Commande optique de transistors à nanotubes de carbone fonctionnalisés et autoassemblés chimiquement

Cette thèse présente l'étude de composants opto-électroniques à base de nanotubes de carbone. Fonctionnaliser un transistor à effet de champ dont le canal est un (ou plusieurs) nanotube(s) de carbone semiconducteur(s) par un film mince de polymère conjugué permet de combiner les propriétés de transport exceptionnelles des nanotubes avec les propriétés optiques très riches des matériaux organiques. Les transistor sont autoassemblés chimiquement grâce au dépôt sélectif de nanotubes sur une monocouche moléculaire. Ils sont ensuite fonctionnalisés par le dépôt d'un film mince de polythiophène, un polymère conjugue semiconducteur. Cette étude montre que la photogénération de charges dans le polymère permet de moduler la conductance du nanotube de carbone sur quatre ordres de grandeur. L'étude des propriétés de transport (statiques et transitoires), sous éclairement, des transistors à nanotubes non fonctionnalisés, puis des transistors fonctionnalisés et enfin des transistors organiques sans nanotubes montre que la commande optique est due au piégeage d'électrons dans les états de surface du diélectrique de grille. Selon le potentiel de grille, ce composant se comporte comme un modulateur optique de courant ou une mémoire non volatile à écriture optique et effacement électrique. Le nanotube se comporte alors comme une sonde locale remarquablement sensible à la distribution des charges photogénérées dans le film de polymère et à l'interface polymère-diélectrique. Ces travaux ouvrent des perspectives intéressantes dans le domaine de la détection de lumière (pixels sub- denses à bas coût) et celui des architectures de nano-composants à base d'une double commande électro-optique.This thesis presents the study of opto-electronic nanodevices based on carbon nanotubes. A nanotube field effect transistor constituted of one or a few semiconducting nanotubes is coated by a thin polymer film to combine the exceptional electrical properties of nanotubes with the optical properties of organic materials. The transistors used in this work are fabricated using a chemical self-assembly technique based on the selective deposition of nanotubes on a molecular amine monolayer. The transistors are then functionalized by the spin coating of a thin film of polythiophene, a semiconducting conjugated polymer. This study demonstrates that the photogenerated charges in the polymer modulate the nanotube conductivity over four orders of magnitude. We studied the static and transient electrical properties under illumination of as-made or functionalized nanotube transistors as well as polythiophene TFTs without nanotubes. We demonstrate that the optical command of the devices is due to the electron trapping in the gate dielectric surface traps. Depending on the gate bias, the device behaves either as an optical current modulator or as an electrically erasable non volatile optical memory. The carbon nanotube acts as a local probe remarkably sensitive to the photogenerated charge distribution in the polymer film and at the polymer-dielectric interface. This work has interesting perspectives for light detection and innovative architecture: low-cost sub- pixels are currently under development while new architecture based on nanodevices with a double electrical and optical command are under investigation.ORSAY-PARIS 11-BU Sciences (914712101) / SudocSudocFranceF

‘Memristive’ switches enable ‘stateful’ logic operations via material implication

The authors of the International Technology Roadmap for Semiconductors1 —the industry consensus set of goals established for advancing silicon integrated circuit technology—have challenged the computing research community to find new physical state variables (other than charge or voltage), new devices, and new architectures that offer memory and logic functions1–6 beyond those available with standard transistors. Recently, ultra-dense resistive memory arrays built from various two-terminal semiconductor or insulator thin film devices have been demonstrated7–12. Among these, bipolar voltage-actuated switches have been identified as physical realizations of ‘memristors’ or memristive devices, combining the electrical properties of a memory element and a resistor13,14. Such devices were first hypothesized by Chua in 1971 (ref. 15), and are characterized by one or more state variables16 that define the resistance of the switch depending upon its voltage history. Here we show that this family of nonlinear dynamical memory devices can also be used for logic operations: we demonstrate that they can execute material implication (IMP), which is a fundamental Boolean logic operation on two variables p and q such that pIMPq is equivalent to (NOTp)ORq. Incorporated within an appropriate circuit17,18, memristive switches can thus perform ‘stateful’ logic operations for which the same devices serve simultaneously as gates (logic) and latches19 (memory) that use resistance instead of voltage or charge as the physical state variable

A hybrid nanomemristor/transistor logic circuit capable of self-programming

Memristor crossbars were fabricated at 40 nm half-pitch, using nanoimprint lithography on the same substrate with Si metal-oxide-semiconductor field effect transistor (MOS FET) arrays to form fully integrated hybrid memory resistor (memristor)/transistor circuits. The digitally configured memristor crossbars were used to perform logic functions, to serve as a routing fabric for interconnecting the FETs and as the target for storing information. As an illustrative demonstration, the compound Boolean logic operation (A AND B) OR (C AND D) was performed with kilohertz frequency inputs, using resistor-based logic in a memristor crossbar with FET inverter/amplifier outputs. By routing the output signal of a logic operation back onto a target memristor inside the array, the crossbar was conditionally configured by setting the state of a nonvolatile switch. Such conditional programming illuminates the way for a variety of self-programmed logic arrays, and for electronic synaptic computing

Spiking Neural Computing in Memristive Neuromorphic Platforms

International audienceAbstract Neuromorphic computation using Spiking Neural Networks (SNN) is pro-posed as an alternative solution for future of computation to conquer the memorybottelneck issue in recent computer architecture. Different spike codings have beendiscussed to improve data transferring and data processing in neuro-inspired compu-tation paradigms. Choosing the appropriate neural network topology could result inbetter performance of computation, recognition and classification. The model of theneuron is another important factor to design and implement SNN systems. The speedof simulation and implementation, ability of integration to the other elements of thenetwork, and suitability for scalable networks are the factors to select a neuron model.The learning algorithms are significant consideration to train the neural network forweight modification. Improving learning in neuromorphic architecture is feasibleby improving the quality of artificial synapse as well as learning algorithm such asSTDP. In this chapter we proposed a new synapse box that can remember and forget.Furthermore, as the most frequent used unsupervised method for network training inSNN is STDP, we analyze and review the various methods of STDP. The sequentialorder of pre- or postsynaptic spikes occurring across a synapse in an interval of timeleads to defining different STDP methods. Based on the importance of stability aswell as Hebbian competition or anti-Hebbian competition the method will be usedin weight modification. We survey the most significant projects that cause makingneuromorphic platform. The advantages and disadvantages of each neuromorphicplatform are introduced in this chapter