Tag Archives: R. Stanley Williams

An artificial synapse tuned by light, a ferromagnetic memristor, and a transparent, flexible artificial synapse

Down the memristor rabbit hole one more time.* I started out with news about two new papers and inadvertently found two more. In a bid to keep this posting to a manageable size, I’m stopping at four.

UK

In a June 19, 2019 Nanowerk Spotlight article, Dr. Neil Kemp discusses memristors and some of his latest work (Note: A link has been removed),

Memristor (or memory resistors) devices are non-volatile electronic memory devices that were first theorized by Leon Chua in the 1970’s. However, it was some thirty years later that the first practical device was fabricated. This was in 2008 when a group led by Stanley Williams at HP Research Labs realized that switching of the resistance between a conducting and less conducting state in metal-oxide thin-film devices was showing Leon Chua’s memristor behaviour.

The high interest in memristor devices also stems from the fact that these devices emulate the memory and learning properties of biological synapses. i.e. the electrical resistance value of the device is dependent on the history of the current flowing through it.

There is a huge effort underway to use memristor devices in neuromorphic computing applications and it is now reasonable to imagine the development of a new generation of artificial intelligent devices with very low power consumption (non-volatile), ultra-fast performance and high-density integration.

These discoveries come at an important juncture in microelectronics, since there is increasing disparity between computational needs of Big Data, Artificial Intelligence (A.I.) and the Internet of Things (IoT), and the capabilities of existing computers. The increases in speed, efficiency and performance of computer technology cannot continue in the same manner as it has done since the 1960s.

To date, most memristor research has focussed on the electronic switching properties of the device. However, for many applications it is useful to have an additional handle (or degree of freedom) on the device to control its resistive state. For example memory and processing in the brain also involves numerous chemical and bio-chemical reactions that control the brain structure and its evolution through development.

To emulate this in a simple solid-state system composed of just switches alone is not possible. In our research, we are interested in using light to mediate this essential control.

We have demonstrated that light can be used to make short and long-term memory and we have shown how light can modulate a special type of learning, called spike timing dependent plasticity (STDP). STDP involves two neuronal spikes incident across a synapse at the same time. Depending on the relative timing of the spikes and their overlap across the synaptic cleft, the connection strength is other strengthened or weakened.

In our earlier work, we were only able to achieve to small switching effects in memristors using light. In our latest work (Advanced Electronic Materials, “Percolation Threshold Enables Optical Resistive-Memory Switching and Light-Tuneable Synaptic Learning in Segregated Nanocomposites”), we take advantage of a percolating-like nanoparticle morphology to vastly increase the magnitude of the switching between electronic resistance states when light is incident on the device.

We have used an inhomogeneous percolating network consisting of metallic nanoparticles distributed in filamentary-like conduction paths. Electronic conduction and the resistance of the device is very sensitive to any disruption of the conduction path(s).

By embedding the nanoparticles in a polymer that can expand or contract with light the conduction pathways are broken or re-connected causing very large changes in the electrical resistance and memristance of the device.

Our devices could lead to the development of new memristor-based artificial intelligence systems that are adaptive and reconfigurable using a combination of optical and electronic signalling. Furthermore, they have the potential for the development of very fast optical cameras for artificial intelligence recognition systems.

Our work provides a nice proof-of-concept but the materials used means the optical switching is slow. The materials are also not well suited to industry fabrication. In our on-going work we are addressing these switching speed issues whilst also focussing on industry compatible materials.

Currently we are working on a new type of optical memristor device that should give us orders of magnitude improvement in the optical switching speeds whilst also retaining a large difference between the resistance on and off states. We hope to be able to achieve nanosecond switching speeds. The materials used are also compatible with industry standard methods of fabrication.

The new devices should also have applications in optical communications, interfacing and photonic computing. We are currently looking for commercial investors to help fund the research on these devices so that we can bring the device specifications to a level of commercial interest.

If you’re interested in memristors, Kemp’s article is well written and quite informative for nonexperts, assuming of course you can tolerate not understanding everything perfectly.

Here are links and citations for two papers. The first is the latest referred to in the article, a May 2019 paper and the second is a paper appearing in July 2019.

Percolation Threshold Enables Optical Resistive‐Memory Switching and Light‐Tuneable Synaptic Learning in Segregated Nanocomposites by Ayoub H. Jaafar, Mary O’Neill, Stephen M. Kelly, Emanuele Verrelli, Neil T. Kemp. Advanced Electronic Materials DOI: https://doi.org/10.1002/aelm.201900197 First published: 28 May 2019

Wavelength dependent light tunable resistive switching graphene oxide nonvolatile memory devices by Ayoub H.Jaafar, N.T.Kemp. DOI: https://doi.org/10.1016/j.carbon.2019.07.007 Carbon Available online 3 July 2019

The first paper (May 2019) is definitely behind a paywall and the second paper (July 2019) appears to be behind a paywall.

Dr. Kemp’s work has been featured here previously in a January 3, 2018 posting in the subsection titled, Shining a light on the memristor.

China

This work from China was announced in a June 20, 2019 news item on Nanowerk,

Memristors, demonstrated by solid-state devices with continuously tunable resistance, have emerged as a new paradigm for self-adaptive networks that require synapse-like functions. Spin-based memristors offer advantages over other types of memristors because of their significant endurance and high energy effciency.

However, it remains a challenge to build dense and functional spintronic memristors with structures and materials that are compatible with existing ferromagnetic devices. Ta/CoFeB/MgO heterostructures are commonly used in interfacial PMA-based [perpendicular magnetic anisotropy] magnetic tunnel junctions, which exhibit large tunnel magnetoresistance and are implemented in commercial MRAM [magnetic random access memory] products.

“To achieve the memristive function, DW is driven back and forth in a continuous manner in the CoFeB layer by applying in-plane positive or negative current pulses along the Ta layer, utilizing SOT that the current exerts on the CoFeB magnetization,” said Shuai Zhang, a coauthor in the paper. “Slowly propagating domain wall generates a creep in the detection area of the device, which yields a broad range of intermediate resistive states in the AHE [anomalous Hall effect] measurements. Consequently, AHE resistance is modulated in an analog manner, being controlled by the pulsed current characteristics including amplitude, duration, and repetition number.”

