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

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 HfO 2 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 HfO 2 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

Research and Development of Parameter Extraction Approaches for Memristor Models

“…The approximation of a single, experimentally derived volt-ampere characteristic is usually not very difficult and is usually done using computationally expensive full bruteforce methods in combination with simple gradient methods; for instance, in [14], where a combination of brute-force was used together with the stochastic gradient descent method. Another example of local and global approximation methods usage is presented in [16].…”

“…The third group comprises of methods for constructing an optimization problem by approximating the key characteristics of the device response or its volt-ampere characteristic. The main differences in the approaches of this group are related to the choice of a characteristic for approximation, the construction of the error functional, and methods of data preprocessing and approximation [14][15][16].…”