Tag Archives: memristors

Memristive-like qualities with pectin

As the drive to create a synthetic neuronal network, as powered by memristors, continues, scientists are investigating pectin. From a Nov. 11, 2016 news item on ScienceDaily,

Most of us know pectin as a key ingredient for making delicious jellies and jams, not as a component for a complex hybrid device that links biological and electronic systems. But a team of Italian scientists have built on previous work in this field using pectin with a high degree of methylation as the medium to create a new architecture of hybrid device with a double-layered polyelectrolyte that alone drives memristive behavior.

A Nov. 11, 2016 American Institute of Physics news release on EurekAlert, which originated the news item, defines memristors and describes the research,

A memristive device can be thought of as a synapse analogue, a device that has a memory. Simply stated, its behavior in a certain moment depends on its previous activity, similar to the way information in the human brain is transmitted from one neuron to another.

In an article published this week in AIP Advances, from AIP Publishing, the team explains the creation of the hybrid device. “In this research, we applied materials generally used in the pharmaceutical and food industries in our electrochemical devices,” said Angelica Cifarelli, a doctoral candidate at the University of Parma in Italy. “The idea of using the ‘buffering’ capability of these biocompatible materials as solid polyelectrolyte is completely innovative and our work is the first time that these bio-polymers have been used in devices based on organic polymers and in a memristive device.”

Memristors can provide a bridge for interfacing electronic circuits with nervous systems, moving us closer to realization of a double-layer perceptron, an element that can perform classification functions after an appropriate learning procedure. The main difficulty the research team faced was understanding the complex electrochemical interplay that is the basis for the memristive behavior, which would give them the means to control it. The team addressed this challenge by using commercial polymers, and modifying their electrochemical properties at the macroscopic level. The most surprising result was that it was possible to check the electrochemical response of the device by changing the formulation of gels acting as polyelectrolytes, allowing study of the ionic exchanges relating to the biological object, which activates the electrochemical response of the conductive polymer.

“Our developments open the way to make compatible polyaniline based devices with an interface that should be naturally, biologically and electrochemically compatible and functional,” said Cifarelli. The next steps are interfacing the memristor network with other living beings, for example, plants and ultimately the realization of hybrid systems that can “learn” and perform logic/classification functions.

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

Polysaccarides-based gels and solid-state electronic devices with memresistive properties: Synergy between polyaniline electrochemistry and biology by Angelica Cifarelli, Tatiana Berzina, Antonella Parisini, Victor Erokhin, and Salvatore Iannotta. AIP Advances 6, 111302 (2016); http://dx.doi.org/10.1063/1.4966559 Published Nov. 8, 2016

This paper appears to be open access.

The memristor as computing device

An Oct. 27, 2016 news item on Nanowerk both builds on the Richard Feynman legend/myth and announces some new work with memristors,

In 1959 renowned physicist Richard Feynman, in his talk “[There’s] Plenty of Room at the Bottom,” spoke of a future in which tiny machines could perform huge feats. Like many forward-looking concepts, his molecule and atom-sized world remained for years in the realm of science fiction.

And then, scientists and other creative thinkers began to realize Feynman’s nanotechnological visions.

In the spirit of Feynman’s insight, and in response to the challenges he issued as a way to inspire scientific and engineering creativity, electrical and computer engineers at UC Santa Barbara [University of California at Santa Barbara, UCSB] have developed a design for a functional nanoscale computing device. The concept involves a dense, three-dimensional circuit operating on an unconventional type of logic that could, theoretically, be packed into a block no bigger than 50 nanometers on any side.

A figure depicting the structure of stacked memristors with dimensions that could satisfy the Feynman Grand Challenge Photo Credit: Courtesy Image

A figure depicting the structure of stacked memristors with dimensions that could satisfy the Feynman Grand Challenge. Photo Credit: Courtesy Image

An Oct. 27, 2016 UCSB news release (also on EurekAlert) by Sonia Fernandez, which originated the news item, offers a basic explanation of the work (useful for anyone unfamiliar with memristors) along with more detail,

“Novel computing paradigms are needed to keep up with the demand for faster, smaller and more energy-efficient devices,” said Gina Adam, postdoctoral researcher at UCSB’s Department of Computer Science and lead author of the paper “Optimized stateful material implication logic for three dimensional data manipulation,” published in the journal Nano Research. “In a regular computer, data processing and memory storage are separated, which slows down computation. Processing data directly inside a three-dimensional memory structure would allow more data to be stored and processed much faster.”

While efforts to shrink computing devices have been ongoing for decades — in fact, Feynman’s challenges as he presented them in his 1959 talk have been met — scientists and engineers continue to carve out room at the bottom for even more advanced nanotechnology. A nanoscale 8-bit adder operating in 50-by-50-by-50 nanometer dimension, put forth as part of the current Feynman Grand Prize challenge by the Foresight Institute, has not yet been achieved. However, the continuing development and fabrication of progressively smaller components is bringing this virus-sized computing device closer to reality, said Dmitri Strukov, a UCSB professor of computer science.

