7 research outputs found

    Nouvelles Architectures Hybrides (Logique / Mémoires Non-Volatiles et technologies associées.)

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    Les nouvelles approches de technologies mĂ©moires permettront une intĂ©gration dite back-end, oĂč les cellules Ă©lĂ©mentaires de stockage seront fabriquĂ©es lors des derniĂšres Ă©tapes de rĂ©alisation Ă  grande Ă©chelle du circuit. Ces approches innovantes sont souvent basĂ©es sur l'utilisation de matĂ©riaux actifs prĂ©sentant deux Ă©tats de rĂ©sistance distincts. Le passage d'un Ă©tat Ă  l'autre est contrĂŽlĂ© en courant ou en tension donnant lieu Ă  une caractĂ©ristique I-V hystĂ©rĂ©tique. Nos mĂ©moires rĂ©sistives sont composĂ©es d'argent en mĂ©tal Ă©lectrochimiquement actif et de sulfure amorphe agissant comme Ă©lectrolyte. Leur fonctionnement repose sur la formation rĂ©versible et la dissolution d'un filament conducteur. Le potentiel d'application de ces nouveaux dispositifs n'est pas limitĂ© aux mĂ©moires ultra-haute densitĂ© mais aussi aux circuits embarquĂ©s. En empilant ces mĂ©moires dans la troisiĂšme dimension au niveau des interconnections des circuits logiques CMOS, de nouvelles architectures hybrides et innovantes deviennent possibles. Il serait alors envisageable d'exploiter un fonctionnement Ă  basse Ă©nergie, Ă  haute vitesse d'Ă©criture/lecture et de haute performance telles que l'endurance et la rĂ©tention. Dans cette thĂšse, en se concentrant sur les aspects de la technologie de mĂ©moire en vue de dĂ©velopper de nouvelles architectures, l'introduction d'une fonctionnalitĂ© non-volatile au niveau logique est dĂ©montrĂ©e par trois circuits hybrides: commutateurs de routage non volatiles dans un Field Programmable Gate Arrays, un 6T-SRAM non volatile, et les neurones stochastiques pour un rĂ©seau neuronal. Pour amĂ©liorer les solutions existantes, les limitations de la performances des dispositifs mĂ©moires sont identifiĂ©s et rĂ©solus avec des nouveaux empilements ou en fournissant des dĂ©fauts de circuits tolĂ©rants.Novel approaches in the field of memory technology should enable backend integration, where individual storage nodes will be fabricated during the last fabrication steps of the VLSI circuit. In this case, memory operation is often based upon the use of active materials with resistive switching properties. A topology of resistive memory consists of silver as electrochemically active metal and amorphous sulfide acting as electrolyte and relies on the reversible formation and dissolution of a conductive filament. The application potential of these new memories is not limited to stand-alone (ultra-high density), but is also suitable for embedded applications. By stacking these memories in the third dimension at the interconnection level of CMOS logic, new ultra-scalable hybrid architectures becomes possible which exploit low energy operation, fast write/read access and high performance with respect to endurance and retention. In this thesis, focusing on memory technology aspects in view of developing new architectures, the introduction of non-volatile functionality at the logic level is demonstrated through three hybrid (CMOS logic ReRAM devices) circuits: nonvolatile routing switches in a Field Programmable Gate Array, nonvolatile 6T-SRAMs, and stochastic neurons of an hardware neural network. To be competitive or even improve existing solutions, limitations on the memory devices performances are identified and solved by stack engineering of CBRAM devices or providing faults tolerant circuits.SAVOIE-SCD - Bib.Ă©lectronique (730659901) / SudocGRENOBLE1/INP-Bib.Ă©lectronique (384210012) / SudocGRENOBLE2/3-Bib.Ă©lectronique (384219901) / SudocSudocFranceF

    Evaluation des performances des mĂ©moires CBRAM (Conductive bridge memory) afin d’optimiser les empilements technologiques et les solutions d’intĂ©gration

