Blockchains go mainstream…


I read an article a while back on Finland’s use of blockchain technology to provide bank accounts and identity services to immigrants (see  MIT TechReview article about Finland).

Blockchains were originally invented as a way of supporting financial transactions outside the current, government monitored, financial marketplace. With Finland’s experiment, the government is starting to use blockchains to support the unbanked and monitoring their financial activity – go figure.

Debit cards on blockchain

Finland’s using a Helsinki based startup MONI, to assign a MONI card, essentially a prepaid MasterCard, to all immigrants. An immigrant can use their MONI card to pay for anything online or in real life, use it as a direct deposit account or to receive and track the use of government assistance.

Underlying the MONI card is public blockchain technology. That is MONI  is not using normal credit card services to support it’s bank accounts, MONI money transfers are done through the use of public blockchains.

MONI accounts are essentially (crypto currency) wallets but used as a debit card. The user merely enters a series of numbers into web forms or uses their MONI card at a credit card terminals throughout Europe. Transferring money between MONI users anywhere in the World is also free and instantaneous.

Finland also sees an immutable record of all immigrant financial transactions,  that can be monitored to track immigrant (financial) integration into the country.

MONI is intending to make this service more broadly available. A MONI card account costs €2/month and MONI take’s a small cut out of each monetary transaction.

IDs on blockchain

I read another article the other day “Microsoft to implement blockchain-based ID system” in CoinTelegraph about using blockchains as a universal digital ID.

India has over the last decade, implemented a digital government ID using biometrics (see Aadhaar wikipedia article). Other countries have been moving to e-government where use of government services is implemented over the Internet (see EU article on eGovernment in Lithuania). Such eGovernment services depend on a digitized population registry.

Although it’s unclear whether Aadhaar and Lithuania make use of blockchain technology for their ID services, Microsoft’s definitely looking to blockchains to provide unique accounts/digital IDs to it’s population of users.

User signon’s has been a prevalent problem of the web for years. Each and every web and mobile App requires a person to signon to personalize their App. Nowadays, many Apps support using Google ID or Facebook ID for a single signon and there are other technologies being offered that provide similar services. Using a blockchain ID could easily support a single signon service.

The blockchain ID (wallet) public key could easily be used to encrypt an authentication transaction, identifying the App and the user. This authentication transaction would be processed by the blockchain digital ID service would use the private key to decrypt the transaction and use a backend ID App repository for the user to check to see that the user loging in, is the person that opened the account, acting as a sort of “proof of who you are”

Storage on blockchain

Filecoin and StorJ are storage providers that use blockchain services to allow others to use your local (or networked) storage to provide storage to the world.

A while back I had written about (free) peer to peer storage and compute services  (see my Free P2P cloud storage … post). But the problem was how do people benefit from hosting the P2P storage or compute. Filecoin and Storj solved this by paying in cryptocurrencies for storage hosted on your hardware.

Filecoin offers a storage auction and hosting service that anyone worldwide can log into and use. The data stored is encrypted end-to-end so that no one can see what’s being stored and the data is also erasure coded so that it  is protected and accessible even with having one or more hosting sites be offline.

Filecoin uses “proofs of storage“, “proofs of space”, “proofs of data possession“, and “proofs of retrievability” as a way to guarantee their storage service works properly. They also use chained “proofs of replication” as “proofs of spacetime” as service validation checks. Proofs of Replication are a way of insuring that storage providers are not deduplicating data copies and charging for non-deduped storage. (See Filecoin’s Proof of Replication paper for more info).

Storj looks somewhat similar to Filecoin, but without as much sophistication behind it.

Compute on blockchain

Ethereum was invented to support smart contracts that run on blockchain technology. IBM’s HyperLegder OpenLedger project (see our GreyBeardsOnStorga Podcast and RayOnStorage post on Hyperledger) is another example.

Smart contracts are essentially applications that run in a blockchains virtualized environment. Blockchain services are used to run an application and validate that’s it’s run only once. In some cases smart contracts use  external oracles to query as a way to verify something or some action has occurred outside the blockchain. Other oracles can be entirely digital entities that check on a particular commodity price, weather pattern, account value, etc. The oracle becomes a critical step in determining the go no go status of a smartcontract.

