Tag Archives: Ivan Shorubalko

Memristors based on halide perovskite nanocrystals are more powerful and easier to manufacture

A March 8, 2023 news item on phys.org announces research from Swiss and Italian researchers into a new type of memristor,

Researchers at Empa, ETH Zurich and the Politecnico di Milano are developing a new type of computer component that is more powerful and easier to manufacture than its predecessors. Inspired by the human brain, it is designed to process large amounts of data fast and in an energy-efficient way.

In many respects, the human brain is still superior to modern computers. Although most people can’t do math as fast as a computer, we can effortlessly process complex sensory information and learn from experiences, while a computer cannot – at least not yet. And, the brain does all this by consuming less than half as much energy as a laptop.

One of the reasons for the brain’s energy efficiency is its structure. The individual brain cells – the neurons and their connections, the synapses – can both store and process information. In computers, however, the memory is separate from the processor, and data must be transported back and forth between these two components. The speed of this transfer is limited, which can slow down the whole computer when working with large amounts of data.

One possible solution to this bottleneck are novel computer architectures that are modeled on the human brain. To this end, scientists are developing so-called memristors: components that, like brain cells, combine data storage and processing. A team of researchers from Empa, ETH Zurich and the “Politecnico di Milano” has now developed a memristor that is more powerful and easier to manufacture than its predecessors. The researchers have recently published their results in the journal Science Advances.

A March 8, 2023 Swiss Federal Laboratories for Materials Science and Technology (EMPA) press release (also on EurekAlert), which originated the news item, provides details about what makes this memristor different,

Performance through mixed ionic and electronic conductivity

The novel memristors are based on halide perovskite nanocrystals, a semiconductor material known from solar cell manufacturing. “Halide perovskites conduct both ions and electrons,” explains Rohit John, former ETH Fellow and postdoctoral researcher at both ETH Zurich and Empa. “This dual conductivity enables more complex calculations that closely resemble processes in the brain.”

The researchers conducted the experimental part of the study entirely at Empa: They manufactured the thin-film memristors at the Thin Films and Photovoltaics laboratory and investigated their physical properties at the Transport at Nanoscale Interfaces laboratory. Based on the measurement results, they then simulated a complex computational task that corresponds to a learning process in the visual cortex in the brain. The task involved determining the orientation of light based on signals from the retina.

“As far as we know, this is only the second time this kind of computation has been performed on memristors,” says Maksym Kovalenko, professor at ETH Zurich and head of the Functional Inorganic Materials research group at Empa. “At the same time, our memristors are much easier to manufacture than before.” This is because, in contrast to many other semiconductors, perovskites crystallize at low temperatures. In addition, the new memristors do not require the complex preconditioning through application of specific voltages that comparable devices need for such computing tasks. This makes them faster and more energy-efficient.

Complementing rather than replacing

The technology, though, is not quite ready for deployment yet. The ease with which the new memristors can be manufactured also makes them difficult to integrate with existing computer chips: Perovskites cannot withstand temperatures of 400 to 500 degrees Celsius that are needed to process silicon – at least not yet. But according to Daniele Ielmini, professor at the “Politecnico di Milano”, that integration is key to the success for new brain-like computer technologies. “Our goal is not to replace classical computer architecture,” he explains. “Rather, we want to develop alternative architectures that can perform certain tasks faster and with greater energy efficiency. This includes, for example, the parallel processing of large amounts of data, which is generated everywhere today, from agriculture to space exploration.”

Promisingly, there are other materials with similar properties that could be used to make high-performance memristors. “We can now test our memristor design with different materials,” says Alessandro Milozzi, a doctoral student at the “Politecnico di Milano”. “It is quite possible that some of them are better suited for integration with silicon.”

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

Ionic-electronic halide perovskite memdiodes enabling neuromorphic computing with a second-order complexity by Rohit Abraham John, Alessandro Milozzi, Sergey Tsarev, Rolf Brönnimann, Simon C. Boehme, Erfu Wu, Ivan Shorubalko, Maksym V. Kovalenko, and Daniele Ielmini. Science Advances 23 Dec 2022 Vol 8, Issue 51 DOI: 10.1126/sciadv.ade0072

This paper is open access.

Swiss researchers, memristors, perovskite crystals, and neuromorphic (brainlike) computing

A May 18, 2022 news item on Nanowerk highlights research into making memristors more ‘flexible’, (Note: There’s an almost identical May 18, 2022 news item on ScienceDaily but the issuing agency is listed as ETH Zurich rather than Empa as listed on Nanowerk),

Compared with computers, the human brain is incredibly energy-efficient. Scientists are therefore drawing on how the brain and its interconnected neurons function for inspiration in designing innovative computing technologies. They foresee that these brain-inspired computing systems, will be more energy-efficient than conventional ones, as well as better at performing machine-learning tasks.

Much like neurons, which are responsible for both data storage and data processing in the brain, scientists want to combine storage and processing in a single type of electronic component, known as a memristor. Their hope is that this will help to achieve greater efficiency because moving data between the processor and the storage, as conventional computers do, is the main reason for the high energy consumption in machine-learning applications.