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    The constant evolution of the data storage needs over the last decades have led the technological landscape to completely change and reinvent itself. From the early stage of magnetic storage to the most recent solid state devices, the bit density keeps increasing toward what seems from a consumer point of view infinite storage capacity and performances. However, behind each storage technology transition stand density and performances limitations that required strong research work to overcome. This manuscript revolves around one of the promising emerging technology aiming to revolutionize data storage landscape: the Conductive Bridge Random Access Memory (CBRAM). This technology based on the reversible formation and dissolution of a conductive path in a solid electrolyte matrix offers great advantages in term of power consumption, performances, density and the possibility to be integrated in the back end of line. However, for this technology to be competitive some roadblocks still have to be overcome especially regarding the technology variability, reliability and thermal stability. This manuscript proposes a comprehensive understanding of the CBRAM operations based on experimental results and a specially developed Kinetic Monte Carlo model. This understanding creates bridges between the physical properties of the materials involved in the devices and the devices performances (Forming, SET and RESET time and voltage, retention, endurance, variability). A strong emphasis is placed on the current limitations of the technology previously stated and how to overcome these limitations. Improvement of the thermal stability and device reliability are demonstrated with optimized operating conditions and proper devices engineering.Ces derniĂšres dĂ©cennies, la constante Ă©volution des besoins de stockage de donnĂ©es a menĂ© Ă  un bouleversement du paysage technologique qui s’est complĂštement mĂ©tamorphosĂ© et rĂ©inventĂ©. Depuis les dĂ©buts du stockage magnĂ©tique jusqu’aux plus rĂ©cents dispositifs fondĂ©s sur l’électronique dit d’état solide, la densitĂ© de bits stockĂ©s continue d’augmenter vers ce qui semble du point de vue du consommateur comme des capacitĂ©s de stockage et des performances infinies. Cependant, derriĂšre chaque transition et Ă©volution des technologies de stockage se cachent des limitations en termes de densitĂ© et performances qui nĂ©cessitent de lourds travaux de recherche afin d’ĂȘtre surmontĂ©es et repoussĂ©es. Ce manuscrit s’articule autour d’une technologie Ă©mergeante prometteuse ayant pour vocation de rĂ©volutionner le paysage du stockage de donnĂ©es : la mĂ©moire Ă  pont conducteur ou Conductive Bridge Random Access Memory (CBRAM). Cette technologie est fondĂ©e sur la formation et dissolution rĂ©versible d’un chemin Ă©lectriquement conducteur dans un Ă©lectrolyte solide. Elle offre de nombreux avantages face aux technologies actuelles tels qu’une faible consommation Ă©lectrique, de trĂšs bonnes performances d’écriture et de lecture et la capacitĂ© d’ĂȘtre intĂ©grĂ© aux seins des interconnexions mĂ©talliques d’une puce afin d’augmenter la densitĂ© de stockage. MalgrĂ© tout, pour que cette technologie soit compĂ©titive certaines limitations ont besoin d’ĂȘtre surmontĂ©es et particuliĂšrement sa variabilitĂ© et sa stabilitĂ© thermique qui posent encore problĂšme. Ce manuscrit propose une comprĂ©hension physique globale du fonctionnement de la technologie CBRAM fondĂ©e sur une Ă©tude expĂ©rimentale approfondie couplĂ©e Ă  un modĂšle Monte Carlo cinĂ©tique spĂ©cialement dĂ©veloppĂ©. Cette comprĂ©hension fait le lien entre les propriĂ©tĂ©s physiques des matĂ©riaux composant la mĂ©moire CBRAM et ses performances (Tension et temps d’écriture et d’effacement, rĂ©tention de donnĂ©e, endurance et variabilitĂ©). Un fort accent est mis la comprĂ©hension des limites actuelle de la technologie et comment les repousser. GrĂące Ă  une optimisation des conditions d’opĂ©rations ainsi qu’à un travail d’ingĂ©nierie des dispositifs mĂ©moire, il est dĂ©montrĂ© dans ce manuscrit une forte amĂ©lioration de la stabilitĂ© thermique ainsi que de la variabilitĂ© des Ă©tats Ă©crits et effacĂ©s