“For a follow-up study, we are working on more neuromorphic operations, such as spike-timing-dependent plasticity and paired pulsed facilitation,” concludes You. …

Here’s are links to and citations for the paper (Note: It’s a little confusing but I believe that one of the links will take you to the online version, as for the ‘open access’ link, keep reading),

A Spin–Orbit‐Torque Memristive Device by Shuai Zhang, Shijiang Luo, Nuo Xu, Qiming Zou, Min Song, Jijun Yun, Qiang Luo, Zhe Guo, Ruofan Li, Weicheng Tian, Xin Li, Hengan Zhou, Huiming Chen, Yue Zhang, Xiaofei Yang, Wanjun Jiang, Ka Shen, Jeongmin Hong, Zhe Yuan, Li Xi, Ke Xia, Sayeef Salahuddin, Bernard Dieny, Long You. Advanced Electronic Materials Volume 5, Issue 4 April 2019 (print version) 1800782 DOI: https://doi.org/10.1002/aelm.201800782 First published [online]: 30 January 2019 Note: there is another DOI, https://doi.org/10.1002/aelm.201970022 where you can have open access to Memristors: A Spin–Orbit‐Torque Memristive Device (Adv. Electron. Mater. 4/2019)

The paper published online in January 2019 is behind a paywall and the paper (almost the same title) published in April 2019 has a new DOI and is open access. Final note: I tried accessing the ‘free’ paper and opened up a free file for the artwork featuring the work from China on the back cover of the April 2019 of Advanced Electronic Materials.

Korea

Usually when I see the words transparency and flexibility, I expect to see graphene is one of the materials. That’s not the case for this paper (link to and citation for),

Transparent and flexible photonic artificial synapse with piezo-phototronic modulator: Versatile memory capability and higher order learning algorithm by Mohit Kumar, Joondong Kim, Ching-Ping Wong. Nano Energy Volume 63, September 2019, 103843 DOI: https://doi.org/10.1016/j.nanoen.2019.06.039 Available online 22 June 2019

Here’s the abstract for the paper where you’ll see that the material is made up of zinc oxide silver nanowires,

An artificial photonic synapse having tunable manifold synaptic response can be an essential step forward for the advancement of novel neuromorphic computing. In this work, we reported the development of highly transparent and flexible two-terminal ZnO/Ag-nanowires/PET photonic artificial synapse [emphasis mine]. The device shows purely photo-triggered all essential synaptic functions such as transition from short-to long-term plasticity, paired-pulse facilitation, and spike-timing-dependent plasticity, including in the versatile memory capability. Importantly, strain-induced piezo-phototronic effect within ZnO provides an additional degree of regulation to modulate all of the synaptic functions in multi-levels. The observed effect is quantitatively explained as a dynamic of photo-induced electron-hole trapping/detraining via the defect states such as oxygen vacancies. We revealed that the synaptic functions can be consolidated and converted by applied strain, which is not previously applied any of the reported synaptic devices. This study will open a new avenue to the scientific community to control and design highly transparent wearable neuromorphic computing.

This paper is behind a paywall.

Mott memristor

Mott memristors (mentioned in my Aug. 24, 2017 posting about neuristors and brainlike computing) gets more fulsome treatment in an Oct. 9, 2017 posting by Samuel K. Moore on the Nanoclast blog (found on the IEEE [Institute of Electrical and Electronics Engineers] website) Note: 1: Links have been removed; Note 2 : I quite like Moore’s writing style but he’s not for the impatient reader,

When you’re really harried, you probably feel like your head is brimful of chaos. You’re pretty close. Neuroscientists say your brain operates in a regime termed the “edge of chaos,” and it’s actually a good thing. It’s a state that allows for fast, efficient analog computation of the kind that can solve problems that grow vastly more difficult as they become bigger in size.

The trouble is, if you’re trying to replicate that kind of chaotic computation with electronics, you need an element that both acts chaotically—how and when you want it to—and could scale up to form a big system.

“No one had been able to show chaotic dynamics in a single scalable electronic device,” says Suhas Kumar, a researcher at Hewlett Packard Labs, in Palo Alto, Calif. Until now, that is.

He, John Paul Strachan, and R. Stanley Williams recently reported in the journal Nature that a particular configuration of a certain type of memristor contains that seed of controlled chaos. What’s more, when they simulated wiring these up into a type of circuit called a Hopfield neural network, the circuit was capable of solving a ridiculously difficult problem—1,000 instances of the traveling salesman problem—at a rate of 10 trillion operations per second per watt.

(It’s not an apples-to-apples comparison, but the world’s most powerful supercomputer as of June 2017 managed 93,015 trillion floating point operations per second but consumed 15 megawatts doing it. So about 6 billion operations per second per watt.)

The device in question is called a Mott memristor. Memristors generally are devices that hold a memory, in the form of resistance, of the current that has flowed through them. The most familiar type is called resistive RAM (or ReRAM or RRAM, depending on who’s asking). Mott memristors have an added ability in that they can also reflect a temperature-driven change in resistance.

The HP Labs team made their memristor from an 8-nanometer-thick layer of niobium dioxide (NbO2) sandwiched between two layers of titanium nitride. The bottom titanium nitride layer was in the form of a 70-nanometer wide pillar. “We showed that this type of memristor can generate chaotic and nonchaotic signals,” says Williams, who invented the memristor based on theory by Leon Chua.

(The traveling salesman problem is one of these. In it, the salesman must find the shortest route that lets him visit all of his customers’ cities, without going through any of them twice. It’s a difficult problem because it becomes exponentially more difficult to solve with each city you add.)

Here’s what the niobium dioxide-based Mott memristor looks like,

Photo: Suhas Kumar/Hewlett Packard Labs
A micrograph shows the construction of a Mott memristor composed of an 8-nanometer-thick layer of niobium dioxide between two layers of titanium nitride.

