News and posts

Time: Friday, October 12th, 10:10
Speaker: Amalio FERNANDEZ-PACHECO, University of Cambridge

Three-dimensional nanomagnetism is a new and exciting area of research focused on investigating nanomagnets that extend beyond the standard planar configuration [1]. In these systems, with unconventional geometries and spin interactions, new physical effects emerge, with geometry, topology and chirality becoming interlinked, which paves the way to novel devices with functionalities beyond the substrate plane. Specifically, future non-volatile 3D devices are expected to have ultra-high storage densities and very high interconnectivity, of great interest for future neuromorphic computing devices [2].
The leap to 3D is extremely complex from a fabrication and characterisation point of view, but it is starting to become possible thanks to new nanotechnology tools suitable to create and probe 3D geometries and spin configurations [3]. In this talk, I will review the state of the art of 3D nanomagnetism and will present some of our recent work on 3D magnetic nanostructures for applications in spintronics.
The introduction of novel 3D nano-printing methods based on focused electron beams is allowing us for the first time to prototype complex 3D nanomagnets [4-6]. In particular, the exploitation of auto-catalytic effects can be used to create diameter-modulated suspended nanowires [7], where advanced X-ray magnetic microscopy experiments reveal the presence of skyrmionic tubes during their magnetic reversal, in agreement with recent theoretical predictions [8]. Additionally, we have successfully injected domain walls from the substrate plane onto 3D Permalloy “ramped” nanowires interconnected to the substrate [9], a pioneering work comprising the development of a new 3D magneto-optical nano-magnetometry method exploiting dark-field effects. This work opens an exciting new route to advanced operation of 3D domain wall conduits.
I acknowledge funding from EPSRC grants EP/M008517/1 and EP/L015978/1, from the Cambridge Winton Program for the Physics of Sustainability, the Royal Society and EU funding via the COST action CELINA.
[1] Fernández-Pacheco et al, Nature Comm. 8, 15756 (2017).
[2] Torrejón et al, Nature 547, 428 (2017).
[3] Donnelly et al, Nature 547, 328 (2017).
[4] Sanz-Hernández et al, Beilstein J. Nanotechnol. 8, 2151 (2017).
[5] Fowlkes et al, ACS Appl. Nano Mater. 1, 1028 (2018).
[6] Sanz-Hernández et al, Nanomaterials 8, 483 (2018).
[7] Pablo-Navarro et al. J. Phys. D: Appl. Physics 50, 18LT01 (2017).
[8] Charilaou et al, arXiv:1711.03511 (2017).
[9] Sanz-Hernández et al, ACS Nano 11, 11066 (2017).

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Time: Friday, October 12th, 09:00
Speaker: Kerem CAMSARI, Purdue University

Digital computers are built out of bits that represent information in deterministic states 0 and 1. At the other end, quantum computers are built out of q-bits that represent information as delicate superpositions of 0 and 1. In this talk, I will introduce our body of work on probabilistic bits (p-bit) that randomly fluctuate between 0 and 1, placing p-bits conceptually in between deterministic and quantum bits. We have shown that hardware p-bits can be realized with present day, room temperature devices using magnetic and non-magnetic components and p-circuits built of interconnected p-bits can be useful for a wide variety of real world applications that are inherently probabilistic, just as quantum bits are naturally suited for inherently quantum mechanical problems.

We have shown that p-circuits can be broadly relevant for Quantum Computing and Machine Learning inspired problems. In the context of Machine Learning, p-bits can function as low-level representations of Binary Stochastic Neurons (BSN), therefore they can be used to realize efficient hardware implementations for Bayesian and Inference Networks, as well as hardware accelerators for BSN-based statistical learning algorithms. In the context of Quantum Computing, p-bits can be used for optimization problems, covering both classical and quantum annealing, to solve hard problems such as Traveling Salesman and Integer Factorization. Inverse problems such as Integer Factorization are enabled by the "invertibility" feature of p-circuits, where a logic gate not only finds the appropriate output for a set of fixed inputs but also the appropriate inputs for a fixed set of outputs. I will illustrate each application by a representative example either by using experimentally benchmarked device models simulated in circuit simulators such as SPICE or by hardware implementations of p-circuits using non-magnetic building blocks.

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Time: Thursday, October 11th, 15:30
Speaker: Alice MIZRAHI, NIST

Magnetic tunnel junctions are bi-stable nanodevices which magnetic state can be both read and written electrically. Their high endurance, reliability and CMOS-compatibility have made them flagship devices for novel forms of computing. While they are mostly used as non-volatile binary memories, they can be made unstable and thus behave as stochastic oscillators. Here, we show how stochastic magnetic tunnel junctions are promising elements for low energy implementations of unconventional computing. In this goal, we present several uses of these devices.
First, an analogy can be drawn between stochastic magnetic tunnel junctions and stochastic spiking neurons. We apply neuroscience computing paradigm to these devices and demonstrate that they can be the building blocks of low energy artificial neural networks capable of on-chip learning.
Then, we demonstrate that these stochastic oscillators are capable of harnessing noise to synchronize on a source at much lower energy consumption than deterministic oscillators. This opens the way to low energy implementations of synchronization based computing.
Finally, we show that these devices are low energy true random number generators, which can be used for various applications such as cryptography and stochastic computing.

