| Autor: |
Juan Bisquert, Agustín Bou, Antonio Guerrero, Enrique Hernández-Balaguera |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
APL Machine Learning, Vol 1, Iss 3, Pp 036101-036101-9 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2770-9019 |
| DOI: |
10.1063/5.0153289 |
| Popis: |
Memristor devices have been investigated for their properties of resistive modulation that can be used in data storage and brain-like computation elements as artificial synapses and neurons. Memristors are characterized by an onset of high current values under applied voltage that produces a transition to a low resistance state or successively to different stable states of increasing conductivity that implement synaptic weights. Here, we develop a nonlinear model to explain the variation with time of the voltage and the resistance and compare it to experimental results on ionic–electronic halide perovskite memristors. We find separate experimental signatures of the capacitive discharge and inductive current increase. We show that the capacitor produces an increase step of the resistance due to the influence of the series resistance. In contrast, the inductor feature associated with inverted hysteresis causes a decrease of the resistance, as observed experimentally. The chemical inductor feature dominates the potentiation effect in which the conductivity increases with the voltage stimulus. Our results enable a quantitative characterization of highly nonlinear electronic devices using a combination of techniques such as time transient decays and impedance spectroscopy. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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