“Our contribution is that we improved the specific features of that logic and designed it so it could be built in three dimensions,” he said.

Key to this development is the use of a logic system called material implication logic combined with memristors — circuit elements whose resistance depends on the most recent charges and the directions of those currents that have flowed through them. Unlike the conventional computing logic and circuitry found in our present computers and other devices, in this form of computing, logic operation and information storage happen simultaneously and locally. This greatly reduces the need for components and space typically used to perform logic operations and to move data back and forth between operation and memory storage. The result of the computation is immediately stored in a memory element, which prevents data loss in the event of power outages — a critical function in autonomous systems such as robotics.

In addition, the researchers reconfigured the traditionally two-dimensional architecture of the memristor into a three-dimensional block, which could then be stacked and packed into the space required to meet the Feynman Grand Prize Challenge.

“Previous groups show that individual blocks can be scaled to very small dimensions, let’s say 10-by-10 nanometers,” said Strukov, who worked at technology company Hewlett-Packard’s labs when they ramped up development of memristors and material implication logic. By applying those results to his group’s developments, he said, the challenge could easily be met.

The tiny memristors are being heavily researched in academia and in industry for their promising uses in memory storage and neuromorphic computing. While implementations of material implication logic are rather exotic and not yet mainstream, uses for it could pop up any time, particularly in energy scarce systems such as robotics and medical implants.

“Since this technology is still new, more research is needed to increase its reliability and lifetime and to demonstrate large scale three-dimensional circuits tightly packed in tens or hundreds of layers,” Adam said.

HP Labs, mentioned in the news release, announced the ‘discovery’ of memristors and subsequent application of engineering control in two papers in 2008.

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

Optimized stateful material implication logic for threedimensional data manipulation by Gina C. Adam, Brian D. Hoskins, Mirko Prezioso, &Dmitri B. Strukov. Nano Res. (2016) pp. 1 – 10. doi:10.1007/s12274-016-1260-1 First Online: 29 September 2016

This paper is behind a paywall.

You can find many articles about memristors here by using either ‘memristor’ or ‘memristors’ as your search term.

X-rays reveal memristor workings

A June 14, 2016 news item on ScienceDaily focuses on memristors. (It’s been about two months since my last memristor posting on April 22, 2016 regarding electronic synapses and neural networks). This piece announces new insight into how memristors function at the atomic scale,

In experiments at two Department of Energy national labs — SLAC National Accelerator Laboratory and Lawrence Berkeley National Laboratory — scientists at Hewlett Packard Enterprise (HPE) [also referred to as HP Labs or Hewlett Packard Laboratories] have experimentally confirmed critical aspects of how a new type of microelectronic device, the memristor, works at an atomic scale.

This result is an important step in designing these solid-state devices for use in future computer memories that operate much faster, last longer and use less energy than today’s flash memory. …

“We need information like this to be able to design memristors that will succeed commercially,” said Suhas Kumar, an HPE scientist and first author on the group’s technical paper.

A June 13, 2016 SLAC news release, which originated the news item, offers a brief history according to HPE and provides details about the latest work,

The memristor was proposed theoretically [by Dr. Leon Chua] in 1971 as the fourth basic electrical device element alongside the resistor, capacitor and inductor. At its heart is a tiny piece of a transition metal oxide sandwiched between two electrodes. Applying a positive or negative voltage pulse dramatically increases or decreases the memristor’s electrical resistance. This behavior makes it suitable for use as a “non-volatile” computer memory that, like flash memory, can retain its state without being refreshed with additional power.

Over the past decade, an HPE group led by senior fellow R. Stanley Williams has explored memristor designs, materials and behavior in detail. Since 2009 they have used intense synchrotron X-rays to reveal the movements of atoms in memristors during switching. Despite advances in understanding the nature of this switching, critical details that would be important in designing commercially successful circuits  remained controversial. For example, the forces that move the atoms, resulting in dramatic resistance changes during switching, remain under debate.

In recent years, the group examined memristors made with oxides of titanium, tantalum and vanadium. Initial experiments revealed that switching in the tantalum oxide devices could be controlled most easily, so it was chosen for further exploration at two DOE Office of Science User Facilities – SLAC’s Stanford Synchrotron Radiation Lightsource (SSRL) and Berkeley Lab’s Advanced Light Source (ALS).

At ALS, the HPE researchers mapped the positions of oxygen atoms before and after switching. For this, they used a scanning transmission X-ray microscope and an apparatus they built to precisely control the position of their sample and the timing and intensity of the 500-electronvolt ALS X-rays, which were tuned to see oxygen.

The experiments revealed that even weak voltage pulses create a thin conductive path through the memristor. During the pulse the path heats up, which creates a force that pushes oxygen atoms away from the path, making it even more conductive. Reversing the voltage pulse resets the memristor by sucking some of oxygen atoms back into the conducting path, thereby increasing the device’s resistance. The memristor’s resistance changes between 10-fold and 1 million-fold, depending on operating parameters like the voltage-pulse amplitude. This resistance change is dramatic enough to exploit commercially.

