Stan Williams, HP Senior Fellow & Dir. Memristor Research Group
We have been working on a project at HP Labs to explore the possibility of using “locally-active memristors” as the basis for extremely low-power transistorless computation. We first analyzed the thermally-induced first order phase transition from a Mott insulator to a highly conducting state in a family of correlated-electron transition-metal oxides, such as VO2 and NbO2. The current-voltage characteristic of a simple cross-point device that has a thin film of such an oxide sandwiched between two metal electrodes displays a current-controlled or ‘S’-type negative differential resistance (NDR) caused by Joule self-heating if the ambient temperature is below the metal-insulator transition (MIT). We derived simple analytical equations for the behavior these devices  that quantitatively reproduce their experimentally measured electrical characteristics with only one or two fitting parameters, and found that the resulting dynamical model was mathematically equivalent to the “memristive system” formulation of Leon Chua and Steve Kang ; we thus call these devices “Mott Memristors”. Moreover, these devices display the property of “local activity”; because of the NDR, they are capable of injecting energy into a circuit (converting DC to AC electrical power) over a limited biasing range. We built and demonstrated Pearson-Anson oscillators with no inductors based on a parallel circuit of one Mott memristor and one capacitor, and were able to quantitatively reproduce the dynamical behavior of the circuit, including the subnanosecond and subpicoJoule memristor switching time and energy, using SPICE. We then built a neuristor, an active subcircuit originally proposed by Hewitt Crane  in 1960 without an experimental implementation, using two Mott memristors and two capacitors. The neuristor electronically emulates the Hodgkin-Huxley model of the axon action potential of a neuron, which has been recently shown by Chua et al.  to be a circuit with two parallel ionic memristors, and we show experimental results that are quantitatively matched by SPICE simulations of the output bifurcation, signal gain and spiking behavior in our inorganic and electronic circuit  that are believed to be the basis for computation in biological systems. Finally, through SPICE, we demonstrate that spiking neuristors are capable of Boolean logic and Turing complete computation by designing and simulating a one dimensional cellular automaton  based on ‘Rule 137′.
1. Pickett, M. D. and Williams, R. S. Sub-100 femtoJoule and sub-nanosecond thermally-driven threshold switching,” Nanotechnology 23, art. #215202 (2012).
2. Chua, L. & Kang, S. Memristive devices and systems. Proceedings of the IEEE 64, 209-223 (1976).
3. Crane, H. D. The Neuristor. IRE Transactions on Electronic Computers EC-9, 370-371 (1960).
4. Chua, L., Sbitnev, V. & Kim, H. Hodgkin-Huxley axon is made of memristors. International Journal of
Bifurcation and Chaos 22, 1-48 (2012).
5. Pickett, M. D., Medeiros-Ribeiro, G. and Williams, R. S. A scalable neuristor built with Mott memristors, Nature Materials 12, 114-117 (2013).
6. Pickett, M. D. and Williams, R. S. Phase transitions enable computational universality in neuristor-based cellular automata, Nanotechnology 24, 384002 (2013).
About Stan Williams:
Hosted by: The Computer Engineering Program and co-sponsored by the Institute for Energy Efficiency