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

Chaotic dynamics in nanoscale NbO2 Mott memristors for analogue computing by Suhas Kumar, John Paul Strachan & R. Stanley Williams. Nature 548, 318–321 (17 August 2017) doi:10.1038/nature23307 Published online: 09 August 2017

This paper is behind a paywall.

Neuristors and brainlike computing

As you might suspect, a neuristor is based on a memristor .(For a description of a memristor there’s this Wikipedia entry and you can search this blog with the tags ‘memristor’ and neuromorphic engineering’ for more here.)

Being new to neuristors ,I needed a little more information before reading the latest and found this Dec. 24, 2012 article by John Timmer for Ars Technica (Note: Links have been removed),

Computing hardware is composed of a series of binary switches; they’re either on or off. The other piece of computational hardware we’re familiar with, the brain, doesn’t work anything like that. Rather than being on or off, individual neurons exhibit brief spikes of activity, and encode information in the pattern and timing of these spikes. The differences between the two have made it difficult to model neurons using computer hardware. In fact, the recent, successful generation of a flexible neural system required that each neuron be modeled separately in software in order to get the sort of spiking behavior real neurons display.

But researchers may have figured out a way to create a chip that spikes. The people at HP labs who have been working on memristors have figured out a combination of memristors and capacitors that can create a spiking output pattern. Although these spikes appear to be more regular than the ones produced by actual neurons, it might be possible to create versions that are a bit more variable than this one. And, more significantly, it should be possible to fabricate them in large numbers, possibly right on a silicon chip.

The key to making the devices is something called a Mott insulator. These are materials that would normally be able to conduct electricity, but are unable to because of interactions among their electrons. Critically, these interactions weaken with elevated temperatures. So, by heating a Mott insulator, it’s possible to turn it into a conductor. In the case of the material used here, NbO2, the heat is supplied by resistance itself. By applying a voltage to the NbO2 in the device, it becomes a resistor, heats up, and, when it reaches a critical temperature, turns into a conductor, allowing current to flow through. But, given the chance to cool off, the device will return to its resistive state. Formally, this behavior is described as a memristor.

To get the sort of spiking behavior seen in a neuron, the authors turned to a simplified model of neurons based on the proteins that allow them to transmit electrical signals. When a neuron fires, sodium channels open, allowing ions to rush into a nerve cell, and changing the relative charges inside and outside its membrane. In response to these changes, potassium channels then open, allowing different ions out, and restoring the charge balance. That shuts the whole thing down, and allows various pumps to start restoring the initial ion balance.

Here’s a link to and a citation for the research paper described in Timmer’s article,

A scalable neuristor built with Mott memristors by Matthew D. Pickett, Gilberto Medeiros-Ribeiro, & R. Stanley Williams. Nature Materials 12, 114–117 (2013) doi:10.1038/nmat3510 Published online 16 December 2012

This paper is behind a paywall.

A July 28, 2017 news item on Nanowerk provides an update on neuristors,

A future android brain like that of Star Trek’s Commander Data might contain neuristors, multi-circuit components that emulate the firings of human neurons.

Neuristors already exist today in labs, in small quantities, and to fuel the quest to boost neuristors’ power and numbers for practical use in brain-like computing, the U.S. Department of Defense has awarded a $7.1 million grant to a research team led by the Georgia Institute of Technology. The researchers will mainly work on new metal oxide materials that buzz electronically at the nanoscale to emulate the way human neural networks buzz with electric potential on a cellular level.

A July 28, 2017 Georgia Tech news release, which originated the news item, delves further into neuristors and the proposed work leading to an artificial retina that can learn (!). This was not where I was expecting things to go,

But let’s walk expectations back from the distant sci-fi future into the scientific present: The research team is developing its neuristor materials to build an intelligent light sensor, and not some artificial version of the human brain, which would require hundreds of trillions of circuits.

“We’re not going to reach circuit complexities of that magnitude, not even a tenth,” said Alan Doolittle, a professor at Georgia Tech’s School of Electrical and Computer Engineering. “Also, currently science doesn’t really know yet very well how the human brain works, so we can’t duplicate it.”

Intelligent retina

But an artificial retina that can learn autonomously appears well within reach of the research team from Georgia Tech and Binghamton University. Despite the term “retina,” the development is not intended as a medical implant, but it could be used in advanced image recognition cameras for national defense and police work.

At the same time, it would significantly advance brain-mimicking, or neuromorphic, computing. The research field that takes its cues from what science already does know about how the brain computes to develop exponentially more powerful computing.

The retina would be comprised of an array of ultra-compact circuits called neuristors (a word combining “neuron” and “transistor”) that sense light, compute an image out of it and store the image. All three of the functions would occur simultaneously and nearly instantaneously.

“The same device senses, computes and stores the image,” Doolittle said. “The device is the sensor, and it’s the processor, and it’s the memory all at the same time.” A neuristor itself is comprised in part of devices called memristors inspired by the way human neurons work.

Brain vs. PC

That cuts out loads of processing and memory lag time that are inherent in traditional computing.

Take the device you’re reading this article on: Its microprocessor has to tap a separate memory component to get data, then do some processing, tap memory again for more data, process some more, etc. “That back-and-forth from memory to microprocessor has created a bottleneck,” Doolittle said.

A neuristor array breaks the bottleneck by emulating the extreme flexibility of biological nervous systems: When a brain computes, it uses a broad set of neural pathways that flash with enormous data. Then, later, to compute the same thing again, it will use quite different neural paths.

Traditional computer pathways, by contrast, are hardwired. For example, look at a present-day processor and you’ll see lines etched into it. Those are pathways that computational signals are limited to.

The new memristor materials at the heart of the neuristor are not etched, and signals flow through the surface very freely, more like they do through the brain, exponentially increasing the number of possible pathways computation can take. That helps the new intelligent retina compute powerfully and swiftly.

Terrorists, missing children

The retina’s memory could also store thousands of photos, allowing it to immediately match up what it sees with the saved images. The retina could pinpoint known terror suspects in a crowd, find missing children, or identify enemy aircraft virtually instantaneously, without having to trawl databases to correctly identify what is in the images.

