Abstract
The purpose of this paper was to propose a complete analysis and parameter estimations of a new simplified and highly nonlinear hafnium dioxide memristor model that is appropriate for high-frequency signals. For the simulations; a nonlinear window function previously offered by the author together with a highly nonlinear memristor model was used. This model was tuned according to an experimentally recorded current–voltage relationship of a HfO2 memristor. This study offered an estimation of the optimal model parameters using a least squares algorithm in SIMULINK and a methodology for adjusting the model by varying its parameters overbroad ranges. The optimal values of the memristor model parameters were obtained after minimizing the error between the experimental and simulated current–voltage characteristics. A comparison of the obtained errors between the simulated and experimental current–voltage relationships was made. The error derived by the optimization algorithm was a little bit lower than that obtained by the used methodology. To avoid convergence problems; the step function in the considered model was replaced by a differentiable tangent hyperbolic function. A PSpice library model of the HfO2 memristor based on its mathematical model was created. The considered model was successfully applied and tested in a multilayer memristor neural network with bridge memristor–resistor synapses
Highlights
The memristor is a very important electricone-port element, together with the capacitor, resistor, and inductor [1,2]
The highly nonlinear Lehtonen–Laihomemristor model [7,8] is based on physical measurements and on the mechanism of the electric current flow through amorphous semiconducting transition-metal oxides [8]
The coefficient γ represents the exponentially increasing nonlinear ionic dopant drift for higher voltage signals. In this memristor model described in References [7,8], the state variable x is a normalized parameter in the range [0,1]
Summary
The memristor is a very important electricone-port element, together with the capacitor, resistor, and inductor [1,2]. The highly nonlinear Lehtonen–Laihomemristor model [7,8] is based on physical measurements and on the mechanism of the electric current flow through amorphous semiconducting transition-metal oxides [8] It has a good precision, is adjustable, and can be described using mathematical equations [7,8]. The motivation for the present investigation was to offer a simple nonlinear ionic drift model for a HfO2 memristor with a strongly nonlinear window function [14,15],parameter estimations of the model using the least squares algorithm in MATLAB-Simulink [15,16], to minimize the root mean square error and provide a simulation the memristor model with the obtained parameters, to compare the derived errors with those obtained using the applied methodology, to compare the errors with those derived using the best existing memristor models [10,11], and to replace the step function in the considered model using a differentiable tangent hyperbolic function [17].
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