Topical Collection "Nanoarchitectonics of the Fourth Fundamental Electronic Component: Memristor, Meminductor and Memcapacitor"
Interests: neuromorphic computing; sensors; data storage; chaos identification
2. Intel Corporation, Hillsboro, OR 97124, USA
Interests: memristor; memcomputing; neuromorphic systems; biomimic smart system
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In 1971, Chua L.O. theorised a new classical electronic, memristor, as the fourth component complementing resistor, inductor, and capacitor. Despite the early physical phenomenon observed by Gibbons and Beadle in 1964, the development of the memristor did not receive much attention until the Hewlett-Packard Lab first elucidated that the ionic electron dynamic was the cause behind the phenomenon in 2008. Since then, its unique resistance tunability has received significant attention due to its potential application for data storage. However, we later found that besides its resistance (R), its inductance (I) and capacitance (C) can also be tuned! And thus, it is also called the meminductor and the memcapacitor, respectively. Any of its R, I or C can be reconfigured and used for different purposes. Moreover, the architectonic of this device is small, fast and low-powered. Henceforth, we foresee that the application of this technology is endless; memories, sensors, neuromorphic computing, random number generators, physically unclonable functions, advanced logic and adaptive/reconfigurable circuits are just a few examples from its long list of potential applications in both analogue and digital electronics.
This Topical Collection addresses the latest advances in the nanoarchitechtonics of the memristor, meminductor and memcapacitor. We invite scientists and engineers to contribute original research, reviews and perspective articles to inspire and shape the future directive towards the deployment of this fourth component for next-generation electronics. The scope of this collection includes, but is not limited to:
- Nanoscale fabrication, novel architecture and processes;
- Surface/interface and electrical characterisation, methodology and benchmarking;
- Multifunctional capabilities: in-memory computing, in-memory sensing and beyond;
- Integration and embedded techniques.
Dr. Firman Simanjuntak
Dr. Yao-Feng Chang
Dr. Sridhar Chandrasekaran
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- neuromorphic computing