Even if you take away the optics, the new neuristor arrays still advance artificial intelligence. Instead of light, a surface of neuristors could absorb massive data streams at once, compute them, store them, and compare them to patterns of other data, immediately. It could even autonomously learn to extrapolate further information, like calculating the third dimension out of data from two dimensions.

“It will work with anything that has a repetitive pattern like radar signatures, for example,” Doolittle said. “Right now, that’s too challenging to compute, because radar information is flying out at such a high data rate that no computer can even think about keeping up.”

Smart materials

The research project’s title acronym CEREBRAL may hint at distant dreams of an artificial brain, but what it stands for spells out the present goal in neuromorphic computing: Cross-disciplinary Electronic-ionic Research Enabling Biologically Realistic Autonomous Learning.

The intelligent retina’s neuristors are based on novel metal oxide nanotechnology materials, unique to Georgia Tech. They allow computing signals to flow flexibly across pathways that are electronic, which is customary in computing, and at the same time make use of ion motion, which is more commonly know from the way batteries and biological systems work.

The new materials have already been created, and they work, but the researchers don’t yet fully understand why.

Much of the project is dedicated to examining quantum states in the materials and how those states help create useful electronic-ionic properties. Researchers will view them by bombarding the metal oxides with extremely bright x-ray photons at the recently constructed National Synchrotron Light Source II.

Grant sub-awardee Binghamton University is located close by, and Binghamton physicists will run experiments and hone them via theoretical modeling.

‘Sea of lithium’

The neuristors are created mainly by the way the metal oxide materials are grown in the lab, which has advantages over building neuristors in a more wired way.

This materials-growing approach is conducive to mass production. Also, though neuristors in general free signals to take multiple pathways, Georgia Tech’s neuristors do it much more flexibly thanks to chemical properties.

“We also have a sea of lithium, and it’s like an infinite reservoir of computational ionic fluid,” Doolittle said. The lithium niobite imitates the way ionic fluid bathes biological neurons and allows them to flash with electric potential while signaling. In a neuristor array, the lithium niobite helps computational signaling move in myriad directions.

“It’s not like the typical semiconductor material, where you etch a line, and only that line has the computational material,” Doolittle said.

Commander Data’s brain?

“Unlike any other previous neuristors, our neuristors will adapt themselves in their computational-electronic pulsing on the fly, which makes them more like a neurological system,” Doolittle said. “They mimic biology in that we have ion drift across the material to create the memristors (the memory part of neuristors).”

Brains are far superior to computers at most things, but not all. Brains recognize objects and do motor tasks much better. But computers are much better at arithmetic and data processing.

Neuristor arrays can meld both types of computing, making them biological and algorithmic at once, a bit like Commander Data’s brain.

The research is being funded through the U.S. Department of Defense’s Multidisciplinary University Research Initiatives (MURI) Program under grant number FOA: N00014-16-R-FO05. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of those agencies.

Fascinating, non?

A new memristor circuit

Apparently engineers at the University of Massachusetts at Amherst have developed a new kind of memristor. A Sept. 29, 2016 news item on Nanowerk makes the announcement (Note: A link has been removed),

Engineers at the University of Massachusetts Amherst are leading a research team that is developing a new type of nanodevice for computer microprocessors that can mimic the functioning of a biological synapse—the place where a signal passes from one nerve cell to another in the body. The work is featured in the advance online publication of Nature Materials (“Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing”).

Such neuromorphic computing in which microprocessors are configured more like human brains is one of the most promising transformative computing technologies currently under study.

While it doesn’t sound different from any other memristor, that’s misleading. Do read on. A Sept. 27, 2016 University of Massachusetts at Amherst news release, which originated the news item, provides more detail about the researchers and the work,

J. Joshua Yang and Qiangfei Xia are professors in the electrical and computer engineering department in the UMass Amherst College of Engineering. Yang describes the research as part of collaborative work on a new type of memristive device.

Memristive devices are electrical resistance switches that can alter their resistance based on the history of applied voltage and current. These devices can store and process information and offer several key performance characteristics that exceed conventional integrated circuit technology.

“Memristors have become a leading candidate to enable neuromorphic computing by reproducing the functions in biological synapses and neurons in a neural network system, while providing advantages in energy and size,” the researchers say.

Neuromorphic computing—meaning microprocessors configured more like human brains than like traditional computer chips—is one of the most promising transformative computing technologies currently under intensive study. Xia says, “This work opens a new avenue of neuromorphic computing hardware based on memristors.”

They say that most previous work in this field with memristors has not implemented diffusive dynamics without using large standard technology found in integrated circuits commonly used in microprocessors, microcontrollers, static random access memory and other digital logic circuits.

The researchers say they proposed and demonstrated a bio-inspired solution to the diffusive dynamics that is fundamentally different from the standard technology for integrated circuits while sharing great similarities with synapses. They say, “Specifically, we developed a diffusive-type memristor where diffusion of atoms offers a similar dynamics [?] and the needed time-scales as its bio-counterpart, leading to a more faithful emulation of actual synapses, i.e., a true synaptic emulator.”

The researchers say, “The results here provide an encouraging pathway toward synaptic emulation using diffusive memristors for neuromorphic computing.”

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

Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing by Zhongrui Wang, Saumil Joshi, Sergey E. Savel’ev, Hao Jiang, Rivu Midya, Peng Lin, Miao Hu, Ning Ge, John Paul Strachan, Zhiyong Li, Qing Wu, Mark Barnell, Geng-Lin Li, Huolin L. Xin, R. Stanley Williams [emphasis mine], Qiangfei Xia, & J. Joshua Yang. Nature Materials (2016) doi:10.1038/nmat4756 Published online 26 September 2016

This paper is behind a paywall.

I’ve emphasized R. Stanley Williams’ name as he was the lead researcher on the HP Labs team that proved Leon Chua’s 1971 theory about the memristor and exerted engineering control of the memristor in 2008. (Bernard Widrow, in the 1960s,  predicted and proved the existence of something he termed a ‘memistor’. Chua arrived at his ‘memristor’ theory independently.)

