Transistors cannot enable very low-power IoT, could memristors do the job?

Transistors cannot enable very low-power IoT, could memristors do the job?

Technology News |
The Internet of Things is on the horizon but handling the explosion of data that will follow poses a huge challenge, with predictions of 50 billion industrial internet sensors in place by 2020. Not all these sensors are low data rate – 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.
By Christoph Hammerschmidt

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To build the IoT as envisioned by the the industry, power as well as data storage and analysis are key. Transistor-based technology faces two huge hurdles. First, current transistors in computer chips must be miniaturized to the size of only few nanometres – but at this line width they dont work anymore. Second, analysing and storing unprecedented amounts of data will require equally huge amounts of energy.

Sayani Majumdar, Academy Fellow at Aalto University, along with her colleagues, is designing technology to tackle both issues and create the basic building blocks for the components that will comprise future “neuromorphic” computers. It’s a field of research on which the largest ICT companies in the world and also the EU are investing heavily.

“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 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. Image courtesy of Tapio Reinekoski.

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 is 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 ‘memorise’ information not unlike the brain as well as 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’, that are ideal for computation similar to that in biological brains. In effect, easily printable, organic thin films that can retain data for more than 10 years without power and work with low voltages could become the building block of future computers that mimic the human brain

“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.

www.aalto.fi/en

Refrerence

‘Electrode Dependence of Tunneling Electroresistance and Switching Stability in Organic Ferroelectric P(VDF-TrFE)-Based Tunnel Junctions’: Sayani Majumdar, Binbin. Chen, Qi Hang Qin, Himadri. S. Majumdar, Sebastiaan van Dijken, Advanced Functional Materials 2017, 1703273.

https://doi.org/10.1002/adfm.201703273

 

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