Speeding up memory with T-rays; memristors power artificial synapses; refracting electrons in graphene.
Speeding up memory with T-rays
Scientists at the Moscow Institute of Physics and Technology (MIPT), the University of Regensburg in Germany, Radboud University Nijmegen in the Netherlands, and Moscow Technological University proposed a way to improve the performance of memory through using T-waves, or terahertz radiation, as a means of resetting memory cells. This process is several thousand times faster than magnetic-field-induced switching.
“We have demonstrated an entirely new way of controlling magnetization, which relies on short electromagnetic pulses at terahertz frequencies. This is an important step towards terahertz electronics. As far as we know, our study is the first to make use of this mechanism to trigger the oscillations of magnetic subsystems,” said Anatoly Zvezdin of the Prokhorov General Physics Institute and MIPT.
To find out whether T-rays could be used for convenient memory states switching (storing “magnetic bits” of information), the researchers performed an experiment with thulium orthoferrite (TmFeO₃). As a weak ferromagnet, it generates a magnetic field by virtue of the ordered alignment of the magnetic moments, or spins of atoms in the microcrystals (magnetic domains). In order to induce a reorientation of spins, an external magnetic field is necessary.
However, the experiment showed that it is also possible to control magnetization directly by using terahertz radiation, which excites electronic transitions in thulium ions and alters the magnetic properties of both iron and thulium ions. Furthermore, the effect of T-rays proved to be almost ten times greater than that of the external magnetic field. In other words, the researchers have devised a fast and highly efficient remagnetization technique — a solid foundation for developing ultrafast memory.
The scientists expect their “T-ray switching” to work with other materials as well. Thulium orthoferrite, which was used in the experiment, happened to be convenient for the purposes of demonstration, but the team says the proposed magnetization control scheme itself is applicable to many other magnetic materials.
Memristors power artificial synapses
Researchers from the University of Southampton, Graz University of Technology, the Kirchhoff Institute for Physics, and Imperial College, London found that memristors could be used to power artificial systems that can mimic the human brain.
Artificial neural networks (ANNs) exhibit learning abilities and can perform tasks which are difficult for conventional computing systems, such as pattern recognition, on-line learning and classification. Practical ANN implementations are currently hampered by the lack of efficient hardware synapses, a key component that every ANN requires in large numbers.
To this end, the team demonstrated an ANN that used memristor synapses supporting sophisticated learning rules in order to carry out reversible learning of noisy input data.
Acting like synapses in the brain, their metal-oxide memristor array was capable of learning and re-learning input patterns in an unsupervised manner within a probabilistic winner-take-all network. This is extremely useful for enabling low-power embedded processors (such as for the IoT) that can process big data in real-time without any prior knowledge of the data.
Alex Serb of the University of Southampton said, “If we want to build artificial systems that can mimic the brain in function and power we need to use hundreds of billions, perhaps even trillions of artificial synapses, many of which must be able to implement learning rules of varying degrees of complexity. Whilst currently available electronic components can certainly be pieced together to create such synapses, the required power and area efficiency benchmarks will be extremely difficult to meet — if even possible at all — without designing new and bespoke ‘synapse components’.
“Memristors offer a possible route towards that end by supporting many fundamental features of learning synapses (memory storage, on-line learning, computationally powerful learning rule implementation, two-terminal structure) in extremely compact volumes and at exceptionally low energy costs. If artificial brains are ever going to become reality, therefore, memristive synapses have to succeed.”
Refracting electrons in graphene
A team from Columbia University and the University of Virginia observed a property of graphene which could lead to the development of new types of electron switches, based on the principles of optics rather than electronics.
The effect has electrons in graphene behaving like light rays, and subject to manipulation by optical devices such as prisms and lenses. In particular, the team was interested in negative refraction.
“The ability to manipulate electrons in a conducting material like light rays opens up entirely new ways of thinking about electronics,” said Cory Dean, assistant professor of physics at Columbia. “For example, the switches that make up computer chips operate by turning the entire device on or off, and this consumes significant power. Using lensing to steer an electron ‘beam’ between electrodes could be dramatically more efficient, solving one of the critical bottlenecks to achieving faster and more energy efficient electronics.”
Electrons travelling through very pure conductors can travel in straight lines like light rays, enabling optics-like phenomena to emerge. In materials, the electron density plays a similar role to the index of refraction, and electrons refract when they pass from a region of one density to another.
“Unlike in optical materials, where creating a negative index metamaterial is a significant engineering challenge, negative electron refraction occurs naturally in solid state materials at any p-n junction,” said James Hone, professor of mechanical engineering at Columbia Engineering,
While graphene has been widely explored for supporting high electron speed, it is notoriously hard to turn off the electrons without hurting their mobility. Avik Ghosh, professor of electrical and computer engineering at the University of Virginia, thinks that “the natural follow-up is to see if we can achieve a strong current turn-off in graphene with multiple angled junctions. If that works to our satisfaction, we’ll have on our hands a low-power, ultra-high-speed switching device for both analog (RF) and digital (CMOS) electronics, potentially mitigating many of the challenges we face with the high energy cost and thermal budget of present day electronics.”