Austin Silver in a Sept. 29, 2016 posting on The Human OS blog (on the IEEE [Institute of Electrical and Electronics Engineers] website) delves into this latest memristor research (Note: Links have been removed),

In research published in Nature Materials on 26 September [2016], Yang and his team mimicked a crucial underlying component of how synaptic connections get stronger or weaker: the flow of calcium.

The movement of calcium into or out of the neuronal membrane, neuroscientists have found, directly affects the connection. Chemical processes move the calcium in and out— triggering a long-term change in the synapses’ strength. 2015 research in ACS NanoLetters and Advanced Functional Materials discovered that types of memristors can simulate some of the calcium behavior, but not all.

In the new research, Yang combined two types of memristors in series to create an artificial synapse. The hybrid device more closely mimics biological synapse behavior—the calcium flow in particular, Yang says.

The new memristor used–called a diffusive memristor because atoms in the resistive material move even without an applied voltage when the device is in the high resistance state—was a dielectic film sandwiched between Pt [platinum] or Au [gold] electrodes. The film contained Ag [silver] nanoparticles, which would play the role of calcium in the experiments.

By tracking the movement of the silver nanoparticles inside the diffusive memristor, the researchers noticed a striking similarity to how calcium functions in biological systems.

A voltage pulse to the hybrid device drove silver into the gap between the diffusive memristor’s two electrodes–creating a filament bridge. After the pulse died away, the filament started to break and the silver moved back— resistance increased.

Like the case with calcium, a force made silver go in and a force made silver go out.

To complete the artificial synapse, the researchers connected the diffusive memristor in series to another type of memristor that had been studied before.

When presented with a sequence of voltage pulses with particular timing, the artificial synapse showed the kind of long-term strengthening behavior a real synapse would, according to the researchers. “We think it is sort of a real emulation, rather than simulation because they have the physical similarity,” Yang says.

I was glad to find some additional technical detail about this new memristor and to find the Human OS blog, which is new to me and according to its home page is a “biomedical blog, featuring the wearable sensors, big data analytics, and implanted devices that enable new ventures in personalized medicine.”

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.

Memristors have always been with us

Sprightly, a word not often used in conjunction with technology of any kind,  is the best of way describing the approach that researchers Varun Aggarwal and Gaurav Gandhi, along with Dr. Leon Chua, have taken towards their discovery that memristors are all around us. ( For anyone not familiar with the concept, I suggest reading the Wikipedia essay on memristors as it includes information about the various critiques of the memristor definition, as well as, the definition.)

It was Dexter Johnson in his June 6, 2013 post on the IEEE (Institute of Electrical and Electronics Engineers) Nanoclast blog who alerted me to this latest memristor work (Note: Links have been removed),

Two researchers from mLabs in India, along with Prof. Leon Chua at the University of California Berkeley, who first postulated the memristor in a paper back in 1971, have discovered the simplest physical implementation for the memristor, which can be built by anyone and everyone.

In two separate papers, one published in arXiv (“Bipolar electrical switching in metal-metal contacts”) and the other in the IEEE’s own Circuits and Systems Magazine (“The First Radios Were Made Using Memristors!”), Chua and the researchers, Varun Aggarwal and Gaurav Gandhi, discovered that simple imperfect point contacts all around us act as memristors.

“Our arXiv paper talks about the coherer, which comprises an imperfect metal-metal contact in embodiments such as a point contact between two metallic balls, granular media or a metal-mercury interface,” Gandhi explained to me via e-email. “On the other hand, the CAS paper comprises an imperfect metal-semiconductor contact (Cat’s Whisker) which was also the first solid-state diode. Both the systems have as their signature an imperfect point contact between two conducting/partially-conducting elements. Both act like memristor.”

I’ll get to the articles in a minutes, first let’s look at the researchers’ website, Mlabs home page (splash page). BTW, I have a soft spot for websites that are easy to navigate and don’t irritate me with movement or pop-ups (thank you mLabs). I think this description of the researchers (Aggarwal and Gandhi) and how they came to develop mLabs (excerpted from the About us page) explains why I described their approach as sprightly,

As they say, anything can happen over a cup of coffee and this story is no different! Gaurav and Varun were friends for over a decade, and one fine day they were sitting at a coffee house discussing Gaurav’s trip to the Second Memristor and Memristive Symposium at Berkeley. Gaurav shared the exciting work around memristor that he witnessed at Berkeley. Varun, who has been an evangelist of Jagadish Chandra Bose’s work thought there was some correlation between the research work of Bose and memristor. He convinced Gaurav to look deeper into these aspects. Soon, a plan was put forth, they wore their engineering gloves and mLabs was born. Gaurav quit his job for full time involvement at mLabs, while Varun assisted and advised throughout.

Three years of curiosity, experimentation, discussions and support from various researchers and professors from different parts of the world, led us to where we are today.

We are also sincerely grateful to Prof. Leon Chua for his continuous support, mentorship and indispensable contribution to our work.

As Dexter notes, Aggarwal and Gandhi have written papers about two different ways to create memristors, the arXiv paper, Bipolar electrical switching in metal-metal contacts, describes how coherers* could be used to create simple memristors for research purposes. This paper also makes the argument that the memristor is a fundamental circuit (a claim which is a matter of considerable debate as the Wikipedia Memristor essay notes briefly),

Our new results show that bipolar switching can be observed in a large class of metals by a simple construction in form of a point-contact or granular media. It does not require complex construction, particular materials or small geometries. The signature of all our devices is an imperfect metal-metal contact and the physical mechanism for the observed behavior needs to be further studied. That the electrical behavior of these simple, naturally-occurring physical constructs can be modeled by a memristor, but not the other three passive elements, is an indication of its fundamental nature. By providing the canonic physical implementation for memristor, the present work not only lls an important gap in the study of switching devices, but also brings them into the realm of immediate practical use and implementation.

Due to the fact that the second article, the one in the IEEE published Circuits and Systems magazine, is behind a paywall, I can’t do much more than offer the title and the first paragraph,

The First Radios Were Made Using Memristors!

