Professor Leon Chua of the Electrical Engineering Department of UC Berkeley coined the term memristor while working on mathematical models in electrical engineering. He noted that resistors relate voltage to current (R=V/I), capacitors relate charge to voltage (C = Q/V), and inductors relate magnetic flux to current (L= Φ/I). What seemed to be missing was a component that related charge (Q) to magnetic flux (Φ). He called the missing component a “memristor,” because he worked out that the device should be able to retain memory of its resistance even without a bias. Professor Chua published his findings in a paper, “Memristor – The missing circuit element.” IEEE Trans. Circuit Theory CT-18, 507-519 (1971)
Think of a memristor as resistors with memory or “resistive RAM.” Memristors are not out of the lab yet but someday will revolutionize computing with a fundamental component that uses less energy, is smaller in size, and has a huge capacity for memory beyond anything that we have today. Memristors hold much promise as a combination resistor and memory device, however, because like resistors they can regulate the amount of current flowing through them and yet the memristor retains its last known resistance, even after power has been removed. If current flows in one direction, the resistance of the component increases. Charge flowing in the opposite direction causes the resistance to decrease. Removing the power (the voltage bias across the memristor) causes the memristor to stop changing resistor value but remain at the resistance that it was before the power was removed. The memristor can reveal how much voltage it had on it, what direction the current flowed through it, and how long the current was flowing through it.
HP Labs in 2008.[i] announced the first working memristor. Memristant properties have not been readily apparent in micro-scale electronics but were identified by HP Lab’s Dmitri Strukov, Gregory Snider, Duncan Stewart, and Stanley Williams at nanoscale, specifically in titanium dioxide (TIO2). From the lab research, Hewlett-Packard published a paper in Nature, titled “The Missing Memristor Found“, where they state that “memristance arises naturally in nanoscale systems in which solid-state electronic and ionic transport are coupled under an external bias voltage.
These results serve as the foundation for understanding a wide range of hysteretic current–voltage behavior observed in many nanoscale electronic devices that involve the motion of charged atomic or molecular species, in particular, certain titanium dioxide cross-point switches.”[ii] As HP Senior Fellow Stanley Williams states in the HP FAQ on memristors, “…the equations for the drift of oxygen vacancies in TiO2 and their influence on the electronic conduction in the material were also identical to our equivalent circuit model, and thus Chua’s memristor equations. From this, we could for the first time write down a formula for the memristance of a device in terms of material and geometrical properties of the device (just as the resistance is the resistivity of the material times the length divided by the cross-sectional area of the resistor). Our memristance formula immediately showed that the size of the most important term in the memristance gets larger the smaller the device – thus showing that it was not very important for micron-scale electronics but is becoming vital for nanoscale devices.”[iii]
In a press release on Oct 8, 2015, HP and SanDisk announced their collaboration on a new type of storage device within the Storage Class Memory (SCM) category of technology. The press release states, “The partnership will center around HP’s memristor technology and expertise and SanDisk’s non-volatile ReRAM memory technology….to create new enterprise-wide solutions for Memory-driven Computing.”[iv]
Memristors, although elusive to capture as a product, may someday become a reality in applications such as neural networks, which leads to serious advantages for applications in deep learning and advanced artificial intelligence. The beauty of memristors is that they do not conform to Moore’s Law and use less energy than conventional memory. In the original 2008 press release, HP states, “This scientific advancement could make it possible to develop computer systems that have memories that do not forget, do not need to be booted up, consume far less power and associate information like that of the human brain.”[v]
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