Tuning the conductance of a memristive device is a  process that requires energy and involves power dissipation. In  this letter, the role the memory state programming strategy plays  in this connection is investigated. To this end, the device model  equations representing the electron transport and metal  ion/oxygen vacancy displacement caused by the application of an external signal must be solved consistently. However, if instead of  considering the applied voltage as the model input, a memory state  trajectory is assumed, the model equations can be decoupled  allowing an analytic description of the problem. In order to  accomplish this objective a more accurate version of the dynamic  memdiode model is used which incorporates additional physical  considerations in the characteristic switching times. It is  demonstrated that alternative trajectories (concave, convex, and  sigmoidal) lead to a variety of energy consumption-maximum  dissipated power relationships indicating the key role played by  the selected programming strategy. This kind of study contributes  to the basic understanding of the writing process of memristors (synaptic weight assignment) and sheds light on its electrical  consequences. Â

Circuit simulators are fundamentally used for solving electric circuit problems with different degrees of complexity in which node voltages and branch currents are the unknowns. This is fully understandable since they were originally created for this specific task. However, behind the curtains, powerful simulation engines based on a variety of numerical techniques operate so as to always comply with Kirchhoff’s current and voltage laws. In this paper, it is shown how a simple circuital configuration, referred to as the elementary solver, consistent in two behavioral current sources in series, can be used to solve mathematical problems that go beyond electronics. Of course, the intention is not to substitute mathematical packages with well proven calculus capacity but to increase the scope of circuit simulators for their application in other areas of research or simply for educational purposes. It is worth mentioning that no special programming skills are required (except a basic knowledge of the available tools and language) and, furthermore, that the user can operate exclusively in a graphical environment. It is shown, throughout a series of selected examples, how the method of elementary solvers (MES) works providing a new and practical dimension to the applicability of circuit simulators.

This letter reports a compact SPICE model for the electron transport characteristics of Al2O3/HfO2-based nanolaminates for their use in multilevel one-time programmable (M-OTP) memories. The model comprises three simulation blocks corresponding to the electrical stimulus applied to the device, the equivalent circuit of the memory cell, and the events generator associated with the dielectric breakdown of the insulating layer. For a clear assessment of the quantum effects occurring in these structures, constant voltage stress was used as the primary electrical stimulus. The antifuse (AF) cell is represented by a combination of series and parallel resistances that account for the formation of filamentary conducting paths with quantum properties across the structure. The arrival of successive breakdown events is simulated using a power-law nonhomogeneous Poisson process.

This work reports a compact behavioral model for the hysteretic conduction characteristics of Al/Gd0.1Ca0.9MnO3(GCMO)/Au resistive switching devices suitable for SPICE simulations. The devices are nonvolatile, forming-less, compliance-free, and self-rectifying multistate memristive structures which makes them of maximum interest for neuromorphic computing and memory applications. The proposed model relies on two coupled equations, one for the electron transport and a second one for the vacancy displacement. The proposed model considers a novel approach for solving the internal state of the device based on the so-called generalized quasi-static hysteron whose application can be extended to other structures and dynamics in addition to the ones discussed here.

As theoretically predicted by Prof. Chua, the input signal frequency has a major impact on the electrical behavior of memristors. According with one of the so-called fingerprints of such devices, the resistive window, i.e. the difference between the low and high resistance states, shrinks as the frequency increases for a given input signal amplitude. Physically, this effect stems from the incapability of ions/vacancies to follow the external electrical stimulus. In terms of the electrical behavior, the collapse of the resistive window can be ascribed to the shift of the set/reset voltages toward higher values. Moreover, for a given frequency, the resistance window increases with the signal amplitude. In this letter, we show that both phenomena are the two sides of the same coin and that can be consistently explained after considering the snapback effect and a balance model equation for the device memory state.

This paper reports the fundamentals and SPICE implementation of the dynamic memdiode model (DMM) for the conduction characteristics of bipolar resistive switching devices. The model equations are implemented in the LTSpice simulator using an equivalent circuital approach with behavioral components and sources.

A memory state equation consistent with several experimental observations is presented and discussed within the framework of Chua's memristive systems theory. The proposed equation describes the evolution of the memory state corresponding to a bipolar resistive switching device subjected to a variety of electrical stimulus.