In 2008, Williams et al. reported the discovery of the fourth fundamental passive circuit element, memristor, which exhibits electrically controllable state-dependent resistance [1]. We show that one of the first wireless radio detector, called cat?s whisker, also the world?s first solid-state diode, had memristive properties. We have identified the state variable governing the resistance state of the device and can program it to switch between multiple stable resistance states. Our observations and results are valid for a larger class of devices called coherers, which include the cat?s whisker. These devices constitute the missing canonical physical implementations for a memristor (ref. Fig. 1).

It’s fascinating when you consider that up until now researching memristors meant having high tech equipment. I wonder how many backyard memristor labs are going to spring up?

On a somewhat related note, Dexter mentions that HP Labs ‘memristor’ products will be available in 2014. This latest date represents two postponements. Originally meant to be on the market in the summer of 2013, the new products were then supposed to brought to market in late 2013 as per my Feb. 7, 2013 posting; scroll down about 75% of the way).

*’corherers’ corrected to ‘coherers’ Oct. 16, 2015 1345 hours PST.

A step closer to artificial synapses courtesy of memristors

Researchers from HRL Laboratories and the University of Michigan have built what they claim is a type of artificial synapse by using memristors. From the March 29, 2012 news item on Nanowerk,

In a step toward computers that mimic the parallel processing of complex biological brains, researchers from HRL Laboratories, LLC, and the University of Michigan have built a type of artificial synapse.

They have demonstrated the first functioning “memristor” array stacked on a conventional complementary metal-oxide semiconductor (CMOS) circuit. Memristors combine the functions of memory and logic like the synapses of biological brains.

The researchers developed a vertically integrated hybrid electronic circuit by combining the novel memristor developed at the University of Michigan with wafer scale heterogeneous process integration methodology and CMOS read/write circuitry developed at HRL. “This hybrid circuit is a critical advance in developing intelligent machines,” said HRL SyNAPSE program manager and principal investigator Narayan Srinivasa. “We have created a multi-bit fully addressable memory storage capability with a density of up to 30 Gbits/cm², which is unprecedented in microelectronics.”

Industry is seeking hybrid systems such as this one, the researchers say. Dubbed “R-RAM,” they could shatter the looming limits of Moore’s Law, which predicts a doubling of transistor density and therefore chip speed every two years.

“We’re reaching the fundamental limits of transistor scaling. This hybrid integration opens many opportunities for greater memory capacity and higher performance of conventional computers.  It has great potential in future non-volatile memory that would improve upon today’s Flash, as well as reconfigurable circuits,” said Wei Lu, an associate professor at the U-M Department of Electrical Engineering and Computer Science whose group developed the memristor array.

This work is being done as part of a DARPA (Defense Advanced Research Projects Agency) project titled, SyNAPSE, from the news item,

The work is part of the Defense Advanced Research Projects Agency’s (DARPA) SyNAPSE Program, or Systems of Neuromorphic Adaptive Plastic Scalable Electronics. Since 2008, the HRL-led SyNAPSE team has been developing a new paradigm for “neuromorphic computing” modeled after biology.

While I haven’t come across HRL Laboratories before, I have mentioned Dr. Wei Lu and his work with memristors in my April 15, 2010 posting. As for HRL Laboratories, they were founded in 1948 by Howard Hughes as the Hughes Research Laboratories (from the company’s History page),

HRL Laboratories continues the legacy of technology advances that began at Hughes Research Laboratories, established by Howard Hughes in 1948. HRL Laboratories, LLC, was organized as a limited liability company (LLC) on December 17, 1997 and received its first patent on September 12, 2000. With more than 750 patents to our name since then and counting, we’re proud of our talented group of researchers, who continue the long tradition of technical excellence in innovation.

First Laser
One of Hughes’ most notable achievements came in 1960 with the demonstration of the world’s first laser which used a synthetic ruby crystal. The ruby laser became the basis of a multibillion-dollar laser range finder business for Hughes. In 2010 during the 50th anniversary of the laser, HRL was designated a Physics Historic Site by the American Physical Society and was selected an IEEE Milestones location as the facility where the first working laser was demonstrated.

HRL has organized its researchers in a number of teams, the one of most interest in this context is the Center for Neural and Emergent Systems,

Part of HRL’s Information and Systems Sciences Laboratory, the Center for Neural and Emergent Systems (CNES) is dedicated to exploring and developing an innovative neural & emergent computing paradigm for creating intelligent, efficient machines that can interact with, react and adapt to, evolve, and learn from their environments.

CNES was founded on the principle that all intelligent systems are open thermodynamic systems capable of self-organization, whereby structural order emerges from disorder as a natural consequence of exchanging energy, matter or entropy with their environments.

These systems exist in a state far from equilibrium where the evolution of complex behaviors cannot be readily predicted from purely local interactions between the system’s parts. Rather, the emergent order and structure of the system arises from manifold interactions of its parts. These emergent systems contain amplifying-damping loops as a result of which very small perturbations can cause large effects or no effect at all. They become adaptive when the component relationships within the system become tuned for a particular set of tasks.

CNES promotes the idea that the neural system in the brain is an example of such a complex adaptive system. A key goal of CNES is to explain how computations in the brain can help explain the realization of complex behaviors such as perception, planning, decision making and navigation due to brain-body-environment interactions.

This has reminded me of HP Labs and their work with memristors (I have many postings, too many to list here) and understand that they will be rolling out ‘memristor-based’ products in 2013. From the  Oct. 8, 2011 article by Peter Clarke for EE Times,

The ‘memristor’ two-terminal non-volatile memory technology, in development at Hewlett Packard Co. since 2008, is on track to be in the market and taking share from flash memory within 18 months, according to Stan Williams, senior fellow at HP Labs.

“We have a lot of big plans for it and we’re working with Hynix Semiconductor to launch a replacement for flash in the summer of 2013 and also to address the solid-state drive market,” Williams told the audience of the International Electronics Forum, being held here [Seville, Spain].

ETA June 11, 2012: New artificial synapse development is mentioned in George Dvorsky’s June 11, 2012 posting (on the IO9.com website) about a nanoscale electrochemical switch developed by researchers in a Japan.