To be sure of their conclusion, the researchers also needed to understand if the tantalum atoms were moving along with the oxygen during switching. Imaging tantalum required higher-energy, 10,000-electronvolt X-rays, which they obtained at SSRL’s Beam Line 6-2. In a single session there, they determined that the tantalum remained stationary.

“That sealed the deal, convincing us that our hypothesis was correct,” said HPE scientist Catherine Graves, who had worked at SSRL as a Stanford graduate student. She added that discussions with SLAC experts were critical in guiding the HPE team toward the X-ray techniques that would allow them to see the tantalum accurately.

Kumar said the most promising aspect of the tantalum oxide results was that the scientists saw no degradation in switching over more than a billion voltage pulses of a magnitude suitable for commercial use. He added that this knowledge helped his group build memristors that lasted nearly a billion switching cycles, about a thousand-fold improvement.

“This is much longer endurance than is possible with today’s flash memory devices,” Kumar said. “In addition, we also used much higher voltage pulses to accelerate and observe memristor failures, which is also important in understanding how these devices work. Failures occurred when oxygen atoms were forced so far away that they did not return to their initial positions.”

Beyond memory chips, Kumar says memristors’ rapid switching speed and small size could make them suitable for use in logic circuits. Additional memristor characteristics may also be beneficial in the emerging class of brain-inspired neuromorphic computing circuits.

“Transistors are big and bulky compared to memristors,” he said. “Memristors are also much better suited for creating the neuron-like voltage spikes that characterize neuromorphic circuits.”

The researchers have provided an animation illustrating how memristors can fail,

This animation shows how millions of high-voltage switching cycles can cause memristors to fail. The high-voltage switching eventually creates regions that are permanently rich (blue pits) or deficient (red peaks) in oxygen and cannot be switched back. Switching at lower voltages that would be suitable for commercial devices did not show this performance degradation. These observations allowed the researchers to develop materials processing and operating conditions that improved the memristors’ endurance by nearly a thousand times. (Suhas Kumar) Courtesy: SLAC

This animation shows how millions of high-voltage switching cycles can cause memristors to fail. The high-voltage switching eventually creates regions that are permanently rich (blue pits) or deficient (red peaks) in oxygen and cannot be switched back. Switching at lower voltages that would be suitable for commercial devices did not show this performance degradation. These observations allowed the researchers to develop materials processing and operating conditions that improved the memristors’ endurance by nearly a thousand times. (Suhas Kumar) Courtesy: SLAC

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

Direct Observation of Localized Radial Oxygen Migration in Functioning Tantalum Oxide Memristors by Suhas Kumar, Catherine E. Graves, John Paul Strachan, Emmanuelle Merced Grafals, Arthur L. David Kilcoyne3, Tolek Tyliszczak, Johanna Nelson Weker, Yoshio Nishi, and R. Stanley Williams. Advanced Materials, First published: 2 February 2016; Print: Volume 28, Issue 14 April 13, 2016 Pages 2772–2776 DOI: 10.1002/adma.201505435

This paper is behind a paywall.

Some of the ‘memristor story’ is contested and you can find a brief overview of the discussion in this Wikipedia memristor entry in the section on ‘definition and criticism’. There is also a history of the memristor which dates back to the 19th century featured in my May 22, 2012 posting.

Memristor-based electronic synapses for neural networks

Caption: Neuron connections in biological neural networks. Credit: MIPT press office

Caption: Neuron connections in biological neural networks. Credit: MIPT press office

Russian scientists have recently published a paper about neural networks and electronic synapses based on ‘thin film’ memristors according to an April 19, 2016 news item on Nanowerk,

A team of scientists from the Moscow Institute of Physics and Technology (MIPT) have created prototypes of “electronic synapses” based on ultra-thin films of hafnium oxide (HfO2). These prototypes could potentially be used in fundamentally new computing systems.

An April 20, 2016 MIPT press release (also on EurekAlert), which originated the news item (the date inconsistency likely due to timezone differences) explains the connection between thin films and memristors,

The group of researchers from MIPT have made HfO2-based memristors measuring just 40×40 nm2. The nanostructures they built exhibit properties similar to biological synapses. Using newly developed technology, the memristors were integrated in matrices: in the future this technology may be used to design computers that function similar to biological neural networks.

Memristors (resistors with memory) are devices that are able to change their state (conductivity) depending on the charge passing through them, and they therefore have a memory of their “history”. In this study, the scientists used devices based on thin-film hafnium oxide, a material that is already used in the production of modern processors. This means that this new lab technology could, if required, easily be used in industrial processes.

“In a simpler version, memristors are promising binary non-volatile memory cells, in which information is written by switching the electric resistance – from high to low and back again. What we are trying to demonstrate are much more complex functions of memristors – that they behave similar to biological synapses,” said Yury Matveyev, the corresponding author of the paper, and senior researcher of MIPT’s Laboratory of Functional Materials and Devices for Nanoelectronics, commenting on the study.