Advertisements vs. crypto mining

Salon, a news providing website, offers readers an option to see advertisements or to allow Salon to use their computer (browser) to mine crypto coins. (See Salon offers… article in CoinDesk).

I believe this offer is made when the website detects a viewer is using  ad blockers.


Tthe trend is clear, people, organizations and even governments are looking at blockchain technology to provide basic and advanced services around the world.

If anyone would is interested in providing a pre-paid Visa card via blockchains, please contact me. I’d like to help.

Now if I could just find my GPU’s at a decent price somewhere…

Speaking of advertising… RayOnStorage doesn’t use advertising. But blogging like this takes time and money. If anyone’s interested in helping fund this blog, please consider sending some BTC our way, even 0.0001 BTC would help.

Our BTC wallet address is:


Photo Credit(s): Blockchain and the public sector on

Unleash your design teams with single signon on

Understanding the difference between P2P and Client-server networks on LinkedIN

Blockgeek’s guide to smart contracts

AI reaches a crossroads

There’s been a lot of talk on the extendability of current AI this past week and it appears that while we may have a good deal of runway left on the machine learning/deep learning/pattern recognition, there’s something ahead that we don’t understand.

Let’s start with MIT IQ (Intelligence Quest),  which is essentially a moon shot project to understand and replicate human intelligence. The Quest is attempting to answer “How does human intelligence work, in engineering terms? And how can we use that deep grasp of human intelligence to build wiser and more useful machines, to the benefit of society?“.

Where’s HAL?

The problem with AI’s deep learning today is that it’s fine for pattern recognition, but it doesn’t appear to develop any basic understanding of the world beyond recognition.

Some AI scientists concede that there’s more to human/mamalian intelligence than just pattern recognition expertise, while others’ disagree. MIT IQ is trying to determine, what’s beyond pattern recognition.

There’s a great article in Wired about the limits of deep learning,  Greedy, Brittle, Opaque and Shallow: the Downsides to Deep Learning. The article says deep learning is greedy because it needs lots of data (training sets) to work, it’s brittle because step one inch beyond what’s it’s been trained  to do and it falls down, and it’s opaque because there’s no way to understand how it came to label something the way it did. Deep learning is great for pattern recognition of known patterns but outside of that, there must be more to intelligence.

The limited steps using unsupervised learning don’t show a lot of hope, yet

“Pattern recognition” all the way down…

There’s a case to be made that all mammalian intelligence is based on hierarchies of pattern recognition capabilities.

That is, at a bottom level  human intelligence consists of pattern recognition, such as vision, hearing, touch, balance, taste, etc. systems which are just sophisticated pattern recognition algorithms that label what we are hearing as Bethovan’s Ninth Symphony, tasting as grandma’s pasta sauce, and seeing as the Grand Canyon.

Then, at the next level there’s another pattern recognition(-like) system that takes all these labels and somehow recognizes this scene as danger, romance, school,  etc.

Then, at the next level, human intelligence just looks up what to do in this scene.  Almost as if we have a defined list of action templates that are what we do when we are in danger (fight or flight), in romance (kiss, cuddle or ?), in school (answer, study, view, hide, …), etc.  Almost like a simple lookup table with procedural logic behind each entry

One question for this view is how are these action templates defined and  how many are there. If, as it seems, there’s almost an infinite number of them, how are they selected (some finer level of granularity in scene labeling – romance but only flirting …).

No, it’s not …

But to other scientists, there appears to be more than just pattern recognition(-like) algorithms and lookup and act algorithms, going on inside our brains.

For example, once I interpret a scene surrounding me as in danger, romance, school, etc.,  I believe I start to generate possible action lists which I could take in this domain, and then somehow I select the one to do which makes the most sense in this situation or rather gets me closer to my current goal (whatever that is) in this situation.

This is beyond just procedural logic and involves some sort of memory system, action generative system, goal generative/recollection system, weighing of possible action scripts, etc.

