Coherent control of correlated nanodevices: A hybrid time-dependent numerical renormalization-group approach to periodic switching

The time-dependent numerical renormalization-group approach (TD-NRG), originally devised for tracking the real-time dynamics of quantum-impurity systems following a single quantum quench, is extended to multiple switching events. This generalization of the TD-NRG encompasses the possibility of periodic switching, allowing for coherent control of strongly correlated systems by an external time-dependent field. To this end, we have embedded the TD-NRG in a hybrid framework that combines the outstanding capabilities of the numerical renormalization group to systematically construct the effective low-energy Hamiltonian of the system with the prowess of complementary approaches for calculating the real-time dynamics derived from this Hamiltonian. We demonstrate the power of our approach by hybridizing the TD-NRG with the Chebyshev expansion technique in order to investigate periodic switching in the interacting resonant-level model. Although the interacting model shares the same low-energy fixed point as its noninteracting counterpart, we surprisingly find the gradual emergence of damped oscillations as the interaction strength is increased. Focusing on a single quantum quench and using a strong-coupling analysis, we reveal the origin of these interaction-induced oscillations and provide an analytical estimate for their frequency. The latter agrees well with the numerical results.Comment: 20 pager, Revtex, 10 figures, submitted to Physical Review

Molybdenum chalcohalide nanowires as building blocks of nanodevices

Molybdenum chalcohalide nanowires are systems, which structural, electronic and optical properties have been analyzed in detail. However, their potential as building blocks for electronic devices has not been investigated so far. This question is raised in Dissertation, focusing on unique electronic transport properties of these systems, and comparing them with those of the popular carbon nanotubes

Memristors for the Curious Outsiders

We present both an overview and a perspective of recent experimental advances and proposed new approaches to performing computation using memristors. A memristor is a 2-terminal passive component with a dynamic resistance depending on an internal parameter. We provide an brief historical introduction, as well as an overview over the physical mechanism that lead to memristive behavior. This review is meant to guide nonpractitioners in the field of memristive circuits and their connection to machine learning and neural computation.Comment: Perpective paper for MDPI Technologies; 43 page

Wake-Up Transceiver Architectures with Novel Symbol Time Synchronization Schemes for ElectroMagnetic NanoNetworks

Projecte final de carrera fet en col.laboració amb Georgia Institute of TechnologyEnglish: The work we present on this thesis covers unresolved PHY-Layer topics for Electromagnetic Nanonetworks that covers the transceiver architecture, frequency estimation for symbol synchronization and a wake-up receiver module for node synchronziation. Solving these PHY-Layer topics leads to stablish a base to propose and design network protocols on top of a well-defined architecture.Castellano: El trabajo que se presenta en este proyecto cubre temas de capa física no resueltos para Electromagnetic Nanonetworks. Se propone una arquitectura de transceiver, la estimación de frecuencia para la sincronización del tiempo de símbolo y además se propone un modulo wake-up para sincronización entre nodos. Solucionar estos temas de capa física propone establecer una base para el diseño de futuros protocols de redes diseñados sobre bajo dichas consideracionesCatalà: El traball presentat cobreix temes de capa física no resolts per a Electromagnetic Nanonetworks. Es proposa una arquitectura de transceiver, una estimació de freqüència per a la sincronització del temps de símbol i un mòdul wake-up per a sincronització entre nodes. Donant solució a aquests temes de capa física permet establir una base per al diseny de futurs protocols de xarxes dissenyats sota aquestes consideracions

KITE : high-performance accurate modelling of electronic structure and response functions of large molecules, disordered crystals and heterostructures

We present KITE, a general purpose open-source tight-binding software for accurate real-space simulations of electronic structure and quantum transport properties of large-scale molecular and condensed systems with tens of billions of atomic orbitals (N ∼ 10^10). KITE’s core is written in C++, with a versatile Python-based interface, and is fully optimized for shared memory multi-node CPU architectures, thus scalable, efficient and fast. At the core of KITE is a seamless spectral expansion of lattice Green’s functions, which enables large-scale calculations of generic target functions with uniform convergence and fine control over energy resolution. Several functionalities are demonstrated, ranging from simulations of local density of states and photo-emission spectroscopy of disordered materials to large-scale computations of optical conductivity tensors and real-space wave-packet propagation in the presence of magneto-static fields and spin–orbit coupling. On-the-fly calculations of real-space Green’s functions are carried out with an efficient domain decomposition technique, allowing KITE to achieve nearly ideal linear scaling in its multi-threading performance. Crystalline defects and disorder, including vacancies, adsorbates and charged impurity centres, can be easily set up with KITE’s intuitive interface, paving the way to user-friendly large-scale quantum simulations of equilibrium and non-equilibrium properties of molecules, disordered crystals and heterostructures subject to a variety of perturbations and external conditions