The press release offers a description of biological synapses and their relationship to learning and memory,

A synapse is point of connection between neurons, the main function of which is to transmit a signal (a spike – a particular type of signal, see fig. 2) from one neuron to another. Each neuron may have thousands of synapses, i.e. connect with a large number of other neurons. This means that information can be processed in parallel, rather than sequentially (as in modern computers). This is the reason why “living” neural networks are so immensely effective both in terms of speed and energy consumption in solving large range of tasks, such as image / voice recognition, etc.

Over time, synapses may change their “weight”, i.e. their ability to transmit a signal. This property is believed to be the key to understanding the learning and memory functions of thebrain.

From the physical point of view, synaptic “memory” and “learning” in the brain can be interpreted as follows: the neural connection possesses a certain “conductivity”, which is determined by the previous “history” of signals that have passed through the connection. If a synapse transmits a signal from one neuron to another, we can say that it has high “conductivity”, and if it does not, we say it has low “conductivity”. However, synapses do not simply function in on/off mode; they can have any intermediate “weight” (intermediate conductivity value). Accordingly, if we want to simulate them using certain devices, these devices will also have to have analogous characteristics.

The researchers have provided an illustration of a biological synapse,

Fig.2 The type of electrical signal transmitted by neurons (a “spike”). The red lines are various other biological signals, the black line is the averaged signal. Source: MIPT press office

Fig.2 The type of electrical signal transmitted by neurons (a “spike”). The red lines are various other biological signals, the black line is the averaged signal. Source: MIPT press office

Now, the press release ties the memristor information together with the biological synapse information to describe the new work at the MIPT,

As in a biological synapse, the value of the electrical conductivity of a memristor is the result of its previous “life” – from the moment it was made.

There is a number of physical effects that can be exploited to design memristors. In this study, the authors used devices based on ultrathin-film hafnium oxide, which exhibit the effect of soft (reversible) electrical breakdown under an applied external electric field. Most often, these devices use only two different states encoding logic zero and one. However, in order to simulate biological synapses, a continuous spectrum of conductivities had to be used in the devices.

“The detailed physical mechanism behind the function of the memristors in question is still debated. However, the qualitative model is as follows: in the metal–ultrathin oxide–metal structure, charged point defects, such as vacancies of oxygen atoms, are formed and move around in the oxide layer when exposed to an electric field. It is these defects that are responsible for the reversible change in the conductivity of the oxide layer,” says the co-author of the paper and researcher of MIPT’s Laboratory of Functional Materials and Devices for Nanoelectronics, Sergey Zakharchenko.

The authors used the newly developed “analogue” memristors to model various learning mechanisms (“plasticity”) of biological synapses. In particular, this involved functions such as long-term potentiation (LTP) or long-term depression (LTD) of a connection between two neurons. It is generally accepted that these functions are the underlying mechanisms of  memory in the brain.

The authors also succeeded in demonstrating a more complex mechanism – spike-timing-dependent plasticity, i.e. the dependence of the value of the connection between neurons on the relative time taken for them to be “triggered”. It had previously been shown that this mechanism is responsible for associative learning – the ability of the brain to find connections between different events.

To demonstrate this function in their memristor devices, the authors purposefully used an electric signal which reproduced, as far as possible, the signals in living neurons, and they obtained a dependency very similar to those observed in living synapses (see fig. 3).

Fig.3. The change in conductivity of memristors depending on the temporal separation between "spikes"(rigth) and thr change in potential of the neuron connections in biological neural networks. Source: MIPT press office

Fig.3. The change in conductivity of memristors depending on the temporal separation between “spikes”(rigth) and thr change in potential of the neuron connections in biological neural networks. Source: MIPT press office

These results allowed the authors to confirm that the elements that they had developed could be considered a prototype of the “electronic synapse”, which could be used as a basis for the hardware implementation of artificial neural networks.

“We have created a baseline matrix of nanoscale memristors demonstrating the properties of biological synapses. Thanks to this research, we are now one step closer to building an artificial neural network. It may only be the very simplest of networks, but it is nevertheless a hardware prototype,” said the head of MIPT’s Laboratory of Functional Materials and Devices for Nanoelectronics, Andrey Zenkevich.

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

Crossbar Nanoscale HfO2-Based Electronic Synapses by Yury Matveyev, Roman Kirtaev, Alena Fetisova, Sergey Zakharchenko, Dmitry Negrov and Andrey Zenkevich. Nanoscale Research Letters201611:147 DOI: 10.1186/s11671-016-1360-6

Published: 15 March 2016

This is an open access paper.

Indian researchers establish a multiplex number to identify efficiency of multilevel resistive switching devices

There’s a Feb. 1, 2016 Nanowerk Spotlight article by Dr. Abhay Sagade of Cambridge University (UK) about defining efficiency in memristive devices,

In a recent study, researchers at the Jawaharlal Nehru Centre for Advanced Scientific Research (JNCASR), Bangalore, India, have defined a new figure-of-merit to identify the efficiency of resistive switching devices with multiple memory states. The research was carried out in collaboration with the Indian Institute of Technology Madras (IITM), Chennai, and financially supported by Department of Science and Technology, New Delhi.