And what to make of the brain’s seemingly infinite capability to explain itself…

Baby intelligence

Most babies understand their parents language(s) and learn to crawl within months after birth. But they haven’t listened to thousands of hours of people talking or crawled thousands of miles.  And yet, deep learning requires even more learning sets in order to label language properly or  learning how to crawl on four appendages. And of course, understanding language and speaking it are two different capabilities. Ditto for crawling and walking.

How does a baby learn to recognize these patterns without TB of data and millions of reinforcements (“Smile for Mommy”, say “Daddy”). And what to make of the, seemingly impossible to contain wanderlust, of any baby given free reign of an area.

These questions are just scratching the surface in what it really means to engineer human intelligence.


MIT IQ is one attempt to try to answer the question that: assuming we understand how to pattern recognition can be made to work well on today’s computers what else do we need to do to build a more general purpose intelligence.

There are obvious ethical questions on whether we want to engineer a human level of intelligence (see my Existential risks… post). Our main concern is what it does (to humanity) once we achieve it.

But assuming we can somehow contain it for the benefit of humanity, we ought to take another look at just what it entails.


Photo Credits:  Tech trends for 2017: more AI …., the Next Silicon Valley website. 

HAL from 2001 a Space Odyssey 

Design software test labeling… 

Exploration in toddlers…, Science Daily website

Atomristors, a new single (atomic) layer memristor

Read an article the other day about the “Atomristor: non-volatile resistance  switching in atomic sheets of transition metal dichalcogenides” (TMDs), an ACS publication. The article shows research that discovered an atomic sheet level version of a memristor. The device is an atomic sheet of TMD that is sandwiched between two (gold, silver or graphene) electrodes.

They refer to the device switching non-volatile resistance (NVR) from low to high or vice versa but from our perspective it could just as easily be considered a non-volatile device usable for memory, storage, or electronic circuitry.

Prior to this research, it was believed that such resistance switching could not be accomplished with single atomic, sub-nanometre (0.7nm) sized, sheet of material.

NVR atomristor technological properties

The researchers discovered that NVR switching can occur at different device temperatures, sheet areas, compliance current, voltage sweep rate, and layer thickness. In all five degrees of freedom were tested to show that  TMD atomristors had wide applicability and allowed for significant environmental and electronic variability.

Not only was the effect extremely versatile, the researchers identified multiple materials which could be used for the atomic sheet. In fact, TMD are a class of materials and they showed 4 different TMD materials that had the NVR effect.

Surprisingly, some TMD materials exhibited the NVR effect using unipolar voltages and some using bipolar voltages, and some could use both.

The researchers went a long way to showing that the NVR was due to the atomic sheet. In one instance they specifically used non-lithographic measures to fabricate the devices. This process utilized graphene manufacturing like methods to produce an atomic sheet ontop of gold foil and depositing another gold layer ontop of that.

But they also used standard fabrication techniques to build the atomristor devices as well. Using these different fabrication methods, they were able to show the NVR effect using different electrodes types, testing gold, silver, and graphene, all of which worked similarly.

The paper talked of using atomristors in a software defined radio, as a electronic circuit/cross bar switch, or as a memory/storage device. But they also indicated that it could easily be used in a neuromorphic computer as well, effectively simulating neuron like computations.

There’s much more information in the ACS article.

How does it compare to flash?

As compared to flash, atomristors NVR devices should be able to provide higher levels (bits per mm) of density. And due to the lower current (~1v) required for (bipolar) NVR setting, reading and resetting, there’s a lower probability of leakage of stored charges as they’re scaled down to nm sizes.

And of course it comes in 2d sheets, so it’s just as amenable to 3D arrays as NAND and 3DX is today. That means that as fabs start scaling 3D NAND up in layers, atomristor NVR devices should be able to follow their technology roadmap to be scaled up just as high.

Atomristor computers, storage or switch devices

Going from the “lab” to an IT shop is a multifaceted endeavour that takes a lot of time. There are many steps to needed to get to commercialization and many lab breakthroughs never make it that far because of complexity, economics, and other factors.

For instance, memristors were first proposed in 1971 and HP(E) researchers first discovered material that could produce the memristor effect in 2008. In March 2012, HRL fabricated the first memristor chip on CMOS. In Dec. 2017, >9 years later, at their Discover Conference, HPE showed off “The Machine”, a prototype of a memristor based computer to the public. But we are still waiting to see one on the market for sale.