    Quantum Conductance in Memristive Devices: Fundamentals, Developments, and Applications

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    Quantum effects in novel functional materials and new device concepts represent a potential breakthrough for the development of new information processing technologies based on quantum phenomena. Among the emerging technologies, memristive elements that exhibit resistive switching, which relies on the electrochemical formation/rupture of conductive nanofilaments, exhibit quantum conductance effects at room temperature. Despite the underlying resistive switching mechanism having been exploited for the realization of next-generation memories and neuromorphic computing architectures, the potentialities of quantum effects in memristive devices are still rather unexplored. Here, a comprehensive review on memristive quantum devices, where quantum conductance effects can be observed by coupling ionics with electronics, is presented. Fundamental electrochemical and physicochemical phenomena underlying device functionalities are introduced, together with fundamentals of electronic ballistic conduction transport in nanofilaments. Quantum conductance effects including quantum mode splitting, stability, and random telegraph noise are analyzed, reporting experimental techniques and challenges of nanoscale metrology for the characterization of memristive phenomena. Finally, potential applications and future perspectives are envisioned, discussing how memristive devices with controllable atomic-sized conductive filaments can represent not only suitable platforms for the investigation of quantum phenomena but also promising building blocks for the realization of integrated quantum systems working in air at room temperature.status: publishe

    A Contribution Towards Intelligent Autonomous Sensors Based on Perovskite Solar Cells and Ta2O5/ZnO Thin Film Transistors

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    Many broad applications in the field of robotics, brain-machine interfaces, cognitive computing, image and speech processing and wearables require edge devices with very constrained power and hardware requirements that are challenging to realize. This is because these applications require sub-conscious awareness and require to be always “on”, especially when integrated with a sensor node that detects an event in the environment. Present day edge intelligent devices are typically based on hybrid CMOS-memristor arrays that have been so far designed for fast switching, typically in the range of nanoseconds, low energy consumption (typically in nano-Joules), high density and endurance (exceeding 1015 cycles). On the other hand, sensory-processing systems that have the same time constants and dynamics as their input signals, are best placed to learn or extract information from them. To meet this requirement, many applications are implemented using external “delay” in the memristor, in a process which enables each synapse to be modeled as a combination of a temporal delay and a spatial weight parameter. This thesis demonstrates a synaptic thin film transistor capable of inherent logic functions as well as compute-in-memory on similar time scales as biological events. Even beyond a conventional crossbar array architecture, we have relied on new concepts in reservoir computing to demonstrate a delay system reservoir with the highest learning efficiency of 95% reported to date, in comparison to equivalent two terminal memristors, using a single device for the task of image processing. The crux of our findings relied on enhancing our capability to model the unique physics of the device, in the scope of the current thesis, that is not amenable to conventional TCAD simulations. The model provides new insight into the redox characteristics of the gate current and paves way for assessment of device performance in compute-in-memory applications. The diffusion-based mechanism of the device, effectively enables time constants that have potential in applications such as gesture recognition and detection of cardiac arrythmia. The thesis also reports a new orientation of a solution processed perovskite solar cell with an efficiency of 14.9% that is easily integrable into an intelligent sensor node. We examine the influence of the growth orientation on film morphology and solar cell efficiency. Collectively, our work aids the development of more energy-efficient, powerful edge-computing sensor systems for upcoming applications of the IOT

    Micro/Nano Structures and Systems

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    Micro/Nano Structures and Systems: Analysis, Design, Manufacturing, and Reliability is a comprehensive guide that explores the various aspects of micro- and nanostructures and systems. From analysis and design to manufacturing and reliability, this reprint provides a thorough understanding of the latest methods and techniques used in the field. With an emphasis on modern computational and analytical methods and their integration with experimental techniques, this reprint is an invaluable resource for researchers and engineers working in the field of micro- and nanosystems, including micromachines, additive manufacturing at the microscale, micro/nano-electromechanical systems, and more. Written by leading experts in the field, this reprint offers a complete understanding of the physical and mechanical behavior of micro- and nanostructures, making it an essential reference for professionals in this field
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