Researchers at ETH Zurich, Empa and the University of Zurich have now developed an innovative concept for a memristor that can be used in a far wider range of applications than existing memristors.

“There are different operation modes for memristors, and it is advantageous to be able to use all these modes depending on an artificial neural network’s architecture,” explains ETH Zurich postdoc Rohit John. “But previous conventional memristors had to be configured for one of these modes in advance.”

The new memristors can now easily switch between two operation modes while in use: a mode in which the signal grows weaker over time and dies (volatile mode), and one in which the signal remains constant (non-volatile mode).

Once you get past the first two paragraphs in the Nanowerk news item, you find the ETH Zurich and Empa May 18, 2022 press releases by Fabio Begamin, in both cases, are identical (ETH is listed as the authoring agency on EurekAlert), (Note: A link has been removed in the following),

Just like in the brain

“These two operation modes are also found in the human brain,” John says. On the one hand, stimuli at the synapses are transmitted from neuron to neuron with biochemical neurotransmitters. These stimuli start out strong and then gradually become weaker. On the other hand, new synaptic connections to other neurons form in the brain while we learn. These connections are longer-​lasting.

John, who is a postdoc in the group headed by ETH Professor Maksym Kovalenko, was awarded an ETH fellowship for outstanding postdoctoral researchers in 2020. John conducted this research together with Yiğit Demirağ, a doctoral student in Professor Giacomo Indiveri’s group at the Institute for Neuroinformatics of the University of Zurich and ETH Zurich.

Semiconductor known from solar cells

The memristors the researchers have developed are made of halide perovskite nanocrystals, a semiconductor material known primarily from its use in photovoltaic cells. “The ‘nerve conduction’ in these new memristors is mediated by temporarily or permanently stringing together silver ions from an electrode to form a nanofilament penetrating the perovskite structure through which current can flow,” explains Kovalenko.

This process can be regulated to make the silver-​ion filament either thin, so that it gradually breaks back down into individual silver ions (volatile mode), or thick and permanent (non-​volatile mode). This is controlled by the intensity of the current conducted on the memristor: applying a weak current activates the volatile mode, while a strong current activates the non-​volatile mode.

New toolkit for neuroinformaticians

“To our knowledge, this is the first memristor that can be reliably switched between volatile and non-​volatile modes on demand,” Demirağ says. This means that in the future, computer chips can be manufactured with memristors that enable both modes. This is a significance advance because it is usually not possible to combine several different types of memristors on one chip.

Within the scope of the study, which they published in the journal Nature Communications, the researchers tested 25 of these new memristors and carried out 20,000 measurements with them. In this way, they were able to simulate a computational problem on a complex network. The problem involved classifying a number of different neuron spikes as one of four predefined patterns.

Before these memristors can be used in computer technology, they will need to undergo further optimisation.  However, such components are also important for research in neuroinformatics, as Indiveri points out: “These components come closer to real neurons than previous ones. As a result, they help researchers to better test hypotheses in neuroinformatics and hopefully gain a better understanding of the computing principles of real neuronal circuits in humans and animals.”

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

Reconfigurable halide perovskite nanocrystal memristors for neuromorphic computing by Rohit Abraham John, Yiğit Demirağ, Yevhen Shynkarenko, Yuliia Berezovska, Natacha Ohannessian, Melika Payvand, Peng Zeng, Maryna I. Bodnarchuk, Frank Krumeich, Gökhan Kara, Ivan Shorubalko, Manu V. Nair, Graham A. Cooke, Thomas Lippert, Giacomo Indiveri & Maksym V. Kovalenko. Nature Communications volume 13, Article number: 2074 (2022) DOI: https://doi.org/10.1038/s41467-022-29727-1 Published: 19 April 2022

This paper is open access.

Bespoke (custom made) carbon nanotubes

Researchers have found a way to create single-walled carbon nanotubes (SWCNTs) that  are consistent and, hopefully, designed for specific applications if I’m reading the research rightly, (I have an embedded video in a March 15, 2013 posting which illustrates some of the issues with producing carbon nanotubes.) Getting back to this latest research, it suggests that we could order SWCNTs-on-demand. An Aug. 14, 2014 news item on Azonano provides more insight,

In future, it will be possible to specifically equip carbon nanotubes with properties which they need for electronic applications, for example. Researchers at Empa in Dübendorf/Switzerland and the Max Planck Institute for Solid State Research in Stuttgart [Germany] have succeeded for the first time in growing single-walled carbon nanotubes (CNTs) with only a single, prespecified structure.

The nanotubes thereby have identical electronic properties. The decisive trick here: The team has taken up an idea which originated from the Stuttgart-based Max Planck researchers and produced the CNT from custom-made organic precursor molecules. The researchers started with these precursor molecules and have built up the nanotubes on a platinum surface, as they report in the latest issue of the scientific journal Nature. Such CNTs could be used in future, for instance, in ultra-sensitive light detectors and very tiny transistors.

