra-thin heterostructured metal-oxide semiconductor films at TiO₂-In₂O₃/Au Schottky junctions. Using atomic layer deposition (ALD), we nano-engineered In₂O₃/Au-based metal/semiconductor heterointerfaces. TEM studies followed by XPS elemental analysis revealed the chemical and structural characteristics of the heterointerfaces. Subsequent AFM studies of the hybrid heterointerfaces demonstrated supercapacitor-like behavior in nanometer-thick TiO₂-In₂O₃/Au junctions, resembling ultra-thin supercapacitors, pseudocapacitors, and nanobatteries. The highest specific capacitance of 2.6 × 10⁴ F.g⁻¹ was measured in the TiO₂-In₂O₃/Au junctions with an amorphous In₂O₃ electron gate. Additionally, we examined the impact of crystallization, finding that thermal annealing led to the formation of crystalline In₂O₃ films with higher oxygen vacancy content at TiO₂-In₂O₃ heterointerfaces. This crystallization process resulted in the evolution of non-zero I-V hysteresis loops into zero I-V hysteresis loops with supercapacitive-coupled memristive characteristics. This research provides a platform for understanding and designing adjustable ultra-thin Schottky junctions with versatile electronic properties. Full article

Typically, organic multiferroic junctions (OMFJs) are formed of an organic ferroelectric layer sandwiched between two ferromagnetic electrodes. The main scientific interest in OMFJs focuses on the magnetoresistive properties of the magnetic spin valve combined with the electroresistive properties associated with the ferroelectric junction. In consequence, memristive properties that couple magnetoelectric functionalities, which are one of the most active fields of research in material sciences, are opening a large spectrum of technological applications from nonvolatile memory to elements in logic circuits, sensing devices, energy harvesting and biological synapsis models in the emerging area of neuromorphic computing. The realization of these multifunctional electronic elements using organic materials is presenting various advantages related to their low-cost, versatile synthesis and low power consumption functioning for sustainable electronics; green disintegration for transient electronics; and flexibility, light weight and/or biocompatibility for flexible electronics. The purpose of this review is to address the advancement of all OMFJs including not only the achievements in the charge and spin transport through OMFJs together with the effects of electroresistance and magnetoresistance but also the challenges and ways to overcome them for the most used materials for OMFJs. Full article

Empathy is the emotional capacity to feel and understand the emotions experienced by other human beings from within their frame of reference. As a unique psychological faculty, empathy is an important source of motivation to behave altruistically and cooperatively. Although human-like emotion should be a critical component in the construction of artificial intelligence (AI), the discovery of emotional elements such as empathy is subject to complexity and uncertainty. In this work, we demonstrated an interesting electrical device (i.e., an MXene (Ti₃C₂) memristor) and successfully exploited the device to emulate a psychological model of “empathic blame”. To emulate this affective reaction, MXene was introduced into memristive devices because of its interesting structure and ionic capacity. Additionally, depending on several rehearsal repetitions, self-adaptive characteristic of the memristive weights corresponded to different levels of empathy. Moreover, an artificial neural system was designed to analogously realize a moral judgment with empathy. This work may indicate a breakthrough in making cool machines manifest real voltage-motivated feelings at the level of the hardware rather than the algorithm. Full article

The development of novel hardware computing systems and methods has been a topic of increased interest for researchers worldwide. New materials, devices, and architectures are being explored as a means to deliver more efficient solutions to contemporary issues. Along with the advancement of technology, there is a continuous increase in methods available to address significant challenges. However, the increased needs to be fulfilled have also led to problems of increasing complexity that require better and faster computing and processing capabilities. Moreover, there is a wide range of problems in several applications that cannot be addressed using the currently available methods and tools. As a consequence, the need for emerging and more efficient computing methods is of utmost importance and constitutes a topic of active research. Among several proposed solutions, we distinguish the development of a novel nanoelectronic device, called a “memristor”, that can be utilized both for storing and processing, and thus it has emerged as a promising circuit element for the design of compact and energy-efficient circuits and systems. The memristor has been proposed for a wide range of applications. However, in this work, we focus on its use in computing architectures based on the concept of Cellular Automata. The combination of the memristor’s performance characteristics with Cellular Automata has boosted further the concept of processing and storing information on the same physical units of a system, which has been extensively studied in the literature as it provides a very good candidate for the implementation of Cellular Automata computing with increased potential and improved characteristics, compared to traditional hardware implementations. In this context, this paper reviews the most recent advancements toward the development of Cellular-Automata-based computing coupled with memristor devices. Several approaches for the design of such novel architectures, called “Memristive Cellular Automata”, exist in the literature. This extensive review provides a thorough insight into the most important developments so far, helping the reader to grasp all the necessary information, which is here presented in an organized and structured manner. Thus, this article aims to pave the way for further development in the field and to bring attention to technological aspects that require further investigation. Full article

