Memory Circuit Elements Research Articles

Are There an Infinite Number of Passive Circuit Elements in the World?

We found that a second-order ideal memristor [whose state is the charge, i.e., in ] degenerates into a negative nonlinear resistor with an internal power source. After extending analytically and geographically the above local activity (experimentally verified by the two active higher-integral-order memristors extracted from the famous Hodgkin–Huxley circuit) to other higher-order circuit elements, we concluded that all higher-order passive memory circuit elements do not exist in nature and that the periodic table of the two-terminal passive ideal circuit elements can be dramatically reduced to a reduced table comprising only six passive elements: a resistor, inductor, capacitor, memristor, mem-inductor, and mem-capacitor. Such a bounded table answered an open question asked by Chua 40 years ago: Are there an infinite number of passive circuit elements in the world?

Strange Fractal Attractors and Optimal Chaos of Memristor–Memcapacitor via Non-local Differentials

The multi-dimensional electronic devices are so called memory circuit elements (memristor or memcapacitor); such memory circuit elements usually rely on previous applied voltage, current, flux or charge based on memory capability with their resistance, capacitance or inductance. In view of above fact, this manuscript investigates the non-integer modeling of memristor–memcapacitor in discrete-time domain through non-singular kernels of fractal fractional differentials and integrals operators. The governing equations of memristor–memcapacitor have been developed for the sake of the dynamical characteristics of simple chaotic circuit. The fractal fractional differentials and integrals operators have been invoked for non-integer modeling of memristor–memcapacitor that can exhibit a combination of dynamical chaotic phenomena. The numerical schemes, numerical simulations, stability analysis and equilibrium points have been highlighted in detail. The comparative chaotic graphs have been discussed in three ways (i) by keeping fractal component fixed and varying fractional component distinctly, (ii) by keeping fractional component fixed and varying fractal component distinctly and (iii) by varying both fractal component and fractional component distinctly. Our results suggest that fractal-fractional model of memristor–memcapacitor retains the memory characteristics.

Quality indicators in methodology for designing RAM units as part of microprocessor systems in digital devices used in environmental engineering

The article is devoted to the design of RAM blocks as part of microprocessor systems and methods for ensuring fault tolerance. The structural diagram of RAM and the process of the influence of heavy charged particles (HCP) on a memory integrated circuit (IC) are considered. Particular attention is paid to the influence of the biopolar effect on the fault tolerance of IC elements, as well as to emerging multibit events. The article analyzes the various phases of RAM operation and the reactions of memory circuit elements to the occurrence of failures caused by the impact of environment in digital devices used in environmental engineering.

Electronically Controllable Memcapacitor Circuit With Experimental Results

The memcapacitor is one of the new memory circuit elements although it is not found on the market as a discrete circuit element. In this study, two simple memcapacitor emulators were developed using active circuit elements. These proposed circuits are both electronically controllable. The relationship between charge and flux depending on different frequencies was obtained for both presented memcapacitor circuits. To demonstrate the experimental response of the circuit, discrete circuit elements were used to build the first proposed circuit on the breadboard. All experimental results showed good agreement with the theoretical analyses.

Electronically Tunable Memcapacitor Emulator Based on Operational Transconductance Amplifiers

Memory circuit elements are of interest due to their use in different fields of science and technology. In this research, a new multi-outputs operational transconductance amplifiers (MO-OTA)-based memcapacitor emulator is proposed. The proposed emulator employs two OTAs, two capacitors, two resistors and an analog multiplier. The memcapacitor emulator circuit has electronically tunability property. Charge value of the memcapacitor can be adjusted by changing the transconductance [Formula: see text] value with the biasing current of the MO-OTA or frequency value of the input signal. In order to analyze the performance of the proposed circuit, memcapacitor emulator is simulated in 0.18[Formula: see text][Formula: see text]m TSMC CMOS process using LTSPICE and the simulation results are demonstrated.

Анализ качества проектирования блоков ОЗУ в составе микропроцессорных систем с обеспечением минимальной сбоеустойчивости

The article is devoted to the design of RAM blocks as part of microprocessor systems and methods for ensuring fault tolerance. The structural scheme of RAM and the process of influence of heavy charged particles on the integrated circuit (IC) of memory are considered. Special attention is paid to the influence of the biopolar effect on the fault stability of the IC elements, as well as to the emerging multibit events. The article analyzes the various phases of RAM operation and the reactions of memory circuit elements to the occurrence of failures caused by the hit of HCP.

Memory Circuit Elements: Complexity, Complex Systems, and Applications

Memory Circuit Elements: Complexity, Complex Systems, and Applications

Memory models of adaptive behavior.

