Photochromic Dye Makes Up This Novel Optical Memristor

October 10, 2024 by Dan Maloney 20 Comments

Despite being much in the zeitgeist lately, we have to confess to still being a bit foggy about exactly what memristors are. The “mem” part of their name seems to be the important bit, implying a memory function, but the rest of the definition seems somewhat negotiable — enough so that you can make a memristor from a bit of photochromic dye.

Now, we’ll leave the discussion of whether [Markus Bindhammer]’s rather complex optical memory cell officially counts as a memristor to the comments below, and just go through the technical details here. The heart of this experimental device is a photochromic dye known as cis-1,2-dicyano-1,2-bis(2,4,5-trimethyl-3-thienyl)ethene, mercifully shortened to CMTE, which has the useful property of having two stable states. Transitioning from the open state to the closed state occurs when UV light shines upon it, while switching back to the closed state is accomplished with a pulse of green light. Absent the proper wavelength of light, both states are stable, making non-volatile information storage possible.

To accomplish this trick, [Markus] filled a quartz cuvette with a little CMTE-doped epoxy resin. Inside a light-tight enclosure, two lasers — one at 405 nm wavelength, the other at 532 nm — are trained on the cuvette through a dichroic mirror. On the other side of the CMTE resin, he placed a VEML7700 high-accuracy ambient light sensor. An Arduino Nano reads the light sensor and controls the lasers. Writing and erasing are accomplished by turning on the proper laser for a short amount of time; reading the state of the cell involves a carefully timed pulse from the 405 nm laser followed by a 532 nm pulse and watching the output of the sensor.

Is a one-bit memory device that uses a dye that goes for €300 per gram and a pair of laser diodes practical? Of course not, but it’s still pretty cool, and we appreciate all the effort and expense [Markus] went to with this one. Now, if you want some fuel for the “It’s not a memristor” fire, memristors might not even be a thing.

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Memristors Are Cool, Radiation-resistant Memristors Even Moreso

October 5, 2024 by Donald Papp 9 Comments

Space is a challenging environment for semiconductors, but researchers have shown that a specific type of memristor (the hafnium oxide memristor, to be exact) actually reacts quite usefully when exposed to gamma radiation. In fact, it’s even able to leverage this behavior as a way to measure radiation exposure. In essence, it’s able to act as both memory and a sensor.

Being able to resist radiation exposure is highly desirable for space applications. Efficient ways to measure radiation exposure are just as valuable. The hafnium oxide memristor looks like it might be able to do both, but before going into how that works, let’s take a moment for a memristor refresher.

A memristor is essentially two conductive plates between which bridges can be made by applying a voltage to “write” to the device, by which one sets it to a particular resistance. A positive voltage causes bridging to occur between the two ends, lowering the device’s resistance, and a negative voltage reverses the process, increasing the resistance. The exact formulation of a memristor can vary. The memristor was conceived in the 1970s by Leon Chua, and HP Labs created a working one in 2008. An (expensive) 16-pin DIP was first made available in 2015.

A hafnium oxide memristor is a bit different. Normally it would be write-once, meaning a negative voltage does not reset the device, but researchers discovered that exposing it to gamma radiation appears to weaken the bridging, allowing a negative voltage to reset the device as expected. Exposure to radiation also caused a higher voltage to be required to set the memristor; a behavior researchers were able to leverage into using the memristor to measure radiation exposure. Given time, a hafnium oxide memristor exposed to radiation, causing it to require higher-than-normal voltages to be “set”, eventually lost this attribute. After 30 days, the exposed memristors appeared to recover completely from the effects of radiation exposure and no longer required an elevated voltage for writing. This is the behavior the article refers to as “self-healing”.

The research paper has all the details, and it’s interesting to see new things relating to memristors. After all, when it comes to electronic components it’s been quite a long time since we’ve seen something genuinely new.

Investigating The Fourth Passive Component

September 27, 2023 by Bryan Cockfield 45 Comments

When first learning about and building electronic circuits, the first things all of us come across are passive components such as resistors, capacitors, and inductors. These have easily-understandable properties and are used in nearly all circuits in some way or another. Eventually we’ll move on to learning about active components like transistors, but there’s a fourth passive circuit component that’s almost never encountered. Known as the memristor, this mysterious device is not quite as intuitive as the other three, so [Andrew] created an Arduino shield to investigate their properties.

Memristors relate electric charge and magnetic flux linkage, which means that their resistance changes based on the current that passes through them. As their name implies, this means they have memory, and retain their properties even after power is removed. [Andrew] is testing three different memristors, composed of tungsten, carbon, and chromium, using this specialized test set. The rig is based on an Arduino Uno and has a few circuit components that can be used as references and generates data on the behavior of the memristors under various situations.

The memristors used here do exhibit expected behavior when driven with positive voltage signals, but did exhibit a large amount of variability when voltage was applied in a negative direction. [Andrew] speculates that using these devices for storage would be difficult and would likely require fairly bespoke applications for each type. But as the applications for these seemingly bizarre circuit components increase, we expect them to improve much like any other passive component.