Memristor update

HP Labs is making memristor news again. From a news item on physorg.ocm,

HP is partnering with Korean memory chip maker Hynix Semiconductor Inc. to make chips that contain memristors. Memristors are a newly discovered building block of electrical circuits.

HP built one in 2008 that confirmed what scientists had suspected for nearly 40 years but hadn’t been able to prove: that circuits have a weird, natural ability to remember things even when they’re turned off.

I don’t remember the story quite that way, i.e.,  “confirmed what scientists had suspected for nearly 40 years” as I recall the theory that R. Stanley William (the HP Labs team leader) cites  is from Dr. Leon Chua circa 1971 and was almost forgotten. (Unbeknownst to Dr. Chua, there was a previous theorist in the 1960s who posited a similar notion which he called a memistor. See Memistors, Memristors, and the Rise of Strong Artificial Intelligence, an article by Blaise Mouttet, for a more complete history. ETA: There’s additional material from Blaise at http://www.neurdon.com/)

There’s more about HP Labs and its new partner at BBC News in an article by Jason Palmer,

Electronics giant HP has joined the world’s second-largest memory chip maker Hynix to manufacture a novel member of the electronics family.

The deal will see “memristors” – first demonstrated by HP in 2006 [I believe it was 2008] – mass produced for the first time.

Memristors promise significantly greater memory storage requiring less energy and space, and may eventually also be employed in processors.

HP says the first memristors should be widely available in about three years.

If you follow the link to the story, there’s also a brief BBC video interview with Stanley Williams.

My first 2010 story on the memristor is here and later, there’s an interview I had with Forrest H Bennet III who argues that the memristor is not a fourth element (in addition to the capacitor, resistor, and inductor) but is in fact part of an infinite table of circuit elements.

ETA: I have some additional information from the news release on the HP Labs website,

HP today announced that it has entered into a joint development agreement with Hynix Semiconductor Inc., a world leader in the manufacture of computer memory, to bring memristor technology to market.

Memristors represent a fourth basic passive circuit element. They existed only in theory until 2006 – when researchers in HP Labs’ Information and Quantum Systems Laboratory (IQSL) first intentionally demonstrated their existence.

Memory chips created with memristor technology have the potential to run considerably faster and use much less energy than Flash memory technologies, says Dr. Stanley Williams, HP Senior Fellow and IQSL founding Director.

“We believe that the memristor is a universal memory that over time could replace Flash, DRAM, and even hard drives,” he says.

Uniting HP’s world-class research and IP with a first-rate memory manufacturer will allow high-quality, memristor-based memory to be developed quickly and on a mass scale, Williams adds.

Also, the video interview with Dr. Williams is on youtube and is not a BBC video as I believed. So here’s the interview,

The memristor rises; commercialization and academic research in the US; carbon nanotubes could be made safer than we thought

In 2008, two memristor papers were published in Nature and Nature Nanotechnology, respectively. In the first (Nature, May 2008 [article still behind a paywall], a team at HP Labs claimed they had proved the existence of memristors (a fourth member of electrical engineering’s ‘Holy Trinity of the capacitor, resistor, and inductor’). In the second paper (Nature Nanotechnology, July 2008 [article still behind a paywall]) the team reported that they had achieved engineering control.

I mention this because (a) there’s some new excitement about memristors and (b) I love the story (you can read my summary of the 2008 story here on the Nanotech Mysteries wiki).

Unbeknownst to me in 2008, there was another team, located in Japan, whose work  on slime mould inspired research by a group at the University of California San Diego (UC San Diego)  which confirmed theorist Leon Chua’s (he first suggested memristors existed in 1971) intuition that biological organisms used memristive systems to learn. From an article (Synapse on a Chip) by Surf daddy Orca on the HPlus magazine site,

Experiments with slime molds in 2008 by Tetsu Saisuga at Hokkaido University in Sapporo sparked additional research at the University of California, San Diego by Max Di Ventra. Di Ventra was familiar with Chua’s work and built a memristive circuit that was able to learn and predict future signals. This ability turns out to be similar to the electrical activity involved in the ebb and flow of potassium and sodium ions across cellular membranes: synapses altering their response according to the frequency and strength of signals. New Scientist reports that Di Ventra’s work confirmed Chua’s suspicions that “synapses were memristors.” “The ion channel was the missing circuit element I was looking for,” says Chua, “and it already existed in nature.”

Fast forward to 2010 and a team at the University of Michigan led by Dr. Wei Lu showing how synapses behave like memristors (published in Nano Letters, DOI: 10.1021/nl904092h [article behind paywall]). (Fromthe  HPlus site article)

Scientific American describes a US military-funded project that is trying to use the memristor “to make neural computing a reality.” DARPA’s Systems of Neuromorphic Adaptive Plastic Scalable Electronics Program (SyNAPSE) is funded to create “electronic neuromorphic machine technology that is scalable to biological levels.”

I’m not sure if the research in Michigan and elsewhere is being funded by DARPA (the US Dept. of Defense’s Defense Advanced Research Project Agency) although it seems likely.

In the short term, scientists talk about energy savings (no need to reboot your computer when you turn it back on). In the longer term, they talk about hardware being able to learn. (Thanks to the Foresight Institute for the latest update on the memristor story and the pointer to HPlus.) Do visit the HPlus site as there are some videos of scientists talking about memristors and additional information (there’s yet another team working on research that is tangentially related).

Commercializing academic research in US

Thanks to Dave Bruggeman at the Pasco Phronesis blog who’s posted some information about a White House Request for Information (RFI) on commercializing academic research. This is of particular interest not just because of the discussion about innovation in Canada but also because the US National Nanotechnology Initiative’s report to PCAST (President’s Council of Advisors on Science and Technology, my comments about the webcast of the proceedings here). From the Pasco Phronesis posting about the NNI report,

While the report notes that the U.S. continues to have a strong nanotechnology sector and corresponding support from the government. However, as with most other economic and research sectors, the rest of the world is catching up, or spending enough to try and catch up to the United States.

According to the report, more attention needs to be paid to commercialization efforts (a concern not unique to nanotechnology).

