Seminars and Events
Materials for Information Technologies
Coordinator: Harvey Amorín
16 June 2017, 12:00 h. Salón de Actos. Instituto de Cerámica y Vidrio
The Hall Effects Edwin Hall Never Imagined
Fudan University, China
The anomalous Hall effect (AHE) is one of the oldest and most prominent transport phenomena in magnetic materials. However, the microscopic mechanism of the AHE has remained unresolved for more than a century because its rich phenomenology defies standard classification, prompting conflicting claims of the dominant processes. We differentiate these processes through temperature-dependent measurements on epitaxial Fe, Ni, Co, and NixCu1-x films of varying thickness , . The results allow an unambiguous identification of both intrinsic and extrinsic mechanisms of the AHE. The more recently discovered spin Hall effect (SHE) has attracted a great deal of attention because of its potential applications in spin current devices. Various methods have been developed to generate and detect the SHE and search for materials with large spin Hall angles. These efforts notwithstanding, reliable and accurate determination of spin Hall angle remains a challenge. In this lecture I will first give a comprehensive discussion on the basic concepts of AHE and SHE. Exploiting the attributes of epitaxial magnetic thin films, I will then explain how to control independently the different scattering processes through temperature and layer thickness and to identify unambiguously the intrinsic and extrinsic mechanisms of the AHE. Finally, based on the understanding of the microscopic mechanisms of the AHE, I will describe how we developed a new method using H-patterned films to measure quantities inherent in the SHE.
 Phys. Rev. Lett. 103, 087206 (2009)
 Phys. Rev. Lett. 114, 217203 (2015)
22 May 2017, 12:00 h. Salón de Actos
Neuromorphic computing with spintronic nanoscale oscillators
Unite Mixte de Physique, CNRS/Thales, Palaiseau, France
The brain displays many signatures of non-linear dynamical networks, such as synchronization or complex transient behaviour [1,2]. These observations have inspired a whole class of models that harness the complex non-linear dynamical networks to perform neuro-inspired computing . In this framework, neurons are modelled as non-linear oscillators, and synapses as the coupling between oscillators. These abstract models are very good at processing waveforms for pattern recognition but there are very few hardware implementations based on networks of coupled oscillators. This type of computing requires a huge number of oscillators for achieving excellent performance and nanoscale oscillators are necessary for easy integration in a microchip. However small devices tend to be noisy and to lack the stability required to process data in a reliable way. Spin torque nano-oscillators are a promising solution to over this issue because their well-controlled magnetization dynamics can lead to high signal to noise ratios. In addition, the other main advantages of spintronic oscillators compared to others existing oscillators are their exceptional ability to interact, non-linear tunability, fast time response (ns range), long lifetime and lower power consumption . In this seminar, I will show different ways of leveraging the non-linear dynamics of spin-torque nano-oscillators for neuromorphic computing, and present our first experimental results of speech recognition .
 D.R. Chialvo, Nat. Phys. 6, 744 (2010).
 J.Fell and N.Axmacher, Nat.Rev.Neurosci. 12, 105 (2011).
 E.M.Izhikevich, IEEE Trans.NeuralNetworks 15, 1063 (2004).
 J.Grollier, D.Querlioz, and M.D.Stiles, Proc. IEEE 104, 2024 (2016).
 J. Torrejon et al, arXiv:1701.07715 (2017).