That being said, memristor technologies didn’t exist before 2008, so the use of these devices in a computer took sometime to be understood. The fact that atomristors are “just” an extremely, thinner version of memristors should help it be get to market faster that original memristor technologies. But how much faster than 9-12 years is anyone’s guess.



Picture Credit(s): All graphics and pictures are from the article in ACS

Better landslide/avalanche/mudslide modeling

Read an article the other week from Scientific American on Looming Landslide Stokes Fears, … about the Rattlesnake Ridge landslide that’s taking place in Washington State in the US. Apparently there’s a fissure that has been slowly widening  and is -slowly causing a landslide in the area.

Of course, recently there’s been significant mudslides in Montecito near  Las Angeles, that have resulted in a number of deaths and destruction of many millions of dollars of property. Now mudslides and landslides are not exactly the same but my guess is by improving our understanding of landslides may also help us better understand mudslides and hopefully, provide a better way to predict the dangers inherent in both. Ditto for snow avalanches.

Science to the rescue

Geologist and geomorphologists from Washington State and the USGS  have been instrumenting Rattlesnake Ridge with over 70 GPS sensors. They are also following the landslide using LIDAR snapshots to map terrain movement to try to better understand that movement over time.

It appears that Rattlesnake Ridge is moving about 1.6 ft/week. There’s a small community at the bottom of the ridge, and in the event of a complete collapse, knowing where and when to evacuate can save lives.

The belief is that the landslide at Rattlesnake Ridge and elsewhere are the result of an interaction of subsurface materials that holds ground in place and surface material moving down the a mountain side. It is the interface between these two layers that determines the rapidity of the landslide and its direction.

Land/snow/mud slides occur all the time

There’s a website called the Watchers that records significant landslides around the world. Aside from Rattlesnake Ridge and Montecito, they list a significant snow avalanche in South East France that cut off a village of 151 people, floods and landslides in the Philippines resulting from hurricane Kai-Tak that killed 26 people, a massive mudslide in Southern Chile which left 3 dead, 15 missing, and a new lake forming in India  after the Gangotri glacier collapsed that rerouted a river flowing from the glacier melt, all of which occurred during December 2017.

Snow avalanches, mudslides, landslides, etc. are all similar activities involving matter moving down a mountainside. The extent, direction and rapidity of its movement, all depend on the surface topology and subsurface and surface materials present in an area.

Knowing when to call an evacuation of the area immediately in a destructive path of a land/mud/snow slide and knowing where that destructive path is going to be is what the team at Rattlesnake Ridge are trying to help find out.




Photo Credit(s): 2104[sic] Washington Landslide by USGS 

Fissure by Ronan Jouve

SR6 Mudslide by Washington State DoT

New techniques shed light on ancient codex & palimpsests

Read an article the other day from New York Times, A fragile biblical text gets a virtual read about an approach to use detailed CT scans combined with X-rays to read text on a codex (double sided, hand bound book) that’s been mashed together for ~1500 years.

How to read a codex

Dr. Seales created the technology and has used it successfully to read a small charred chunk of material that was a copy of the earliest known version of the Masoretic text, the authoritative Hebrew bible.

However, that only had text on one side. A codex is double sided and being able to distinguish between which side of a piece of papyrus or parchment was yet another level of granularity.

The approach uses X-ray scanning to triangulate where sides of the codex pages are with respect to the material and then uses detailed CT scans to read the ink of the letters of the text in space. Together, the two techniques can read letters and place them on sides of a codex.

Apparently the key to the technique was in creating software could model the surfaces of a codex or other contorted pieces of papyrus/parchment and combining that with the X-ray scans to determine where in space the sides of the papyrus/parchment resided. Then when the CT scans revealed letters in planar scans (space), they could be properly placed on sides of the codex and in sequence to be literally read.

M.910, an unreadable codex

In the article, Dr. Seales and team were testing the technique on a codex written sometime between 400 and 600AD that contained the Acts of the Apostles and one of the books of the New Testament and possibly another book.

