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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 highlighting 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 in neural networks) shedding light on the associated electrical effects.

Very often researchers in the field of resistive switching devices or memristors need to model their experimental data using a compact representation without dealing with the complexities of a circuit simulator or a differential equation solver. Moreover, once a given device is simulated, one is always tempted to find out what would be the effect of changing the value or dependence of one or several model parameters in a simple and straightforward way. In this letter, we provide such a tool. The dynamic memdiode model for the conduction characteristics of resistive switching devices has been rewritten and simplified so as to comply with these demanding requirements. The model formulation is essentially the same except that it is now expressed using a recursive approach fully compatible with its original foundations. This has been possible thanks to the inherent symmetry of the model equations and to the unification of the physical description of the set and reset processes. We show how the proposed approach can be used as a test bench for different hypotheses, in particular, we focus here on the role played by the characteristic switching times during the state transitions.

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. Our study indicates that a M-OTP memory device operating in the quantum regime not only is feasible but also that its stochastic features are addressable by circuit simulations.