Tag Archives: memcomputing

Memristors, memcapacitors, and meminductors for faster computers

While some call memristors a fourth fundamental component alongside resistors, capacitors, and inductors (as mentioned in my June 26, 2014 posting which featured an update of sorts on memristors [scroll down about 80% of the way]), others view memristors as members of an emerging periodic table of circuit elements (as per my April 7, 2010 posting).

It seems scientists, Fabio Traversa, and his colleagues fall into the ‘periodic table of circuit elements’ camp. From Traversa’s  June 27, 2014 posting on nanotechweb.org,

Memristors, memcapacitors and meminductors may retain information even without a power source. Several applications of these devices have already been proposed, yet arguably one of the most appealing is ‘memcomputing’ – a brain-inspired computing paradigm utilizing the ability of emergent nanoscale devices to store and process information on the same physical platform.

A multidisciplinary team of researchers from the Autonomous University of Barcelona in Spain, the University of California San Diego and the University of South Carolina in the US, and the Polytechnic of Turin in Italy, suggest a realization of “memcomputing” based on nanoscale memcapacitors. They propose and analyse a major advancement in using memcapacitive systems (capacitors with memory), as central elements for Very Large Scale Integration (VLSI) circuits capable of storing and processing information on the same physical platform. They name this architecture Dynamic Computing Random Access Memory (DCRAM).

Using the standard configuration of a Dynamic Random Access Memory (DRAM) where the capacitors have been substituted with solid-state based memcapacitive systems, they show the possibility of performing WRITE, READ and polymorphic logic operations by only applying modulated voltage pulses to the memory cells. Being based on memcapacitors, the DCRAM expands very little energy per operation. It is a realistic memcomputing machine that overcomes the von Neumann bottleneck and clearly exhibits intrinsic parallelism and functional polymorphism.

Here’s a link to and a citation for the paper,

Dynamic computing random access memory by F L Traversa, F Bonani, Y V Pershin, and M Di Ventra. Nanotechnology Volume 25 Number 28  doi:10.1088/0957-4484/25/28/285201 Published 27 June 2014

This paper is behind a paywall.

Chaos, brains, and ferroelectrics: “We started to see things that should have been completely impossible …”

Given my interest in neuromorphic (mimicking the human brain) engineering, this work at the US Oak Ridge National Laboratories was guaranteed to catch my attention. From the Nov. 18, 2013 news item on Nanowerk,

Unexpected behavior in ferroelectric materials explored by researchers at the Department of Energy’s Oak Ridge National Laboratory supports a new approach to information storage and processing.

Ferroelectric materials are known for their ability to spontaneously switch polarization when an electric field is applied. Using a scanning probe microscope, the ORNL-led team took advantage of this property to draw areas of switched polarization called domains on the surface of a ferroelectric material. To the researchers’ surprise, when written in dense arrays, the domains began forming complex and unpredictable patterns on the material’s surface.

“When we reduced the distance between domains, we started to see things that should have been completely impossible,” said ORNL’s Anton Ievlev, …

The Nov. 18, 2013 Oak Ridge National Laboratory news release, which originated the news item, provides more details,

“All of a sudden, when we tried to draw a domain, it wouldn’t form, or it would form in an alternating pattern like a checkerboard.  At first glance, it didn’t make any sense. We thought that when a domain forms, it forms. It shouldn’t be dependent on surrounding domains.”  [said Ievlev]

After studying patterns of domain formation under varying conditions, the researchers realized the complex behavior could be explained through chaos theory. One domain would suppress the creation of a second domain nearby but facilitate the formation of one farther away — a precondition of chaotic behavior, says ORNL’s Sergei Kalinin, who led the study.

“Chaotic behavior is generally realized in time, not in space,” he said. ”An example is a dripping faucet: sometimes the droplets fall in a regular pattern, sometimes not, but it is a time-dependent process. To see chaotic behavior realized in space, as in our experiment, is highly unusual.”

Collaborator Yuriy Pershin of the University of South Carolina explains that the team’s system possesses key characteristics needed for memcomputing, an emergent computing paradigm in which information storage and processing occur on the same physical platform.

Memcomputing is basically how the human brain operates: [emphasis mine] Neurons and their connections–synapses–can store and process information in the same location,” Pershin said. “This experiment with ferroelectric domains demonstrates the possibility of memcomputing.”

Encoding information in the domain radius could allow researchers to create logic operations on a surface of ferroelectric material, thereby combining the locations of information storage and processing.

The researchers note that although the system in principle has a universal computing ability, much more work is required to design a commercially attractive all-electronic computing device based on the domain interaction effect.

“These studies also make us rethink the role of surface and electrochemical phenomena in ferroelectric materials, since the domain interactions are directly traced to the behavior of surface screening charges liberated during electrochemical reaction coupled to the switching process,” Kalinin said.

For anyone who’s interested in exploring this particular approach to mimicking the human brain, here’s a citation for and a link to the researchers’ paper,

Intermittency, quasiperiodicity and chaos in probe-induced ferroelectric domain switching by A. V. Ievlev, S. Jesse, A. N. Morozovska, E. Strelcov, E. A. Eliseev, Y. V. Pershin, A. Kumar, V. Ya. Shur, & S. V. Kalinin. Nature Physics (2013) doi:10.1038/nphys2796 Published online 17 November 2013

This paper is behind a paywall although it is possible to preview it for free via ReadCube Access.