I don’t know how long the White House’s RFI has been under development but it was made public at the end of March 2010 just weeks after the latest series of reports to PCAST. As for the RFI itself, from the Pasco Phronesis posting about it,

The RFI questions are organized around two basic concerns:

  • Seeking ideas for supporting the commercialization and diffusion of university research. This would include best practices, useful models, metrics (with evidence of their success), and suggested changes in federal policy and/or research funding. In addition, the RFI is interested in how commercialization ecosystems can be developed where none exist.
  • Collecting data on private proof of concept centers (POCCs). These entities seek to help get research over the so-called “Valley of Death” between demonstrable research idea and final commercial product. The RFI is looking for similar kinds of information as for commercialization in general: best practices, metrics, underlying conditions that facilitate such centers.

I find the news of this RFI a little surprising since I had the impression that commercialization of academic research in the US is far more advanced than it is here in Canada. Mind you, that impression is based on a conversation I had with a researcher a year ago who commented that his mentor at a US university rolled out more than 1 start up company every year. As I understand it researchers in Canada may start up one or two companies in their career but never a series of them.

Carbon nanotubes, is exposure ok?

There’s some new research which suggests that carbon nanotubes can be broken down by an enzyme. From the news item on Nanowerk,

A team of Swedish and American scientists has shown for the first time that carbon nanotubes can be broken down by an enzyme – myeloperoxidase (MPO) – found in white blood cells. Their discoveries are presented in Nature Nanotechnology (“Carbon nanotubes degraded by neutrophil myeloperoxidase induce less pulmonary inflammation”) and contradict what was previously believed, that carbon nanotubes are not broken down in the body or in nature. The scientists hope that this new understanding of how MPO converts carbon nanotubes into water and carbon dioxide can be of significance to medicine.

“Previous studies have shown that carbon nanotubes could be used for introducing drugs or other substances into human cells,” says Bengt Fadeel, associate professor at the Swedish medical university Karolinska Institutet. “The problem has been not knowing how to control the breakdown of the nanotubes, which can caused unwanted toxicity and tissue damage. Our study now shows how they can be broken down biologically into harmless components.”

I believe they tested single-walled carbon nanotubes (CNTs) only as the person who wrote the news release seems unaware that mutil-walled CNTs also exist. In any event, this could be very exciting if this research holds up under more testing.

French want more nanotech public debates; British science oral history project

After last month’s post about disturbances (causing at least one cancellation) taking place during a series of nanotechnology public debates in France, it was a surprise to find that at least one French group wants to continue the ‘discussion’. This last series of  events has been completed with a report due in April 2010. According to a news item on Chemical Watch, France Nature Environnement (FNE) is urging more public debates. From Chemical Watch,

The French public debate on nanotechnologies that began in September ended this week. An official summary of the 17 debates will be published at the end of April, but environmental organisation France Nature Environnement (FNE) says in its conclusions that further discussion is needed to decide where the technology is useful for human advancement and where its use is unacceptable.

You can look at the FNE news item here but it is in French and the site doesn’t seem hospitable to Firefox,  so do try another browser.

Meanwhile, the Brits are embarking on an oral history of British science. From the news item on BBC News,

The British Library has begun a project to create a vast, online oral history and archive of British science.

The three-year project will see 200 British scientists interviewed and their recollections recorded for the audio library.

“We have long been painfully aware that there’s a marked absence of significant recordings of scientists,” said Dr Rob Perks, curator of oral history at the British Library.

For instance, said Dr Perks, in the current sound archives there are only two recordings of Ernest Rutherford, none of computer pioneer Alan Turing, hovercraft inventor Christopher Cockerell or AV Hill, a physiologist and Nobel laureate.

A study carried out prior to the project being started found that in the last ten years, 30 leading British scientists including 9 Nobel winners have died leaving little or no archive of their work.

I’m glad to hear that this oral history is being preserved although I do wonder about the recording formats. One of the problems with archiving materials is maintaining to access them afterwards.

Coincidentally, one of the local Vancouver papers (The Georgia Straight) has an article by Rhiannon Coppin (in the Feb. 25 – March 4, 2010 issue) about the City of Vancouver archives and their attempts at digital archiving. From the article,

Every day, Vancouver’s city archivist and director of records and archives runs a rescue operation on our past. Les Mobbs might send out film reels from the ’30s for repair, or he could receive a donation of early-20th-century photographic negatives that need to be catalogued, scanned, and put into cold storage.

Lately, Mobbs has been putting equal consideration into how to preserve our future. More and more of the city’s legal and cultural record is being created in a digital format; in other words, it’s “born digital”, he told the Georgia Straight.

The pitfall in digital archiving is that we’re poor caretakers of electronic file formats. In 50 or 100 years, we’ll know we’ve won the preservation game if we can open and read a computer document created today. But even in 2010, we’re missing out on 20-year-old WordStar files stuck on five-and-a-quarter-inch floppy disks. Ironically, it may be safer to keep a paper copy of a document than to store the original computer file.

“We’ve been dealing with paper for 2,000 years,” Mobbs said. “We have a lot of experience with what paper is, what it looks like, and how it’s preserved.”

While acid decay, mould, brittleness, and water damage are formidable but vanquishable foes, machine decay, format obsolescence, and file integrity degradation are virtually unconquerable. The short lifetime of many licensed software formats and the quick deaths of so much hardware (remember LaserDisc?) have posed a particular challenge for archivists like Mobbs.

“How do we preserve material that is, for all intents and purposes, essentially transitory?” he asked.

While this discussion might seem irrelevant on a mostly science-oriented blog, the ‘memristor’ story highlights why information about the past is so important. In 2008, R. Stanley Williams (HP Labs) and his colleagues published two papers, the first proving the existence of a fourth member, a memristor, of electrical engineering’s ‘holy trinity’ of the resistor, capacitor, and inductor and the second paper where they established engineering control over the memristor. Williams  and his team both solved a problem they were experiencing in the lab and made engineering history, in part  by reviewing engineering theories dating back at least 30 years. You can read my post about it here.

Imagine if those theories had been locked into formats that were no longer accessible. That’s one of the major reasons for preserving the past, it can yield important information.

In the interest of full disclosure, I once worked for the City of Vancouver archives.