Memristors Retain Data 10 Years Without Power

The internet of things ( IoT) is coming, that much we know. But still it won’t; not until we have components and chips that can handle the explosion of data that comes with IoT. In 2020, there will already be 50 billion industrial internet sensors in place all around us. A single autonomous device – a smart watch, a cleaning robot, or a driverless car – can produce gigabytes of data each day, whereas an airbus may have over 10 000 sensors in one wing alone.

Two hurdles need to be overcome. First, current transistors in computer chips must be miniaturized to the size of only few nanometres; the problem is they won’t work anymore then. Second, analysing and storing unprecedented amounts of data will require equally huge amounts of energy. Sayani Majumdar, Academy Fellow at Aalto University (Finland), along with her colleagues, is designing technology to tackle both issues.

Majumdar has with her colleagues designed and fabricated the basic building blocks of future components in what are called “neuromorphiccomputers inspired by the human brain. It’s a field of research on which the largest ICT companies in the world and also the EU are investing heavily. Still, no one has yet come up with a nano-scale hardware architecture that could be scaled to industrial manufacture and use.

The probe-station device (the full instrument, left, and a closer view of the device connection, right) which measures the electrical responses of the basic components for computers mimicking the human brain. The tunnel junctions are on a thin film on the substrate plate.

The technology and design of neuromorphic computing is advancing more rapidly than its rival revolution, quantum computing. There is already wide speculation both in academia and company R&D about ways to inscribe heavy computing capabilities in the hardware of smart phones, tablets and laptops. The key is to achieve the extreme energy-efficiency of a biological brain and mimic the way neural networks process information through electric impulses,” explains Majumdar.

In their recent article in Advanced Functional Materials, Majumdar and her team show how they have fabricated a new breed of “ferroelectric tunnel junctions”, that is, few-nanometre-thick ferroelectric thin films sandwiched between two electrodes. They have abilities beyond existing technologies and bode well for energy-efficient and stable neuromorphic computing.

The junctions work in low voltages of less than five volts and with a variety of electrode materials – including silicon used in chips in most of our electronics. They also can retain data for more than 10 years without power and be manufactured in normal conditions.

Tunnel junctions have up to this point mostly been made of metal oxides and require 700 degree Celsius temperatures and high vacuums to manufacture. Ferroelectric materials also contain lead which makes them – and all our computers – a serious environmental hazard.

Our junctions are made out of organic hydro-carbon materials and they would reduce the amount of toxic heavy metal waste in electronics. We can also make thousands of junctions a day in room temperature without them suffering from the water or oxygen in the air”, explains Majumdar.

What makes ferroelectric thin film components great for neuromorphic computers is their ability to switch between not only binary states – 0 and 1 – but a large number of intermediate states as well. This allows them to ‘memoriseinformation not unlike the brain: to store it for a long time with minute amounts of energy and to retain the information they have once received – even after being switched off and on again.

We are no longer talking of transistors, but ‘memristors’. They are ideal for computation similar to that in biological brains.  Take for example the Mars 2020 Rover about to go chart the composition of another planet. For the Rover to work and process data on its own using only a single solar panel as an energy source, the unsupervised algorithms in it will need to use an artificial brain in the hardware.

What we are striving for now, is to integrate millions of our tunnel junction memristors into a network on a one square centimetre area. We can expect to pack so many in such a small space because we have now achieved a record-high difference in the current between on and off-states in the junctions and that provides functional stability. The memristors could then perform complex tasks like image and pattern recognition and make decisions autonomously,” says Majumdar.


Nanocomputer Packed Into a 50 Nanometers Block

In 1959 renowned physicist Richard Feynman, in his talk “Plenty of Room at the Bottom,” spoke of a future in which tiny machines could perform huge feats. Like many forward-looking concepts, his molecule and atom-sized world remained for years in the realm of science fiction. And then, scientists and other creative thinkers began to realize Feynman’s nanotechnological visions.

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


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

However, the continuing development and fabrication of progressively smaller components is bringing this virus-sized computing device closer to reality, said Dmitri Strukov, a UCSB professor of computer science.  “Our contribution is that we improved the specific features of that logic and designed it so it could be built in three dimensions,” he said.

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


Mimicking The Neural Networks Of The Human Brain

Researchers at the Centre for Research on Adaptive Nanostructures and Nanodevices (CRANN) at Trinity College in Dublin – Ireland – are pursuing a new nanomaterial-based approach to neural networks that combines work in nanowires and memristors (2-terminal memory devices based on resistance switching effects). They develop a new computing paradigm that mimics the neural networks of the human brain. Both nanowires and memristors are part of the history of research into neural networks and artificial intelligence (AI). Researchers have been investigating the use of nanowires in building electronic meshes on which nerve tissues can be grown; the mesh, they hope, could link nerve cells with electronics. And almost from the time memristors were first isolated and characterized, researchers have been looking at using them in chips that would lead to artificial intelligence.
Professor John Boland, director of CRANN, and his colleagues will be using the research grant to build on their previous work. They already discovered that when electricity—or other stimuli such as chemicals or light—is applied to a random network of nanowires, it generates a chemical reactions at the junctions where the nanowires cross over each other.

nanowire network
This phenomenon is similar to the way the brain works, in that there are bundles of nerves that cross over one another, forming junctions. Over time, the human brain begins to learn which of these junctions is important and discards the rest.