Tag Archives: Sayani Majumdar

Studying quantum conductance in memristive devices

A September 27, 2022 news item on phys.org provides an introduction to the later discussion of quantum effects in memristors,

At the nanoscale, the laws of classical physics suddenly become inadequate to explain the behavior of matter. It is precisely at this juncture that quantum theory comes into play, effectively describing the physical phenomena characteristic of the atomic and subatomic world. Thanks to the different behavior of matter on these length and energy scales, it is possible to develop new materials, devices and technologies based on quantum effects, which could yield a real quantum revolution that promises to innovate areas such as cryptography, telecommunications and computation.

The physics of very small objects, already at the basis of many technologies that we use today, is intrinsically linked to the world of nanotechnologies, the branch of applied science dealing with the control of matter at the nanometer scale (a nanometer is one billionth of a meter). This control of matter at the nanoscale is at the basis of the development of new electronic devices.

A September 27, 2022 Istituto Nazionale di Ricerca Metrologica (INRIM) press release (summary, PDF, and also on EurekAlert), which originated the news item, provides more information about the research,

Among these, memrisistors are considered promising devices for the realization of new computational architectures emulating functions of our brain, allowing the creation of increasingly efficient computation systems suitable for the development of the entire artificial intelligence sector, as recently shown by INRiM researchers in collaboration with several international universities and research institutes [1,2].

In this context, the EMPIR MEMQuD project, coordinated by INRiM, aims to study the quantum effects in such devices in which the electronic conduction properties can be manipulated allowing the observation of quantized conductivity phenomena at room temperature. In addition to analyzing the fundamentals and recent developments, the review work “Quantum Conductance in Memristive Devices: Fundamentals, Developments, and Applications” recently published in the prestigious international journal Advanced Materials (https://doi.org/10.1002/adma.202201248) analyzes how these effects can be used for a wide range of applications, from metrology to the development of next-generation memories and artificial intelligence.

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

Quantum Conductance in Memristive Devices: Fundamentals, Developments, and Applications by Gianluca Milano, Masakazu Aono, Luca Boarino, Umberto Celano, Tsuyoshi Hasegawa, Michael Kozicki, Sayani Majumdar, Mariela Menghini, Enrique Miranda, Carlo Ricciardi, Stefan Tappertzhofen, Kazuya Terabe, Ilia Valov. Advanced Materials Volume 34, Issue32 August 11, 2022 2201248 DOI: https://doi.org/10.1002/adma.202201248 First published: 11 April 2022

This paper is open access.

You can find the EMPIR (European Metrology Programme for Innovation and Research) MEMQuD (quantum effects in memristive devices) project here, from the homepage,

Memristive devices are electrical resistance switches that couple ionics (i.e. dynamics of ions) with electronics. These devices offer a promising platform to observe quantum effects in air, at room temperature, and without an applied magnetic field. For this reason, they can be traced to fundamental physics constants fixed in the revised International System of Units (SI) for the realization of a quantum-based standard of resistance. However, as an emerging technology, memristive devices lack standardization and insights into the fundamental physics underlying its working principles.

The overall aim of the project is to investigate and exploit quantized conductance effects in memristive devices that operate reliably, in air and at room temperature. In particular, the project will focus on the development of memristive model systems and nanometrological characterization techniques at the nanoscale level of memristive devices, in order to better understand and control the quantized effects in memristive devices. Such an outcome would enable not only the development of neuromorphic systems but also the realization of a standard of resistance implementable on-chip for self-calibrating systems with zero-chain traceability in the spirit of the revised SI.

I’m starting to see mention of ‘neuromorphic computing’ in advertisements (specifically a Mercedes Benz car). I will have more about these first mentions of neuromorphic computing in consumer products in a future posting.

New breed of memristors?

This new ‘breed’ of memristor (a component in brain-like/neuromorphic computing) is a kind of thin film. First, here’s an explanation of neuromorphic computing from the Finnish researchers looking into a new kind of memristor, from a January 10, 2018 news item on Nanowerk,

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, 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 “neuromorphic” computers 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.

An Aalto University January 10, 2018 press release, which originated the news item, provides more detail about the work,

“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.

Basic components for computers that work like the brain

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 ‘memorise’ information 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.

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. Photo: Tapio Reinekoski

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

Electrode Dependence of Tunneling Electroresistance and Switching Stability in Organic Ferroelectric P(VDF-TrFE)-Based Tunnel Junctions by Sayani Majumdar, Binbin Chen, Qi Hang Qin, Himadri S. Majumdar, and Sebastiaan van Dijken. Advanced Functional Materials Vol. 28 Issue 2 DOI: 10.1002/adfm.201703273 Version of Record online: 27 NOV 2017

© 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

This paper is behind a paywall.