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Time: Thursday, October 11th, 14:00
Speaker: Tara HAMILTON, Western Sydney University

In this tutorial we will explore two aspects of Stochastic Computing Hardware: Stochastic Electronics and Stochastic Computation. Stochastic Electronics is based on the idea that “noise” (in all its forms) can be used constructively to improve computational performance in “brain-like” processors. Here I will show several examples of how stochasticity can enhance performance while also reducing power consumption and circuit footprint. The other idea, Stochastic Computation, dates back to the 1960s and provides an alternative to conventional binary representations of information in digital systems. Stochastic computation can significantly decrease the computational cost of traditionally high-cost digital operations such as multiplication. Both stochastic electronics and stochastic computation are related ideas and both are based on the probabilistic nature of neural processing. Throughout this tutorial I will discuss how these ideas can (and have) been leveraged by Spintronics.

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Time: Thursday, October 11th, 11:10
Speaker: George BOURIANOFF, Intel Corporation – retired

Artificial Intelligence (AI) related hardware and software products are projected to be the fastest growing segment of the semiconductor and microelectronic industries with projected compound annual growth rates exceeding 100% per year for the next 10 years. Reservoir Computing (RC) is one promising computational approach that enables use of naturally occurring dynamic systems for AI applications. It is a type of recursive neural network commonly used for recognizing and predicting spatial-temporal events relying on a complex hierarchy of nested feedback loops to generate a memory functionality. The RC paradigm does not require any knowledge of the reservoir topology or node weights and can therefore utilize naturally existing networks formed by a wide variety of physical processes. Most efforts prior to this have focused on utilizing memristor or optical techniques to implement recursive neural networks. This presentation examines the potential of magnetic skyrmion fabrics and the complex current patterns which form in them as an attractive physical instantiation for Reservoir Computing. We present new results showing that application of 100mV, 1GHz pulse trains of either square pulse or sinusoidal pulses will generate a strong dynamic response of the skyrmion fabric of approximately 4%. The response is observed through a dynamically varying magnetoresistance with similar time dependence strongly suggesting that the applied signal induces a magneto-dynamic response in the fabric. We hypothesize that the strong magneto-resistive response provides the basis for full RC functionality.

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Time: Thursday, October 11th, 10:10
Speaker: Mark STILES, NIST

Human brains can solve many problems with orders of magnitude more energy efficiency than traditional computers. As the importance of such problems, like image, voice, and video recognition increases, so does the drive to develop computers that approach the energy efficiency of the brain. Progress must come on many fronts ranging from new algorithms to novel devices that are optimized to function in ways more suited to these algorithms than the digital transistors that have been optimized for the present approaches to computing. Magnetic tunnel junctions have several properties that make them attractive for such applications. They are actively being developed for integration into CMOS integrated circuits to provide non-volatile memory. This development makes it feasible to consider other geometries that have different properties. By changing the shape of the devices, they can be non-volatile binary devices, thermally unstable superparamagnetic binary devices, and non-linear oscillators. In this talk, I describe using magnetic tunnel junctions that are non-linear oscillators as the basis for reservoir computing. Reservoir computing uses recurrent neural networks to compute problems like voice recognition. Due to their state dependence, recurrent neural networks can be quite difficult to train. In reservoir computing, the training is simplified by specifying the input weights, letting the internal weights of the network take their natural values, and training only the output weights. A further simplification is to use a single device as the reservoir by using time multiplexing, the natural fading memory of the device, and external feedback. Testing this approach with standard datasets shows that this simplified approach can achieve state of the art results with a nanoscale reservoir.

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Time: Thursday, October 11th, 09:00
Speaker: Daniel BRUNNER, Femto-ST

I will present fully-implemented large scale recurrent photonic neural networks. We photonically created parallel connections for up to2025 nonlinear oscillators and realized efficiently converging photonic reinforcement learning. The system is applied to a chaotic signal prediction benchmark.

