A New Simplified Model and Parameter Estimations for a HfO<sub>2</sub>-Based Memristor <xref rid="fn2-technologies-720271" ref-type="fn">†</xref>
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 w...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Technologies |
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| Online Access: | https://www.mdpi.com/2227-7080/8/1/16 |
| _version_ | 1818778106840743936 |
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| author | Valeri Mladenov |
| author_facet | Valeri Mladenov |
| author_sort | Valeri Mladenov |
| collection | DOAJ |
| description | 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<sub>2</sub> 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<sub>2</sub> 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 |
| first_indexed | 2024-12-18T11:39:27Z |
| format | Article |
| id | doaj.art-52048d50748447f18be3f56d92ffa9a7 |
| institution | Directory Open Access Journal |
| issn | 2227-7080 |
| language | English |
| last_indexed | 2024-12-18T11:39:27Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Technologies |
| spelling | doaj.art-52048d50748447f18be3f56d92ffa9a72022-12-21T21:09:26ZengMDPI AGTechnologies2227-70802020-03-01811610.3390/technologies8010016technologies8010016A New Simplified Model and Parameter Estimations for a HfO<sub>2</sub>-Based Memristor <xref rid="fn2-technologies-720271" ref-type="fn">†</xref>Valeri Mladenov0Department Theoretical Electrical Engineering, Technical University of Sofia, 1000 Sofia, BulgariaThe 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<sub>2</sub> 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<sub>2</sub> 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 synapseshttps://www.mdpi.com/2227-7080/8/1/16hafnium dioxide memristormemristor–resistor synapsenonlinear drift memristor modelparameter estimationstep functionwindow function |
| spellingShingle | Valeri Mladenov A New Simplified Model and Parameter Estimations for a HfO<sub>2</sub>-Based Memristor <xref rid="fn2-technologies-720271" ref-type="fn">†</xref> Technologies hafnium dioxide memristor memristor–resistor synapse nonlinear drift memristor model parameter estimation step function window function |
| title | A New Simplified Model and Parameter Estimations for a HfO<sub>2</sub>-Based Memristor <xref rid="fn2-technologies-720271" ref-type="fn">†</xref> |
| title_full | A New Simplified Model and Parameter Estimations for a HfO<sub>2</sub>-Based Memristor <xref rid="fn2-technologies-720271" ref-type="fn">†</xref> |
| title_fullStr | A New Simplified Model and Parameter Estimations for a HfO<sub>2</sub>-Based Memristor <xref rid="fn2-technologies-720271" ref-type="fn">†</xref> |
| title_full_unstemmed | A New Simplified Model and Parameter Estimations for a HfO<sub>2</sub>-Based Memristor <xref rid="fn2-technologies-720271" ref-type="fn">†</xref> |
| title_short | A New Simplified Model and Parameter Estimations for a HfO<sub>2</sub>-Based Memristor <xref rid="fn2-technologies-720271" ref-type="fn">†</xref> |
| title_sort | new simplified model and parameter estimations for a hfo sub 2 sub based memristor xref rid fn2 technologies 720271 ref type fn † xref |
| topic | hafnium dioxide memristor memristor–resistor synapse nonlinear drift memristor model parameter estimation step function window function |
| url | https://www.mdpi.com/2227-7080/8/1/16 |
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