The pages had been merged together by a cinder that burned through much of the book. Most famous codexes are named but this one was only known as M.910 for the 910th acquisition of the Morgan Library.

M.910 was so fragile that it couldn’t be moved from the library. So the team had to use a portable CT scanner and X-ray machine to scan the codex.

The scans for M.910 were completed this past December and the team should start producing (Coptic) readable pages later this month.

Reading Palimpsests

A palimpsests is a manuscript on which the original writing has been obscured or erased. Another article from UCLA Library News, Lost ancient texts recovered and published online,  that talks about the use of multi-wave length spectral imaging to reveal text and figures that have been erased or obscured from Sinai Palimpsests.  The texts can be read at and total 6800 pages in 10 languages.

In this case the text had been deliberately erased or obscured to reuse parchment or papyrus. The writings are from the 5th to 12th centuries.  The texts were located in St. Catherine’s Monastary and access to it’s collection of ancient and medieval manuscripts is considered 2nd only to that in the Vatican Library.


There are many damaged codexes scurried away in libraries throughout the world today but up until now they were mere curiosities. If successful, this new technique will enable scholars to read their text, translate them and make them available for researchers and the rest of the world to read and understand.

Now if someone could just read my WordPerfect files from 1990’s and SCRIPT/VS files from 1980’s I’d be happy.


Picture credit(s): From NY Times article by Nicole Craine 

Acts of apostles codex

From Sinai Palimpsests Project website

GPU growth and the compute changeover

Attended SC17 last month in Denver and Nvidia had almost as big a presence as Intel. Their VR display was very nice as compared to some of the others at the show.

GPU past

GPU’s were originally designed to support visualization and the computation to render a specific scene quickly and efficiently. In order to do this they were designed with 100s to now 1000s of arithmetically intensive (floating point) compute engines where each engine could be given an individual pixel or segment of an image and compute all the light rays and visual aspects pertinent to that scene in a very short amount of time. This created a quick and efficient multi-core engine to render textures and map polygons of an image.

Image rendering required highly parallel computations and as such more compute engines meant faster scene throughput. This led to todays GPUs that have 1000s of cores. In contrast, standard microprocessor CPUs have 10-60 compute cores today.

GPUs today 

Funny thing, there are lots of other applications for many core engines. For example, GPUs also have a place to play in the development and mining of crypto currencies because of their ability to perform many cryptographic operations a second, all in parallel

Another significant driver of GPU sales and usage today seems to be AI, especially machine learning. For instance, at SC17, visual image recognition was on display at dozens of booths besides Intel and Nvidia. Such image recognition  AI requires a lot of floating point computation to perform well.

I saw one article that said GPUs can speed up Machine Learning (ML) by a factor of 250 over standard CPUs. There’s a highly entertaining video clip at the bottom of the Nvidia post that shows how parallel compute works as compared to standard CPUs.

GPU’s play an important role in speech recognition and image recognition (through ML) as well. So we find that they are being used in self-driving cars, face recognition, and other image processing/speech recognition tasks.

The latest Apple X iPhone has a Neural Engine which my best guess is just another version of a GPU. And the iPhone 8 has a custom GPU.

Tesla is also working on a custom AI engine for its self driving cars.

So, over time, GPUs will have an increasing role to play in the future of AI and crypto currency and as always, image rendering.


Photo Credit(s): SC17 logo, SC17 website;

GTX1070(GP104) vs. GTX1060(GP106) by Fritzchens Fritz;

Intel 2nd Generation core microprocessor codenamed Sandy Bridge wafer by Intel Free Press

Quantum computer programming

I was on a vendor call last week and they were discussing their recent technological advances in quantum computing. During the discussion they mentioned a number of ways to code for quantum computers. The currently most popular one is based on the QIS (Quantum Information Software) Kit.

I went looking for a principle of operations on quantum computers. Ssomething akin to the System 360 Principles of Operations Manual that explained how to code for an IBM 360 computer. But there was no such manual.

Instead there is a paper, on the Open Quantum Assembly Language (QASM) that describes the Quantum computational environment and coding language used in QIS Kit.

It appears that quantum computers can be considered a special computational co-proccesor engine, operated in parallel with normal digital computation. This co-processor happens to provide a quantum simulation.

