A Short Introduction to Memristors
Anyone with basic knowledge in electrical engineering knows that there are four fundamental circuit variables: Current i, Voltage v, Charge q, and Flux φ. With these four parameters, there can be six possible combinations for relating them to each other. So far we have a complete understanding and control over five of these combinations in which three of them are passive two-terminal fundamental circuit elements, namely the resistor R, the capacitor C, and the inductor L.
Each of these components relates different circuit quantities to each other non-linearly; resistors relate voltage and current, capacitors relate voltage and charge and inductors relate flux and current. Missing from this list is a non-linear relationship between flux and charge.
No device was there to relate the charge and the magnetic flux until first theorized by Leon Chua in 1971 and introduced as the new circuit element called “memristor” [1]. In his paper, “Memristor — The missing circuit element”, he described how his memristor is characterized by a relationship between the charge and the flux-linkage. It took about 40 years for the memristor to be actually implemented in a physical form and the unique properties of great interest to researchers and neuroinformatics around the world.
1. Memristor definition
Memristor is a portmanteau of “memory resistor”. [2] It is a passive device with two terminals, where the magnetic flux is related to the amount of passed electric charge through the device. [5] Since memristor is not an active element, it can not store or generate any power. The symbol of a memristor is shown in the figure below.
2. Memristance
But what is interesting about memristors is their property called “memristance”. Memristance involves the dual utilization of resistance, which is the ability to impede the flow of electricity. Memristors have programmable resistance, meaning the level of resistance changes based on the direction of the current. If the power shuts down, the resistance of the memristor freezes. In some sense, the memristor “remembers” its last resistance (state). If the power turns back on again, the resistance of the memristor starts exactly from where it was turned off. This important property of memristor allows wide applications of memristors in many fields.
3. Applications
Memristors with their unique properties can be used in a numerous range of applications. Since May 2008, when HP Labs published their paper in Nature, introducing the discovery of memristor, a huge wave started in the research and engineering communities around the world to find applications for this new device. Some of the applications like the application of memristors in Non-volatile memory or applications of crossbar architectures are really promising and really close to reality, but the most interesting applications lie in the field of neuroinformatics, BCI, and neuroprosthetics.
3.1 Memristors as an artificial synapses
Memristors remember their states exactly like synapses in our biological brains and can adjust based on input. “… I realized that synapses were memristors. The ion channel was the missing link that I was looking for and it already existed in nature…”. This was what Leon Chua said about the analogy example of a memristor that the function of a memristor is quite similar to that of a synapse.
But these artificial synapses need to follow biological learning rules, like Spike Timing Dependent Plasticity– neurons that fire together, wire together. This is where memristors play an important role. Memristors can be easily programmed to have both long and short-term plasticity. Like synapses, they remember their state and can adjust based on input.
In 2008, DARPA launched SyNAPSE (Systems of Neuromorphic Adaptive Plastic Scalable Electronics) and the program was taken by HLR Laboratories, HP and IBM. They managed to develop a nanoscale electronic synaptic component that could perform strength adaptation of the connection between two neurons similar to the way is done on the brain (Hebbian learning). The launch of this program coincided the discovery of the physical realization of the memristor by HP Labs [4].
Memristors, functioning as artificial synapses open up a new world of connection between man and machine and they are working as a way to abstract ideas about how the brain works. This is important because when building an interface between neurons and silicon, we’re going to need a Rosetta stone: a translating interface, a layer of abstraction that permits easy discourse between these two very different environments.
Maybe the most interesting applications lie in the field of neuroinformatics, BCI, and neuroprosthetics, those are not the only applications. The first application of memristor that is more close to reality and will possibly be seen in widespread use in the near future is non-volatile memristor memories, crossbar structure architectures, and hybrid chips, that contained a combination of both transistors and memristors.
In 2012, Mazumder, Kang, and Waser published an article in a special issue of the Proceedings of the IEEE about memristor devices, models, and applications. They presented a taxonomy of possible memristor applications that are shown in the figure below.
4. Conclusion
Attempts of applying memristor in a circuit architectures and use its features and unique properties, or tweaking the old configurations by adding a memristor or utilizing the new properties of the memristor and develop a new architecture — as a result, a wide range of articles and papers were published in the last years and now we are closer the real applications not only in academic and research fields but we are closer and closer to the applications in the industry and everyday use.
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References
- L. Chua, “Memristor-The missing circuit element,” IEEE Transactions on Circuit Theory, vol. 18, no. 5, pp.507– 519, 1971.
- Vahid Keshmiri, A Study of Memristor Models and Applications, LiTH-ISY-EX — 11/4455 — SE, Linköping 2014
- Jessica Hall, Oct 14, 2016, Scientists develop a memristor that can be conditioned just like a real synapse.
- M. Versace and B. Chandler, “The Brain of a New Machine,” IEEE Spectrum, vol. 47, no. 12, pp. 30–37, 2010.