An Aug. 13, 2014 Max Planck Institute press release, which originated the news item, provides more detail,

For 20 years, material scientists working on the development of carbon nanotubes for a range of applications have been battling a problem – now an elegant solution is at hand. With their unusual mechanical, thermal and electronic properties, the tiny tubes with their honeycomb lattice of graphitic carbon have become the embodiment of nanomaterials. They could be used to manufacture the next generation of electronic and electro-optical components so that they are even smaller and with even faster switching times than before. But to achieve this, the material scientists must specifically equip the nanotubes with desired properties, and these depend on their structure. The production methods used to date, however, always result in a mixture of different CNTs. The team from Empa  and the Max Planck Institute for Solid State Research has now remedied the situation with a new production path for single-walled nanotubes.

Carbon nanotubes with the best possible varietal purity are in demand

With a diameter of around one nanometre, single-walled CNTs (SWCNTs) are deemed to be quantum structures; very tiny structural differences, in the diameter, for example, or in the orientation of the atomic lattice, can dramatically change the electronic properties: one SWCNT can be a metal, while one with a slightly different structure is semi-conducting. Correspondingly great is the interest in reliable methods for producing SWCNTs with the best possible varietal purity. Researchers working with Martin Jansen, Director Emeritus at the Max Planck Institute for Solid State Research, have been pursuing suitable concepts for the synthesis for ten years. But it is only now that the surface physicists at Empa and the chemists at the Stuttgart-based Max Planck Institute have succeeded in implementing one of these ideas in the laboratory. The researchers allowed structurally identical SWCNTs to grow on a platinum surface in a self-organised process and were able to unambiguously define their electronic properties.

The Max Planck research team headed by Martin Jansen had the idea of starting with small precursor molecules to synthesise carbon nanotubes. They felt it should be possible to achieve controlled conversion of the precursor molecules into a cap which acts as the seed for a SWCNT and thus unambiguously specify the structure of the nanotube. With this concept, they approached the Empa team working with Roman Fasel, head of Empa’s «nanotech@surfaces» department and titular professor at the Department of Chemistry and Biochemistry of the University of Bern. This group has already been working for some time on how molecules on a surface can be converted or combined into complex nanostructures according to the principle of molecular self-organisation. “The challenge now consists in finding the right precursor molecule which would actually grow on a smooth surface,” says Roman Fasel. This was ultimately achieved by Andreas Mueller and Konstantin Amsharov from the Max Planck Institute in Stuttgart with the synthesis of a hydrocarbon molecule from a not-inconsiderable 150 atoms.

Molecular origami on the platinum surface

What exactly is the process in which the carbon nanotube forms? In the first step, the flat precursor molecule must – as is the case in origami – convert into a three-dimensional object, the seed. This takes place on a hot platinum surface with the aid of a catalytic reaction, whereby hydrogen atoms split off from the precursor molecule and form new carbon-carbon bonds at very specific positions. The seed folds up from the flat molecule: a tiny, domed shape with open rim, which sits on the platinum surface. This so-called end cap forms the top of the growing SWCNT.

In a second chemical process, further carbon atoms, which are formed during the catalytic decomposition of ethanol on the platinum surface, are taken up. They deposit on the open rim between end cap and platinum surface and lift the cap higher and higher; the tube slowly grows upwards. The atomic structure of the nanotube is determined solely by the shape of the seed. The researchers proved this by analysing the vibrational modes of the SWCNTs and taking measurements with the scanning tunnelling microscope. Further investigations at Empa showed that the SWCNTs produced were over 300 nanometres in length.

Different nanotubes are formed from suitable precursor molecules

The researchers have thus proved that they can unambiguously specify the growth and thus the structure of long SWCNTs using custom-made molecular seeds. The SWCNTs synthesised in this study can exist in two forms, which correspond to an object and its mirror image. By choosing the precursor molecule appropriately, the researchers were able to influence which of the two variants forms. Depending on how the honeycomb atomic lattice is derived from the original molecule – straight or oblique with respect to the CNT axis – it is also possible for helically wound tubes, i.e. with right- or left-handed rotation, and with non-mirror symmetry to form. And it is precisely this structure that then determines which electronic, thermo-electric and optical properties of the material. In principle, the researchers can therefore specifically produce materials with different properties through their choice of precursor molecule.

In further steps, Roman Fasel and his colleagues want to gain an even better understanding of how SWCNTs establish themselves on a surface. Even if well in excess of 100 million nanotubes per square centimetre already grow on the platinum surface, only a relatively small fraction of the seeds actually develop into «mature» nanotubes. The question remains as to which processes are responsible for this, and how the yield can be increased.

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

Controlled synthesis of single-chirality carbon nanotubes by Juan Ramon Sanchez-Valencia, Thomas Dienel, Oliver Gröning, Ivan Shorubalko, Andreas Mueller, Martin Jansen, Konstantin Amsharov, Pascal Ruffieux, & Roman Fasel. Nature 512, 61–64 (07 August 2014) doi:10.1038/nature13607

Published online 06 August 2014

This paper is behind a paywall.