Memristive devices have garnered significant attention in the field of electronics over the past few decades. The reason behind this immense interest lies in the ubiquitous nature of memristive dynamics within nanoscale devices, offering the potential for revolutionary applications. These applications span from energy-efficient memories to the development of physical neural networks and neuromorphic computing platforms. In this research article, the angle toppling technique (ATT) was employed to fabricate titanium dioxide (TiO₂) nanoparticles with an estimated size of around 10 nm. The nanoparticles were deposited onto a 50 nm SiO_x thin film (TF), which was situated on an n-type Si substrate. Subsequently, the samples underwent annealing processes at temperatures of 550 °C and 950 °C. The structural studies of the sample were done by field emission gun-scanning electron microscope (FEG-SEM) (JEOL, JSM-7600F). The as-fabricated sample exhibited noticeable clusters of nanoparticles, which were less prominent in the samples annealed at 550 °C and 950 °C. The element composition revealed the presence of titanium (Ti), oxygen (O₂), and silicon (Si) from the substrate within the samples. X-ray diffraction (XRD) analysis revealed that the as-fabricated sample predominantly consisted of the rutile phase. The comparative studies of charge storage and endurance measurements of as-deposited, 550 °C, and 950 °C annealed devices were carried out, where as-grown device showed promising responses towards brain computing applications. Furthermore, the teaching–learning-based optimization (TLBO) technique was used to conduct further comparisons of results. Full article

Mobile phones, laptops, computers, digital watches, and digital calculators are some of the most used products in our daily life. In the background, to make these gadgets work as per our desire, there are many simple components necessary for electronics to function, such as resistors, capacitors, and inductors, which are three basic circuit elements. The Memristor is one such component. This paper provides simulation results of the memristor circuit and its V-I characteristics at different functions as an input signal. A well-trained ANN is able to recognize images with higher precision. To enhance the properties such as accuracy, precision, and efficiency in recognition, memristor characteristics are introduced to the neural network, however, older devices experience some non-linearity issues, causing conductance-tuning problems. At the same time, to be used in some advanceable applications, ANN requires a huge amount of vector-matrix multiplication based on in-depth network expansion. An ionic floating gate (IFG) device with the characteristics of a memristive device can solve these problems. This work proposes a fully connected ANN using the IFG model, and the simulation results of the IFG model are given as synapses in deep learning. We use algorithms such as the gradient-descent model, forward and backward propagation for network building, and weight setting in neural networks to enhance their ability to recognize images. A well-trained network is formed by tuning those memristive devices to an optimized state. The synaptic memory obtained from the IFG device will be used in other deep neural networks to increase recognition accuracy. To be an activation function in the neural network, sigmoid functions were used but later replaced by the ReLu function to avoid vanishing gradients. This paper shows how images were recognized by their front, top, and side views. Full article

The proposal of the memristor, considered as the fourth basic circuit element, suggests a new possibility for the design of high-performance power electronic systems. However, it also brings new challenges. At present, more and more electrical equipment and systems have demonstrated that their external characteristics can exhibit “8”-shaped hysteresis loops and can be regard as memristive equipment and systems. In order to satisfy the requirements of controllability, flexibility, efficiently, and so on, most memristive equipment and systems are not directly connected to the power grid but instead obtain their own required powering through various forms of power electronic converters. Note that memristive loads are distinctive and demonstrate unique nonlinear behaviors. Similarly, there can be nonlinearity from the resistor (R), inductor (L), or capacitor (C) load, but there is no combination of only R, L, and C that could produce memristive characteristics. In particular, the memristance of memristive devices changes continuously during the operation process; in addition, practical power electronic systems composed of memristive devices and power supplies have strong nonlinear characteristics, which are more likely to result in various complex behaviors and are not conducive to the stable operation of the systems. Therefore, exploring complex instability behaviors of power electronic systems with strong nonlinearity in depth is necessary for better protection and utilization of memristive devices. This paper provides an outline of the status of research on complex behaviors of power electronic systems with memristive load; it is expected to provide guidance for the study of complex behavior of strongly nonlinear systems. Full article