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Time: Wednesday, October 10th, 15:30
Speaker: Philipp PIRRO, TU Kaiserslautern

Today’s computational technology based on CMOS has experienced enormous scaling of data processing capability as well as of price and energy consumption per logic element. However, to continue this development successfully into the future, and with the rapid development of artificial intelligence and neural networks in mind, complementary approaches to conventional logic schemes are needed. One of these alternative routes is wave-based computing, which, however, suffered longtime from the lack of a down-scalable system which could be interconnected with conventional CMOS technology. In this context, spin waves, the elementary excitations of the spin system and their quanta, the magnons, have been intensively investigated and successfully brought to the micro- and nanoscale. Also, the connections to conventional electronic and spintronics circuits have been established within a new field known as magnon spintronics. Due to their large variety of intrinsic linear and nonlinear wave phenomena, spin waves constitute a promising candidate for nanoscaled wave-based computing and data processing in general.
We will first discuss the different computing approaches based on (spin-) waves and the advantages and challenges of an interference-based logic. Then, we present a selection of experimentally realized (macroscopic) prototypes for spin-wave based Boolean logic like the majority gate and the magnon transistor. To show the potential of advanced nanoscopic devices, micromagnetic simulations demonstrating the working principles of integrated magnonic circuits are presented. Inspired by the hybrid analog and digital data processing structure of biological brains, we use the unique properties of these circuits to develop an approach to realize neuromorphic computing based on spin waves.

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Time: Wednesday, October 10th, 14:00
Speaker: Laura HEYDERMAN, ETH Zurich

Artificial spin systems [1] have received much attention in recent years, following the creation of arrays of dipolar-coupled nanomagnets with frustrated geometries analogous to that of the rare earth titanate pyrochlores [2], which are referred to as spin ices due to a geometric frustration that leads to a spin arrangement analogous to the proton ordering in water ice [3]. Such arrays of nanomagnets are appropriately named artificial spin ice [4] where single-domain elongated magnets, which have Ising-like moments pointing along one of two directions, are commonly placed on the sites of a square or kagome lattice. At every vertex where the magnets meet, there is a characteristic low energy configuration where the so-called ice rule is obeyed. Going beyond these basic designs, it is possible to devise artificial spin systems with various intricate motifs that display behaviour that is characteristic of a particular geometry.
In this tutorial, I will introduce this topic with the aim to provide the link between artificial spin ice and computation by covering a number of possibilities for control. For example, the creation and separation of emergent magnetic monopoles and their associated Dirac strings on application of a magnetic field can be controlled by modifying the shape of particular nanomagnets in the array [5]. Such modifications to individual nanomagnets can also be used to control the chirality of artificial kagome spin ice building blocks consisting of a small number of hexagonal rings of magnets [6]. Indeed, chirality control is a recurring theme, with a further example being the control of the dynamic chirality, which can be achieved in the so-called chiral ice [7], where the stray field energy associated with the magnetic configurations at the edges of the array defines the sense of rotation of the average magnetisation on thermal relaxation. In addition, the chirality of domain walls will determine how they pass through a connected artificial kagome spin ice [8].
I will address other possibilities to control and access magnetic information in artificial spin ice including further geometries, temperature, local magnetic and electric fields, fast dynamics, as well as methods for computation [9].

[1] L.J. Heyderman and R.L. Stamps, J. Phys.: Condens. Matter 25, 363201 (2013)
[2] R.F. Wang et al. Nature 439, 303 (2006)
[3] M.J. Harris et al. Phys. Rev. Letts. 79, 2554 (1997)
[4] C. Nisoli, R. Moessner and P. Schiffer, Rev. Mod. Phys. 85, 1473 (2013)
[5] E. Mengotti et al. Nat. Phys. 7, 68 (2011); R.V. Hügli et al. Phil. Trans. Roy. Soc. A 370, 5767 (2012)
[6] R.V. Chopdekar et al. New Journal of Physics 15, 125033 (2013)
[7] S. Gliga et al. Nat. Mater. 16, 1106 (2017)
[8] K. Zeissler et al. Sci. Rep. 3, 1252 (2013); A. Pushp et al. Nat. Phys. 9, 505 (2013)
[9] H. Arava et al. Nanotechnology 29, 265205 (2018); P. Gypens et al. Phys. Rev. Appl. 9, 034004 (2018)

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Time: Wednesday, October 10th, 11:10
Speaker: Shunsuke FUKAMI, Tohoku University

Neuromorphic computing has attracted great attention because of its capability to execute complex tasks that the conventional von Neumann computers cannot readily complete. Here, we present an analog spintronic device based artificial neural network. Spintronic devices, in general, offer non-volatility and virtually infinite endurance, showing promise for realization of low-power “edge” neuromorphic computing hardware with online learning capability. An antiferromagnet-ferromagnet heterostructure operated by spin-orbit torque employed here allows us to control the magnetization state in an analog manner and thus can be used as an artificial synapse [1]. Using the developed artificial neural network with the analog spintronic device, we show a proof-of-concept demonstration of an associative memory operation based on the Hopfield model, a representative model of neuromorphic computing [2].
This work is partly supported by R&D Project for ICT Key Technology of MEXT, JSPS KAKENHI No. 17H06093, JST-OPERA, and ImPACT Program of CSTI.
[1] S. Fukami et al., Nature Materials, vol. 15, 535 (2016).
[2] W. A. Borders et al., Appl. Phys. Express, vol. 10, 013007 (2017).

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