QASM coding

One programs a quantum computer by creating a digital program which describes a quantum circuit that uses qubits and quantum registers to perform some algorithm on those circuits. The quantum circuit can be measured to provide a result  which more digital code can interpret and potentially use to create other quantum circuits in a sort of loop.

There are four phases during the processing of a QIS Kit quantum algorithm.

  1. QASM compilation which occurs solely on a digital computer. QASM source code describing the quantum circuit together with compile time parameters are translated into a quantum PLUS digital intermediate representation.
  2. Circuit generation, which also occurs on a digital computer with access to the quantum co-processor. The intermediate language compiled above is combined with other parameters (available from the quantum computer environment) and together these are translated into specific quantum building blocks (circuits) and some classical digital code needed and used during quantum circuit execution.
  3. Execution, which takes place solely on the quantum computer. The system takes as input, the collection of quantum circuits defined above and runtime control parameters,and transforms these using a high-level quantum computer controller into low-level, real time instructions for the quantum computer building the quantum circuits. These are then executed and the results of the quantum circuit(s) execution creates a result stream (measurements) that can be passed back to the digital program for further  processing
  4. Post-Processing, which takes place on a digital computer and uses the results from the quantum circuit(s) execution and other intermediate results and processes these to either generate follow-on quantum circuits or output ae final result for the quantum algorithm.

As qubit coherence only last for a short while, so results from one execution of a quantum circuit cannot be passed directly to another execution of quantum circuits.  Thus these results have to be passed through some digital computations before they can be used in subsequent quantum circuits. A qubit is a quantum bit.

Quantum circuits don’t offer any branching as such.

Quantum circuits

The only storage for QASM are classical (digital) registers (creg) and quantum registers (qreg) which are an array of bits and qubits respectively.

There are limited number of built-in quantum operations that can be performed on qregs and qubits. One described in the QASM paper noted above is the CNOT   operation, which flips a qubit, i.e., CNOT alb will flip a qubit in b, iff a corresponding qubit in a is on.

Quantum circuits are made up of one or more gate(s). Gates are invoked with a set of variable parameter names and quantum arguments (qargs). QASM gates can be construed as macros that are expanded at runtime. Gates are essentially lists of unitary quantum subroutines (other gate invocations), builtin quantum functions or barrier statements that are executed in sequence and operate on the input quantum argument (qargs) used in the gate invocation.

Opaque gates are quantum gates whose circuits (code) have yet to be defined. Opaque gates have a physical implementation may yet be possible but whose definition is undefined. Essentially these operate as place holders to be defined in a subsequent circuit execution or perhaps something the quantum circuit creates in real time depending on gate execution (not really sure how this would work).

In addition to builtin quantum operations,  there are other statements like the measure  or  reset statement. The reset statement sets a qubit or qreg qubits to 0. The measure statement copies the state of a qubit or qreg into a digital bit or creg (digital register).

There is one conditional command in QASM, the If statement. The if statement can compare a creg against an integer and if equal execute a quantum operation. There is one “decision”  creg, used as an integer.  By using IF statements one can essentially construct a case statement in normal coding logic to execute quantum (circuits) blocks.

Quantum logic within a gate can be optimized during the compilation phase so that they may not be executed (e.g., if the same operation occurs twice in a gate, normally the 2nd execution would be optimized out) unless a barrier statement is encountered which prevents optimization.

Quantum computer cloud

In 2016, IBM started offering quantum computers in its BlueMix cloud through the IBM Quantum (Q)  Experience. The IBM Q Experience currently allows researchers access to 5- and 16-qubit quantum computers.

There are three pools of quantum computers: 1 pool called IBMQX5, consists of 8 16-qubit computers and 2 pools of 5 5-qubit computers, IBMQX2 and IBMQX4. As I’m writing this, IBMQX5 and IBMQX2 are offline for maintenance but IBMQX4 is active.

Google has recently released the OpenFermion as open source, which is another software development kit for quantum computation (will review this in another post). Although Google also seems to have quantum computers and has provided researchers access to them, I couldn’t find much documentation on their quantum computers.