As the fourth fundamental circuit element, the memristor may execute computations while storing data. Fracmemristor takes advantage of the fractional calculate’s long-term memory, non-locality, weak singularity, and the memristor’s storage–computational integration. Since the physical structure of the fracmemristor is similar to the topology of the ant transfer probability flow in ACO, we propose the fractional-order memristive ant colony algorithm (FMAC), which uses the fracmemristor physical system to record the probabilistic transfer information of the nodes that the ant will crawl through in the future and pass it to the current node of the ant, so that the ant acquires the ability to predict the future transfer. After instigating the optimization capabilities with TSP, we discovered that FMAC is superior to PACO-3opt, the best integer-order ant colony algorithm currently available. FMAC operates substantially more quickly than the fractional-order memristor ant colony algorithm due to the transfer probability prediction module based on the physical fracmemristor system (FACA). Full article

Convolutional neural networks (CNNs) have been widely used in image recognition and processing tasks. Memristor-based CNNs accumulate the advantages of emerging memristive devices, such as nanometer critical dimensions, low power consumption, and functional similarity to biological synapses. Most studies on memristor-based CNNs use either software models of memristors for simulation analysis or full hardware CNN realization. Here, we propose a hybrid CNN, consisting of a hardware fixed pre-trained and explainable feature extractor and a trainable software classifier. The hardware part was realized on passive crossbar arrays of memristors based on nanocomposite (Co-Fe-B)_x(LiNbO₃)_100−x structures. The constructed 2-kernel CNN was able to classify the binarized Fashion-MNIST dataset with ~ 84% accuracy. The performance of the hybrid CNN is comparable to the other reported memristor-based systems, while the number of trainable parameters for the hybrid CNN is substantially lower. Moreover, the hybrid CNN is robust to the variations in the memristive characteristics: dispersion of 20% leads to only a 3% accuracy decrease. The obtained results pave the way for the efficient and reliable realization of neural networks based on partially unreliable analog elements. Full article

Scaling fractional-order memristor circuit is important for realizing a fractional-order memristor. However, the effective operating-frequency range, operation order, and fractional-order memristance of the scaling fractional-order memristor circuit have not been studied thoroughly; that is, the fractional-order memristance in the effective operating-frequency range has not been calculated quantitatively. The fractional-order memristance is a similar and equally important concept as memristance, memcapacitance, and meminductance. In this paper, the frequency-domain characteristic-analysis principle of the fractional-order memristor is proposed based on the order- and F-frequency characteristic functions. The reasons for selecting the order- and F-frequency characteristic functions are explained. Subsequently, the correctness of the frequency-domain characteristic analysis using the order- and F-frequency characteristic functions is verified from multiple perspectives. Finally, the principle of the frequency-domain characteristic analysis is applied to the recently realized chain-scaling fractional-order memristor circuit. The results of this study indicate that the principle of the frequency-domain characteristic analysis of the fractional-order memristor can successfully calculate the fractional-order memristance of the chain-scaling fractional-order memristor circuit. The proposed principle of frequency-domain characteristic analysis can also be applied to mem-elements, such as memristors, memcapacitors, and meminductors. The main contribution of this study is the principle of the frequency-domain characteristic analysis of the fractional-order memristor based on the order- and F-frequency characteristic functions. Full article

As two-terminal passive fundamental circuit elements with memory characteristics, memristors are promising devices for applications such as neuromorphic systems, in-memory computing, and tunable RF/microwave circuits. The increasingly complex electromagnetic interference (EMI) environment threatens the reliability of memristor systems. However, various EMI signals’ effects on memristors are still unclear. This paper selects continuous waves (CWs) as EMI signals. It provides a deeper insight into the interference effect of CWs on the memristor driven by a sinusoidal excitation voltage, as well as a method for investigating the EMI effect of memristors. The optimal memristor model is obtained by the exhaustive traversing of the possible model parameters, and the interference effect of CWs on memristors is quantified based on this model and the proposed evaluation metrics. Simulation results indicate that CW interference may affect the switching time, dynamic range, nonlinearity, symmetry, time to the boundary, and variation of memristance. The specific interference effect depends on the operating mode of the memristor, the amplitude, and the frequency of the CW. This research provides a foundation for evaluating EMI effects and designing electromagnetic protection for memristive neuromorphic systems. Full article