The scientists identified the versatility of palladium oxide (PdO) as a novel resistive switching material for use in resistive memory devices. Due to the availability to switch multiple redox states in the PdO system, researchers have controlled it by applying different amplitudes of voltage pulses.

To date, many materials have shown multiple memory states but there have been no efforts to define the ability of the fabricated device to switch between all possible memory states.

In this present report, the authors have defined the efficacy in a term coined as “multiplex number (M)” to quantify the performance of a multiple memory switching device:

For the PdO MRS device with five memory states, the multiplex number is found to be 5.7, which translates to 70% efficiency in switching. This is the highest value of M observed in any multiple memory device.

As multilevel resistive switching devices are expected to have great significance in futuristic brain-like memory devices [neuromorphic engineering products], the definition of their efficiency will provide a boost to the field. The number M will assist researches as well as technologist in classifying and deciding the true merit of their memory devices.

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

Defining Switching Efficiency of Multilevel Resistive Memory with PdO as an Example by K. D. M. Rao, Abhay A. Sagade, Robin John, T. Pradeep and G. U. Kulkarni. Advanced Electronic Materials Volume 2, Issue 2, February 2016 DOI: 10.1002/aelm.201500286

© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

This article is behind a paywall.

Memristor shakeup

New discoveries suggest that memristors do not function as was previously theorized. (For anyone who wants a memristor description, there’s this Wikipedia entry.) From an Oct. 13, 2015 posting by Alexander Hellemans for the Nanoclast blog (on the IEEE [Institute for Electrical and Electronics Engineers]), Note: Links have been removed,

What’s going to replace flash? The R&D arms of several companies including Hewlett Packard, Intel, and Samsung think the answer might be memristors (also called resistive RAM, ReRAM, or RRAM). These devices have a chance at unseating the non-volatile memory champion because, they use little energy, are very fast, and retain data without requiring power. However, new research indicates that they don’t work in quite the way we thought they do.

The fundamental mechanism at the heart of how a memristor works is something called an “imperfect point contact,” which was predicted in 1971, long before anybody had built working devices. When voltage is applied to a memristor cell, it reduces the resistance across the device. This change in resistance can be read out by applying another, smaller voltage. By inverting the voltage, the resistance of the device is returned to its initial value, that is, the stored information is erased.

Over the last decade researchers have produced two commercially promising types of memristors: electrochemical metallization memory (ECM) cells, and valence change mechanism memory (VCM) cells.

Now international research teams lead by Ilia Valov at the Peter Grünberg Institute in Jülich, Germany, report in Nature Nanotechnology and Advanced Materials that they have identified new processes that erase many of the differences between EMC and VCM cells.

Valov and coworkers in Germany, Japan, Korea, Greece, and the United States started investigating memristors that had a tantalum oxide electrolyte and an active tantalum electrode. “Our studies show that these two types of switching mechanisms in fact can be bridged, and we don’t have a purely oxygen type of switching as was believed, but that also positive [metal] ions, originating from the active electrode, are mobile,” explains Valov.

Here are links to and citations for both papers,

Graphene-Modified Interface Controls Transition from VCM to ECM Switching Modes in Ta/TaOx Based Memristive Devices by Michael Lübben, Panagiotis Karakolis, Vassilios Ioannou-Sougleridis, Pascal Normand, Pangiotis Dimitrakis, & Ilia Valov. Advanced Materials DOI: 10.1002/adma.201502574 First published: 10 September 2015

Nanoscale cation motion in TaOx, HfOx and TiOx memristive systems by Anja Wedig, Michael Luebben, Deok-Yong Cho, Marco Moors, Katharina Skaja, Vikas Rana, Tsuyoshi Hasegawa, Kiran K. Adepalli, Bilge Yildiz, Rainer Waser, & Ilia Valov. Nature Nanotechnology (2015) doi:10.1038/nnano.2015.221 Published online 28 September 2015

Both papers are behind paywalls.

Computer chips derived in a Darwinian environment

Courtesy: University of Twente

Courtesy: University of Twente

If that ‘computer chip’ looks a brain to you, good, since that’s what the image is intended to illustrate assuming I’ve correctly understood the Sept. 21, 2015 news item on Nanowerk (Note: A link has been removed),

Researchers of the MESA+ Institute for Nanotechnology and the CTIT Institute for ICT Research at the University of Twente in The Netherlands have demonstrated working electronic circuits that have been produced in a radically new way, using methods that resemble Darwinian evolution. The size of these circuits is comparable to the size of their conventional counterparts, but they are much closer to natural networks like the human brain. The findings promise a new generation of powerful, energy-efficient electronics, and have been published in the leading British journal Nature Nanotechnology (“Evolution of a Designless Nanoparticle Network into Reconfigurable Boolean Logic”).

A Sept. 21, 2015 University of Twente press release, which originated the news item, explains why and how they have decided to mimic nature to produce computer chips,

One of the greatest successes of the 20th century has been the development of digital computers. During the last decades these computers have become more and more powerful by integrating ever smaller components on silicon chips. However, it is becoming increasingly hard and extremely expensive to continue this miniaturisation. Current transistors consist of only a handful of atoms. It is a major challenge to produce chips in which the millions of transistors have the same characteristics, and thus to make the chips operate properly. Another drawback is that their energy consumption is reaching unacceptable levels. It is obvious that one has to look for alternative directions, and it is interesting to see what we can learn from nature. Natural evolution has led to powerful ‘computers’ like the human brain, which can solve complex problems in an energy-efficient way. Nature exploits complex networks that can execute many tasks in parallel.

