Leon Chua, the originator of the theory of the memristor, believes memristor will not be taught in undergraduate courses until it is widely adopted in industry for the simple reason that any circuit containing even only one memristor must be analyzed by nonlinear techniques.
All electronic textbooks have been teaching using the wrong variables–voltage and charge–explaining away inaccuracies as anomalies. What they should have been teaching is the relationship between changes in voltage, or flux, and charge.
The memristor is a discovery, and memristive phenomena will become ubiquitous in nano-electronic circuits.
If you understand the math behind memristors, you can create superior device models, such as for SPICE, which means you can design better or more realistic circuits,” elaborates Stan Williams. (Computational neuroscience blog Neurdon has a tutorial on modeling the HP memristor with SPICE.)
Sprintronic Memristors – researchers created new types of memristors that rely on the magnetic properties of electrons. A spin memristor can be more finely tuned and is more flexible than the HP memristor, which was based on the movement of ions in a material
Current flowing through a memristor can alter its electrical resistance, and it retains that altered state even after the current is turned off, making it a natural for nonvolatile memory. The memristor promises the introduction of much tinier circuits, instant-on computers, and the ability to mimic the function of neurons in the human brain.
Yiran Chen and Xiaobin Wang, researchers at disk-drive manufacturer Seagate Technology, in Bloomington, Minn., described three examples of possible magnetic memristors in IEEE Electron Device Letters . In one of the three, resistance is caused by the spin of electrons in one section of the device pointing in a different direction than those in another section, creating a ”domain wall,” a boundary between the two states. Electrons flowing into the device have a certain spin, which alters the magnetization state of the device. Changing the magnetization, in turn, moves the domain wall and changes the device’s resistance.
The different designs can be flipped between high- and low-resistance states at different rates, from picoseconds to microseconds, each preferable in different applications
By redesigning certain types of circuits to include memristors, HP Stan Williams expects to obtain the same function with fewer components, making the circuit itself less expensive and significantly decreasing its power consumption. In fact, he hopes to combine memristors with traditional circuit-design elements to produce a device that does computation in a non-Boolean fashion.
HP is working towards stateful logic using memristors , Stateful logic means that the ’state’ of the memristor acts as both the computer and the memory.
Williams acknowledges that memristors won’t completely supplant silicon logic gates. Because memristors can’t inject energy into a circuit, silicon transistors are needed to drive them. The good news, he says, is that a single operation in a silicon transistor can trigger computation in multiple memristors. He notes that a processor featuring a grid of memristors that operates parallel to a grid of silicon transistors might be two or three times as large as it would be if it only had the silicon. But because the number of simultaneous calculations achieved by the memristors is the square of the number of transistors, tripling a 1000-transistor chip’s size by adding memristors would yield a thousandfold improvement in computing power with a negligible increase in power drawn.
Memristance is simply charge-dependent resistance. If M(q(t)) is a constant, then we obtain Ohm’s Law R(t) = V(t)/ I(t). If M(q(t)) is nontrivial, however, the equation is not equivalent because q(t) and M(q(t)) will vary with time.
Brian Wang is a Futurist Thought Leader and a popular Science blogger with 1 million readers per month. His blog Nextbigfuture.com is ranked #1 Science News Blog. It covers many disruptive technology and trends including Space, Robotics, Artificial Intelligence, Medicine, Anti-aging Biotechnology, and Nanotechnology.
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