Advanced Memristor Modeling: Memristor Circuits and Networks - Tapa dura

Sinopsis

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.

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