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A New Simplified Model and Parameter Estimations for a HfO2-Based Memristor

Department Theoretical Electrical Engineering, Technical University of Sofia, 1000 Sofia, Bulgaria
This paper is an extended version of the paper published in 8th International Conference on Modern Circuit and System Technologies on Electronics and Communications (MOCAST 2019), Thessaloniki, Greece, 13–15 May 2019.
Technologies 2020, 8(1), 16; https://doi.org/10.3390/technologies8010016
Received: 30 January 2020 / Revised: 28 February 2020 / Accepted: 3 March 2020 / Published: 7 March 2020
(This article belongs to the Special Issue MOCAST 2019: Modern Circuits and Systems Technologies on Electronics)
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 View Full-Text
Keywords: hafnium dioxide memristor; memristor–resistor synapse; nonlinear drift memristor model; parameter estimation; step function; window function hafnium dioxide memristor; memristor–resistor synapse; nonlinear drift memristor model; parameter estimation; step function; window function
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Mladenov, V. A New Simplified Model and Parameter Estimations for a HfO2-Based Memristor . Technologies 2020, 8, 16.

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