Moving away from designed circuits

The approach of the researchers at the University of Twente is based on methods that resemble those found in Nature. They have used networks of gold nanoparticles for the execution of essential computational tasks. Contrary to conventional electronics, they have moved away from designed circuits. By using ‘designless’ systems, costly design mistakes are avoided. The computational power of their networks is enabled by applying artificial evolution. This evolution takes less than an hour, rather than millions of years. By applying electrical signals, one and the same network can be configured into 16 different logical gates. The evolutionary approach works around – or can even take advantage of – possible material defects that can be fatal in conventional electronics.

Powerful and energy-efficient

It is the first time that scientists have succeeded in this way in realizing robust electronics with dimensions that can compete with commercial technology. According to prof. Wilfred van der Wiel, the realized circuits currently still have limited computing power. “But with this research we have delivered proof of principle: demonstrated that our approach works in practice. By scaling up the system, real added value will be produced in the future. Take for example the efforts to recognize patterns, such as with face recognition. This is very difficult for a regular computer, while humans and possibly also our circuits can do this much better.”  Another important advantage may be that this type of circuitry uses much less energy, both in the production, and during use. The researchers anticipate a wide range of applications, for example in portable electronics and in the medical world.

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

Evolution of a designless nanoparticle network into reconfigurable Boolean logic by S. K. Bose, C. P. Lawrence, Z. Liu, K. S. Makarenko, R. M. J. van Damme, H. J. Broersma, & W. G. van der Wiel. Nature Nanotechnology (2015) doi:10.1038/nnano.2015.207 Published online 21 September 2015

This paper is behind a paywall.

Final comment, this research, especially with the reference to facial recognition, reminds me of memristors and neuromorphic engineering. I have written many times on this topic and you should be able to find most of the material by using ‘memristor’ as your search term in the blog search engine. For the mildly curious, here are links to two recent memristor articles, Knowm (sounds like gnome?) A memristor company with a commercially available product in a Sept. 10, 2015 posting and Memristor, memristor, you are popular in a May 15, 2015 posting.

Knowm (sounds like gnome?) A memristor company with a commercially available product

German garden gnome Date: 11 August 2006 (original upload date) Source:Transferred from en.wikipedia to Commons. Author: Colibri1968 at English Wikipedia

German garden gnome
Date 11 August 2006 (original upload date)
Source Transferred from en.wikipedia to Commons.
Author Colibri1968 at English Wikipedia

I thought the ‘gnome’knowm’ homonym or, more precisely, homophone, might be an amusing way to lead into yet another memristor story on this blog.  A Sept. 3, 2015 news item on Azonano features a ‘memristor-based’ company/organization, Knowm,

Knowm Inc., a start-up pioneering next-generation advanced computing architectures and technology, today announced they are the first to develop and make commercially-available memristors with bi-directional incremental learning capability.

The device was developed through research from Boise State University’s [Idaho, US] Dr. Kris Campbell, and this new data unequivocally confirms Knowm’s memristors are capable of bi-directional incremental learning. This has been previously deemed impossible in filamentary devices by Knowm’s competitors, including IBM [emphasis mine], despite significant investment in materials, research and development. With this advancement, Knowm delivers the first commercial memristors that can adjust resistance in incremental steps in both direction rather than only one direction with an all-or-nothing ‘erase’. This advancement opens the gateway to extremely efficient and powerful machine learning and artificial intelligence applications.

A Sept. 2, 2015 Knowm news release (also on MarketWatch), which originated the news item, provides more details,

“Having commercially-available memristors with bi-directional voltage-dependent incremental capability is a huge step forward for the field of machine learning and, particularly, AHaH Computing,” said Alex Nugent, CEO and co-founder of Knowm. “We have been dreaming about this device and developing the theory for how to apply them to best maximize their potential for more than a decade, but the lack of capability confirmation had been holding us back. This data is truly a monumental technical milestone and it will serve as a springboard to catapult Knowm and AHaH Computing forward.”

Memristors with the bi-directional incremental resistance change property are the foundation for developing learning hardware such as Knowm Inc.’s recently announced Thermodynamic RAM (kT-RAM) and help realize the full potential of AHaH Computing. The availability of kT-RAM will have the largest impact in fields that require higher computational power for machine learning tasks like autonomous robotics, big-data analysis and intelligent Internet assistants. kT-RAM radically increases the efficiency of synaptic integration and adaptation operations by reducing them to physically adaptive ‘analog’ memristor-based circuits. Synaptic integration and adaptation are the core operations behind tasks such as pattern recognition and inference. Knowm Inc. is the first company in the world to bring this technology to market.

