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ADVANCED MEMRISTOR MODELING IBD

MDPI AG
02 / 2019
9783038971047
Inglés

Sinopsis

The investigation of new memory schemes, neural networks, computerásystems and many other improved electronic devices is very important foráfuture generation&rsquo,s electronic circuits and for their widespreadáapplication in all the areas of industry. In this aspect the analysis of newáefficient and advanced electronic elements and circuits is an essential fieldáof the highly developed electrical and electronic engineering. Theáresistance-switching phenomenon, observed in many amorphous oxides has beenáinvestigated since 1970 and it is a promising technology for constructing newáelectronic memories. It has been established that such oxide materials haveáthe ability for changing their conductance in accordance to the appliedávoltage and memorizing their state for a long-time interval. Similarábehaviour has been predicted for the memristor element by Leon Chua in 1971.áThe memristor is proposed in accordance to symmetry considerations and theárelationships between the four basic electric quantities - electric currentái, voltage v, charge q and magnetic flux &Psi,. The memristor is an essentialápassive one-port element together with the resistor, inductor, and capacitor.áThe Williams HP research group has made a link between resistive switchingádevices, and the memristor proposed by Chua. A number of scientific papersárelated to memristors and memristor devices have been issued and severalámemristor models have been proposed. The memristor is a highly nonlinearácomponent. It relates the electric charge q and the flux linkage, expressedáas a time integral of the voltage. The memristor element has the importantácapability for remembering the electric charge passed through itsácross-section and its respective resistance, when the electrical signals areáswitched off. Due to its nano-scale dimensions, non-volatility and memorizingáproperties, the memristor is a sound potential candidate for application inácomputer high-density memories, artificial neural networks and in many otheráelectronic devices.