Dynamic Nanoplasmonics

Light can strongly interact with metallic nanostructures, leading collective oscillations of conduction electrons known as particle plasmons. For a long time, gold and silver have been the metals of choice for constructing plasmonic nanodevices, given their excellent optical properties. However, these metals present static optical responses. In the past decade, tremendous interest has been witnessed in dynamically controlling the optical properties of plasmonic nanostructures. To enable dynamic functionality, several approaches have been proposed and implemented. First one is to manipulate the configurations of plasmonic structures. Second one is to tune the dielectric surroundings of plasmonic nanostructures. Third one, which is probably the most intriguing one, is to directly regulate the carrier densities and dielectric functions of the metals themselves. Magnesium is one of the promising candidates, as it exhibits excellent optical properties at high frequencies and can absorb/desorb hydrogen, undergoing reversible transitions between metal and dielectric hydride states. This offers great opportunities to design and construct dynamic optical nanodevices at visible frequencies. We envision that Magnesium-based dynamic nanoplasmonics will not only provide insights into understanding the catalytic processes of hydrogen diffusion in metals on the nanometer scale by optical means but also it will open an avenue towards functional plasmonic nanodevices with tailored optical properties for real-world applications

A comprehensive survey on hybrid communication in context of molecular communication and terahertz communication for body-centric nanonetworks

With the huge advancement of nanotechnology over the past years, the devices are shrinking into micro-scale, even nano-scale. Additionally, the Internet of nano-things (IoNTs) are generally regarded as the ultimate formation of the current sensor networks and the development of nanonetworks would be of great help to its fulfilment, which would be ubiquitous with numerous applications in all domains of life. However, the communication between the devices in such nanonetworks is still an open problem. Body-centric nanonetworks are believed to play an essential role in the practical application of IoNTs. BCNNs are also considered as domain specific like wireless sensor networks and always deployed on purpose to support a particular application. In these networks, electromagnetic and molecular communications are widely considered as two main promising paradigms and both follow their own development process. In this survey, the recent developments of these two paradigms are first illustrated in the aspects of applications, network structures, modulation techniques, coding techniques and security to then investigate the potential of hybrid communication paradigms. Meanwhile, the enabling technologies have been presented to apprehend the state-of-art with the discussion on the possibility of the hybrid technologies. Additionally, the inter-connectivity of electromagnetic and molecular body-centric nanonetworks is discussed. Afterwards, the related security issues of the proposed networks are discussed. Finally, the challenges and open research directions are presented

Optimisation de réseaux de neurones à décharges avec contraintes matérielles pour processeur neuromorphique

Les modèles informatiques basés sur l'apprentissage machine ont démarré la seconde révolution de l'intelligence artificielle. Capables d'atteindre des performances que l'on crut inimaginables au préalable, ces modèles semblent devenir partie courante dans plusieurs domaines. La face cachée de ceux-ci est que l'énergie consommée pour l'apprentissage, et l'utilisation de ces techniques, est colossale. La dernière décennie a été marquée par l'arrivée de plusieurs processeurs neuromorphiques pouvant simuler des réseaux de neurones avec une faible consommation d'énergie. Ces processeurs offrent une alternative aux conventionnelles cartes graphiques qui demeurent à ce jour essentielles au domaine. Ces processeurs sont capables de réduire la consommation d'énergie en utilisant un modèle de neurone événementiel, plus communément appelé neurone à décharge. Ce type de neurone est fondamentalement différent du modèle classique, et possède un aspect temporel important. Les méthodes, algorithmes et outils développés pour le modèle de neurone classique ne sont pas adaptés aux neurones à décharges. Cette thèse de doctorat décrit plusieurs approches fondamentales, dédiées à la création de processeurs neuromorphiques analogiques, qui permettent de pallier l'écart existant entre les systèmes à base de neurones conventionnels et à décharges. Dans un premier temps, nous présentons une nouvelle règle de plasticité synaptique permettant l'apprentissage non supervisé des réseaux de neurones récurrents utilisant ce nouveau type de neurone. Puis, nous proposons deux nouvelles méthodes pour la conception des topologies de ce même type de réseau. Finalement, nous améliorons les techniques d'apprentissage supervisé en augmentant la capacité de mémoire de réseaux récurrents. Les éléments de cette thèse marient l'inspiration biologique du cerveau, l'ingénierie neuromorphique et l'informatique fondamentale pour permettre d'optimiser les réseaux de neurones pouvant fonctionner sur des processeurs neuromorphiques analogiques