Knowm is ushering in the next phase of computing with the first general-purpose neuromemristive processor specification. Earlier this year the company announced the commercial availability of the first products in support of the kT-RAM technology stack. These include the sale of discrete memristor chips, a Back End of Line (BEOL) CMOS+memristor service, the SENSE and Application Servers and their first application named “Knowm Anomaly”, the first application built based on the theory of AHaH Computing and kT-RAM architecture. Knowm also simultaneously announced the company’s visionary developer program for organizations and individual developers. This includes the Knowm API, which serves as development hardware and training resources for co-developing the Knowm technology stack.

Knowm certainly has big ambitions. I’m a little surprised they mentioned IBM rather than HP Labs, which is where researchers first claimed to find evidence of the existence of memristors in 2008 (the story is noted in my Nanotech Mysteries wiki here). As I understand it, HP Labs is working busily (having a missed a few deadlines) on developing a commercial product using memristors.

For the curious, my latest informal roundup of memristor stories is in a May 15, 2015 posting.

Getting back to to Knowm and big ambitions, here’s Alex Nugent, Knowm CEO (Chief Executive Officer) and co-founder talking about the company and its technology,

Memristor, memristor, you are popular

Regular readers know I have a long-standing interest in memristor and artificial brains. I have three memristor-related pieces of research,  published in the last month or so, for this post.

First, there’s some research into nano memory at RMIT University, Australia, and the University of California at Santa Barbara (UC Santa Barbara). From a May 12, 2015 news item on ScienceDaily,

RMIT University researchers have mimicked the way the human brain processes information with the development of an electronic long-term memory cell.

Researchers at the MicroNano Research Facility (MNRF) have built the one of the world’s first electronic multi-state memory cell which mirrors the brain’s ability to simultaneously process and store multiple strands of information.

The development brings them closer to imitating key electronic aspects of the human brain — a vital step towards creating a bionic brain — which could help unlock successful treatments for common neurological conditions such as Alzheimer’s and Parkinson’s diseases.

A May 11, 2015 RMIT University news release, which originated the news item, reveals more about the researchers’ excitement and about the research,

“This is the closest we have come to creating a brain-like system with memory that learns and stores analog information and is quick at retrieving this stored information,” Dr Sharath said.

“The human brain is an extremely complex analog computer… its evolution is based on its previous experiences, and up until now this functionality has not been able to be adequately reproduced with digital technology.”

The ability to create highly dense and ultra-fast analog memory cells paves the way for imitating highly sophisticated biological neural networks, he said.

The research builds on RMIT’s previous discovery where ultra-fast nano-scale memories were developed using a functional oxide material in the form of an ultra-thin film – 10,000 times thinner than a human hair.

Dr Hussein Nili, lead author of the study, said: “This new discovery is significant as it allows the multi-state cell to store and process information in the very same way that the brain does.

“Think of an old camera which could only take pictures in black and white. The same analogy applies here, rather than just black and white memories we now have memories in full color with shade, light and texture, it is a major step.”

While these new devices are able to store much more information than conventional digital memories (which store just 0s and 1s), it is their brain-like ability to remember and retain previous information that is exciting.

“We have now introduced controlled faults or defects in the oxide material along with the addition of metallic atoms, which unleashes the full potential of the ‘memristive’ effect – where the memory element’s behaviour is dependent on its past experiences,” Dr Nili said.

Nano-scale memories are precursors to the storage components of the complex artificial intelligence network needed to develop a bionic brain.

Dr Nili said the research had myriad practical applications including the potential for scientists to replicate the human brain outside of the body.

“If you could replicate a brain outside the body, it would minimise ethical issues involved in treating and experimenting on the brain which can lead to better understanding of neurological conditions,” Dr Nili said.

The research, supported by the Australian Research Council, was conducted in collaboration with the University of California Santa Barbara.

Here’s a link to and a citation for this memristive nano device,

Donor-Induced Performance Tuning of Amorphous SrTiO3 Memristive Nanodevices: Multistate Resistive Switching and Mechanical Tunability by  Hussein Nili, Sumeet Walia, Ahmad Esmaielzadeh Kandjani, Rajesh Ramanathan, Philipp Gutruf, Taimur Ahmed, Sivacarendran Balendhran, Vipul Bansal, Dmitri B. Strukov, Omid Kavehei, Madhu Bhaskaran, and Sharath Sriram. Advanced Functional Materials DOI: 10.1002/adfm.201501019 Article first published online: 14 APR 2015

© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

This paper is behind a paywall.

The second published piece of memristor-related research comes from a UC Santa Barbara and  Stony Brook University (New York state) team but is being publicized by UC Santa Barbara. From a May 11, 2015 news item on Nanowerk (Note: A link has been removed),

In what marks a significant step forward for artificial intelligence, researchers at UC Santa Barbara have demonstrated the functionality of a simple artificial neural circuit (Nature, “Training and operation of an integrated neuromorphic network based on metal-oxide memristors”). For the first time, a circuit of about 100 artificial synapses was proved to perform a simple version of a typical human task: image classification.