Adaptive response to varying environment is a common feature of biological organisms. Reproducing such features in electronic systems and circuits is of great importance for a variety of applications. We consider memory models inspired by an intriguing ability of slime molds to both memorize the period of temperature and humidity variations and anticipate the next variations to come, when appropriately trained. Effective circuit models of such behavior are designed using: 1) a set of LC contours with memristive damping and 2) a single memcapacitive system-based adaptive contour with memristive damping. We consider these two approaches in detail by comparing their results and predictions. Finally, possible biological experiments that would discriminate between the models are discussed. In this paper, we also introduce an effective description of certain memory circuit elements.

Port-Hamiltonian Formulation of Systems With Memory

In this paper, we consider memristors, meminductors, and memcapacitors and their properties as port-Hamiltonian systems. The port-Hamiltonian formalism naturally arises from network modeling of physical systems in a variety of domains. Exposing the relation between the energy storage, dissipation, and interconnection structure, this framework underscores the physics of the system. One of the strong aspects of the port-Hamiltonian formalism is that a power-preserving interconnection between port-Hamiltonian systems results in another port-Hamiltonian system with composite energy, dissipation, and interconnection structure. This feature can advantageously be used to model, analyze, and simulate networks consisting of complex interconnections of both conventional and memory circuit elements. Furthermore, the port-Hamiltonian formalism naturally extends the fundamental properties of the memory elements beyond the realm of electrical circuits.

Lagrange formalism of memory circuit elements: Classical and quantum formulations

The general Lagrange-Euler formalism for the three memory circuit elements, namely, memristive, memcapacitive, and meminductive systems, is introduced. In addition, mutual meminductance, i.e., mutual inductance with a state depending on the past evolution of the system, is defined. The Lagrange-Euler formalism for a general circuit network, the related work-energy theorem, and the generalized Joule's first law are also obtained. Examples of this formalism applied to specific circuits are provided, and the corresponding Hamiltonian and its quantization for the case of nondissipative elements are discussed. The notion of memory quanta, the quantum excitations of the memory degrees of freedom, is presented. Specific examples are used to show that the coupling between these quanta and the well-known charge quanta can lead to a splitting of degenerate levels and to other experimentally observable quantum effects.

Teaching Memory Circuit Elements via Experiment-Based Learning

The class of memory circuit elements which comprises memristive, memcapacitive, and meminductive systems, is gaining considerable attention in a broad range of disciplines. This is due to the enormous flexibility these elements provide in solving diverse problems in analog/neuromorphic and digital/quantum computation; the possibility to use them in an integrated computing-memory paradigm, massively-parallel solution of different optimization problems, learning, neural networks, etc. The time is therefore ripe to introduce these elements to the next generation of physicists and engineers with appropriate teaching tools that can be easily implemented in undergraduate teaching laboratories. In this paper, we suggest the use of easy-to-build emulators to provide a hands-on experience for the students to learn the fundamental properties and realize several applications of these memelements. We provide explicit examples of problems that could be tackled with these emulators that range in difficulty from the demonstration of the basic properties of memristive, memcapacitive, and meminductive systems to logic/computation and cross-bar memory. The emulators can be built from off-the-shelf components, with a total cost of a few tens of dollars, thus providing a relatively inexpensive platform for the implementation of these exercises in the classroom. We anticipate that this experiment-based learning can be easily adopted and expanded by the instructors with many more case studies.

Neuromorphic, Digital, and Quantum Computation With Memory Circuit Elements

Memory effects are ubiquitous in nature and the class of memory circuit elements - which includes memristors, memcapacitors and meminductors - shows great potential to understand and simulate the associated fundamental physical processes. Here, we show that such elements can also be used in electronic schemes mimicking biologically-inspired computer architectures, performing digital logic and arithmetic operations, and can expand the capabilities of certain quantum computation schemes. In particular, we will discuss few examples where the concept of memory elements is relevant to the realization of associative memory in neuronal circuits, spike-timing-dependent plasticity of synapses, digital and field-programmable quantum computing.

Memory Circuit Elements: From Systems to Applications

In this paper, we briefly review the concept of memory circuit elements, namely memristors, memcapacitors and meminductors, and then discuss some applications by focusing mainly on the first class. We present several examples, their modeling and applications ranging from analog programming to biological systems. Since the phenomena associated with memory are ubiquitous at the nanoscale, we expect the interest in these circuit elements to increase in coming years.

Memory effects in complex materials and nanoscale systems

Memory effects are ubiquitous in nature and are particularly relevant at the nanoscale where the dynamical properties of electrons and ions strongly depend on the history of the system, at least within certain time scales. We review here the memory properties of various materials and systems which appear most strikingly in their non-trivial time-dependent resistive, capacitative and inductive characteristics. We describe these characteristics within the framework of memristors, memcapacitors and meminductors, namely memory circuit elements whose properties depend on the history and state of the system. We examine basic issues related to such systems and critically report on both theoretical and experimental progress in understanding their functionalities. We also discuss possible applications of memory effects in various areas of science and technology ranging from digital to analog electronics, biologically-inspired circuits, and learning. We finally discuss future research opportunities in the field.