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Memristor Computing On A Chip

August 2, 2019 by Al Williams 8 Comments

Memristors have been — so far — mostly a solution looking for a problem. However, researchers at the University of Michigan are claiming the first memristor-based programmable computer that has the potential to make AI applications more efficient and faster.

Because memristors have a memory, they can accumulate data in a way that is common for — among other things — neural networks. The chip has both an array of nearly 6,000 memristors, a crossbar array, along with analog to digital and digital to analog converters. In fact, there are 486 DACs and 162 ADCs along with an OpenRISC processor.

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Memristor May Be Fake News

August 23, 2018 by Al Williams 31 Comments

The fundamental passive components of electronics are the resistor, the capacitor, the inductor, and the oscillator, right? Actually, no, oscillators aren’t considered fundamental components because they aren’t linear. Resistors, capacitors, and inductors are also irreducible. That is, you can’t combine other passive components to model them unlike, say, a potentiometer. In the last few decades, though, we’ve heard of another fundamental component — the memristor. [Isaac Abraham] asserts, though, that the memristor isn’t a new fundamental component, but just an active device.

To support that premise [Isaac] builds a periodic table of devices showing how components map to changing voltages based on the time-varying property of charge. This shows that all the basic relationships are filled and that memristor actually covers a composition of passive components. This is similar in concept to [Strukov’s] diagram implying that a memristor is the fourth quadrant of a space defined by charge vs flux. However, using the properties of this periodic table [Isaac] argues against the fundamental nature of the memristor.

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Memristors On A Chip Solve Partial Differential Equations

August 4, 2018 by Al Williams 17 Comments

We were always taught that the fundamental passive components were resistors, capacitors, and inductors. But in 1971, [Leon Chua] introduced the idea of a memristor — a sort of resistor with memory. HP created one in 2008 and since then we haven’t really had the burning need to use one. In a recent Nature article, [Mohammed Zidan] and others discuss a 32 by 32 memristor array on a chip they call a memory processing unit. This analog computer on a chip is useful for certain kinds of operations that CPUs are historically not efficient at, including solving differential equations. Other applications include matrix operations used in things like machine learning and weather prediction. The paper is behind a paywall, although the usual places to find scholarly papers will probably have it soon.

There are several key ideas for using these analog elements for high-precision computing. First, the array is set up in a passive crossbar arrangement. In addition, the memristors are quantized so that different resistance values represent different numbers. For example, a memristor element that could have 16 different resistance values would allow it to operate as a base-16 digit.

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Building Memristors For Neural Nets

November 3, 2015 by Brian Benchoff 39 Comments

Most electronic components available today are just improved versions of what was available a few years ago. Microcontrollers get faster, memories get larger, and sensors get smaller, but we haven’t seen a truly novel component for years or even decades. There is no electronic component more interesting with more novel applications than the memristor, and now they’re available commercially from Knowm, a company that is on the bleeding edge of putting machine learning directly onto silicon.

The entire point of digital circuits is to store information as a series of ones and zeros. Memristors as well store information, but do so in a completely analog way. Each memristor changes its own resistance in response to the current going through it; ‘writing’ a positive voltage lowers the resistance, and ‘writing’ a negative voltage puts the device back into a high resistance state.

Cross section of the metal chalcogenide memristor. Source: Knowm.org

This new memristor is based on research done by [Dr. Kris Campbell] of Boise State University – the same researcher responsible for silver chalcogenide memristors we saw earlier this year. Like these earlier devices, the Knowm memristror is built using silver chalcogenide molecules. To lower the resistance of the memristor, a positive voltage ‘pulls’ silver ions into the metal chalcogenide layer. The silver ions stay in this chalcogenide layer until they are ‘pushed’ back with the application of a negative voltage. This gives the memristor it’s core functionality – being able to remember how much current has gone through it.

This technology is different from the first memristors made by HP in 2008, and has allowed Knowm to create functional memristors on silicon with a relatively high yield. Knowm is currently selling a ‘tier 3’ memristor part that only has two out of eight devices failing QC testing. A ‘tier 1’ part, with all eight memristors working, is available for $220 USD.

As for applications for this memristor, Knowm is using this technology in something they call Thermodynamic RAM, or kT-RAM. This is a small coprocessor that allows for faster machine learning than would be possible with a computer with a much more traditional architecture. This kT-RAM uses a binary tree layout with memristors serving as the links between nodes.

While it’s much too soon to say if a kT-RAM processor will be better or more efficient at performing machine learning tasks in real life, a machine learning coprocessor does have a faint echo of the machine learning silicon developed during the 80s AI renaissance. Thirty years ago, neural nets on a chip were created by a few companies around Boston, until someone realized these neural nets could be simulated on a desktop PC much more efficiently. The kT-RAM is somewhat novel and highly parallel, though, and with a new electronic component it could be just what is needed to push machine learning directly into silicon.

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