A May 11, 2015 UC Santa Barbara news release (also on EurekAlert)by Sonia Fernandez, which originated the news item, situates this development within the ‘artificial brain’ effort while describing it in more detail (Note: A link has been removed),

“It’s a small, but important step,” said Dmitri Strukov, a professor of electrical and computer engineering. With time and further progress, the circuitry may eventually be expanded and scaled to approach something like the human brain’s, which has 1015 (one quadrillion) synaptic connections.

For all its errors and potential for faultiness, the human brain remains a model of computational power and efficiency for engineers like Strukov and his colleagues, Mirko Prezioso, Farnood Merrikh-Bayat, Brian Hoskins and Gina Adam. That’s because the brain can accomplish certain functions in a fraction of a second what computers would require far more time and energy to perform.

… As you read this, your brain is making countless split-second decisions about the letters and symbols you see, classifying their shapes and relative positions to each other and deriving different levels of meaning through many channels of context, in as little time as it takes you to scan over this print. Change the font, or even the orientation of the letters, and it’s likely you would still be able to read this and derive the same meaning.

In the researchers’ demonstration, the circuit implementing the rudimentary artificial neural network was able to successfully classify three letters (“z”, “v” and “n”) by their images, each letter stylized in different ways or saturated with “noise”. In a process similar to how we humans pick our friends out from a crowd, or find the right key from a ring of similar keys, the simple neural circuitry was able to correctly classify the simple images.

“While the circuit was very small compared to practical networks, it is big enough to prove the concept of practicality,” said Merrikh-Bayat. According to Gina Adam, as interest grows in the technology, so will research momentum.

“And, as more solutions to the technological challenges are proposed the technology will be able to make it to the market sooner,” she said.

Key to this technology is the memristor (a combination of “memory” and “resistor”), an electronic component whose resistance changes depending on the direction of the flow of the electrical charge. Unlike conventional transistors, which rely on the drift and diffusion of electrons and their holes through semiconducting material, memristor operation is based on ionic movement, similar to the way human neural cells generate neural electrical signals.

“The memory state is stored as a specific concentration profile of defects that can be moved back and forth within the memristor,” said Strukov. The ionic memory mechanism brings several advantages over purely electron-based memories, which makes it very attractive for artificial neural network implementation, he added.

“For example, many different configurations of ionic profiles result in a continuum of memory states and hence analog memory functionality,” he said. “Ions are also much heavier than electrons and do not tunnel easily, which permits aggressive scaling of memristors without sacrificing analog properties.”

This is where analog memory trumps digital memory: In order to create the same human brain-type functionality with conventional technology, the resulting device would have to be enormous — loaded with multitudes of transistors that would require far more energy.

“Classical computers will always find an ineluctable limit to efficient brain-like computation in their very architecture,” said lead researcher Prezioso. “This memristor-based technology relies on a completely different way inspired by biological brain to carry on computation.”

To be able to approach functionality of the human brain, however, many more memristors would be required to build more complex neural networks to do the same kinds of things we can do with barely any effort and energy, such as identify different versions of the same thing or infer the presence or identity of an object not based on the object itself but on other things in a scene.

Potential applications already exist for this emerging technology, such as medical imaging, the improvement of navigation systems or even for searches based on images rather than on text. The energy-efficient compact circuitry the researchers are striving to create would also go a long way toward creating the kind of high-performance computers and memory storage devices users will continue to seek long after the proliferation of digital transistors predicted by Moore’s Law becomes too unwieldy for conventional electronics.

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

Training and operation of an integrated neuromorphic network based on metal-oxide memristors by M. Prezioso, F. Merrikh-Bayat, B. D. Hoskins, G. C. Adam, K. K. Likharev,    & D. B. Strukov. Nature 521, 61–64 (07 May 2015) doi:10.1038/nature14441

This paper is behind a paywall but a free preview is available through ReadCube Access.

The third and last piece of research, which is from Rice University, hasn’t received any publicity yet, unusual given Rice’s very active communications/media department. Here’s a link to and a citation for their memristor paper,

2D materials: Memristor goes two-dimensional by Jiangtan Yuan & Jun Lou. Nature Nanotechnology 10, 389–390 (2015) doi:10.1038/nnano.2015.94 Published online 07 May 2015

This paper is behind a paywall but a free preview is available through ReadCube Access.

Dexter Johnson has written up the RMIT research (his May 14, 2015 post on the Nanoclast blog on the IEEE [Institute of Electrical and Electronics Engineers] website). He linked it to research from Mark Hersam’s team at Northwestern University (my April 10, 2015 posting) on creating a three-terminal memristor enabling its use in complex electronics systems. Dexter strongly hints in his headline that these developments could lead to bionic brains.

For those who’d like more memristor information, this June 26, 2014 posting which brings together some developments at the University of Michigan and information about developments in the industrial sector is my suggestion for a starting point. Also, you may want to check out my material on HP Labs, especially prominent in the story due to the company’s 2008 ‘discovery’ of the memristor, described on a page in my Nanotech Mysteries wiki, and the controversy triggered by the company’s terminology (there’s more about the controversy in my April 7, 2010 interview with Forrest H Bennett III).

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.