Two other companies are working on quantum computation: D-Wave Systems and Rigetti Computing. Rigetti has their Forest 1.0 quantum computing full stack programming and execution environment but I couldn’t easily find anything on D-Wave Systems programming environment.

Last month, IBM announced they have  constructed a 50-Qubit quantum computer prototype.

IBM has also released 20-Qubit quantum computers for customer use and plans to offer the new 50-Qubit computers to customers in the future.


Picture Credit(s): Quantum Leap Supercomputer,  IBM What is Quantum Computing Website

QASM control flow, Open Quantum Assembly Language, by A. Cross, et al

IBM’s newly revealed 50-Qubit Quantum Processer …,  Softcares blog post

Magnonics for configurable electronics

Read an article today in ScienceDaily on [a] New way to write magnetic info … that discusses research done at Imperial College Of London that used a magnetic force microscope (small magnetic probe) to write magnetic fields onto a dense array of nanowires.

Frustrated metamaterials needed

The original research is written up in a Nature article Realization of ground state in artificial kagome spin ice via topological defect driven magnetic writing  (paywall). Unclear what that means but the paper abstract discusses geometrically frustrated magnetic metamaterials.  This is where the physical size or geometrical properties of the materials at the nanometer scale restricts or limits the magnetic states that material can exhibit.

Magnetic storage deals with magnetic material but there are a number of unique interactions of magnetic material when in close (nm) proximity to one another and the way nanowire geometrically frustrated magnetic metamaterials can be magnetized to different magnetic moments which can be exploited for other uses.  These interactions and magnetic moments can be combined to provide electronic circuitry and data storage.

I believe the research provides a proof point that such materials can be written, in close proximity to one another using a magnetic force microscope.

Why it’s important

The key is the potential to create  magnonic circuitry based on the pattern of moments writen into an array of nanowires. In doing so, one can fabricate any electrical circuit. It’s almost like photolithography but without fabs, chemicals, or laser scanners.

At first I thought this could be a denser storage device, but the potential is much greater if electronic circuitry could be constructed without having to fabricate semiconductors. It would seem ideal for testing out circuitry before manufacturing. And ultimately if it could be scaled up, the manufacture/fabrication of electronic circuitry itself could be done using these techniques.

Speed, endurance, write limits?

There was no information in the public article about the speed of writing the “frustrated magnetic metamaterials”. But an atomic force microscope can scan 150×150 micrometers in several minutes. If we assume that a typical chip size today is 150×150 mm, then this would take 1E6 times several minutes, or ~2K days. With multiple scanning force microscopes operating concurrently we could cut this down by a factor of 10 or 100 and maybe someday 1000. 2 days to write any electronic circuit on the order of todays 23nm devices with nanowires and magnetic force microscopes would be a significant advance

Also there was no mention of endurance, write limits or other characteristics we have learned to love with Flash storage. But the assumption is that it can be written multiple times and that the pattern stays around for some amount of time.

How magnetics generate electronic circuits

Neither Wikipedia page, the public article or the paywall articles’ abstract describes how Magnonics can supply electronic circuitry. However both the abstract and the public article discuss applications for this new technology in hardware based neural networks using arrays of densely packed nanowires.

Presumably, by writing different magnetic patterns in these nanowire metamaterials, such patterns can be used to simulate hardware connected neurons. This means that the magnetic information can be overwritten because it can be trained. Also, such magnetic circuits can be constructed to: a) can create different path for electrons to flow through the material; b) can restrict or enhance this electronic flow, and c) can integrate across a number of inputs and determine how electronic flow will proceed from a simulated neuron.

If magnonics can do all that,  it’s very similar to electronic gates today in CPU, GPUs and other electronic circuitry. Maybe it cannot simulate every gate or electronic device that’s found in todays CPUs but it’s a step in the right direction. And magnonics is relatively new. Silicon transistors are over 70 years old and the integrated circuit is almost 60 years old. So in time, magnonics could very well become the next generation of chip technology.

Writing speed is a problem. Maybe if they spun the nanowire array around the magnetic force microscope…


Photo Credits:  Real space observation of emergent magnetic monopoles … Nature article

Realization of ground state in artificial kagome spin ice via topological defect driven magnetic writing, Nature article