Advanced Memristor Modeling: Memristor Circuits and Networks (Hardcover) (ISBN-13: 9783038971047)

The investigation of new memory schemes, neural networks, computer
systems and many other improved electronic devices is very important for
future generation's electronic circuits and for their widespread
application in all the areas of industry. In this aspect the analysis of new
efficient and advanced electronic elements and circuits is an essential field
of the highly developed electrical and electronic engineering. The
resistance-switching phenomenon, observed in many amorphous oxides has been
investigated since 1970 and it is a promising technology for constructing new
electronic memories. It has been established that such oxide materials have
the ability for changing their conductance in accordance to the applied
voltage and memorizing their state for a long-time interval. Similar
behaviour has been predicted for the memristor element by Leon Chua in 1971.
The memristor is proposed in accordance to symmetry considerations and the
relationships between the four basic electric quantities - electric current
i, voltage v, charge q and magnetic flux Ψ. The memristor is an essential
passive one-port element together with the resistor, inductor, and capacitor.
The Williams HP research group has made a link between resistive switching
devices, and the memristor proposed by Chua. A number of scientific papers
related to memristors and memristor devices have been issued and several
memristor models have been proposed. The memristor is a highly nonlinear
component. It relates the electric charge q and the flux linkage, expressed
as a time integral of the voltage. The memristor element has the important
capability for remembering the electric charge passed through its
cross-section and its respective resistance, when the electrical signals are
switched off. Due to its nano-scale dimensions, non-volatility and memorizing
properties, the memristor is a sound potential candidate for application in
computer high-density memories, artificial neural networks and in many other
electronic devices.