Electrical characterization and modeling of pulse-based forming techniques in RRAM arrays

The forming process, which corresponds to the activation of the switching filament in Resistive Random Access Memory (RRAM) arrays, has a strong impact on the cells’ performances. In this paper we characterize and compare different pulse forming techniques in terms of forming time, yield and cell-to-cell variability on 4 kbits RRAM arrays. Moreover, post-forming modeling during Reset operation of correctly working and over formed cells has been performed. An incremental form and verify technique, based on a sequence of trapezoidal waveforms with increasing voltages followed by a verify operation that terminates when the expected switching behavior has been achieved, showed the best results. This procedure narrows the post-forming current distribution whereas reducing the Reset switching voltage and the operative current. These advantages materialize in a better control of the cell-to-cell variability and in an overall time and energy saving at the system level

The Efficacy of Programming Energy Controlled Switching in Resistive Random Access Memory (RRAM)

Current state-of-the-art memory technologies such as FLASH, Static Random Access Memory (SRAM) and Dynamic RAM (DRAM) are based on charge storage. The semiconductor industry has relied on cell miniaturization to increase the performance and density of memory technology, while simultaneously decreasing the cost per bit. However, this approach is not sustainable because the charge-storage mechanism is reaching a fundamental scaling limit. Although stack engineering and 3D integration solutions can delay this limit, alternate strategies based on non-charge storage mechanisms for memory have been introduced and are being actively pursued. Resistive Random Access Memory (RRAM) has emerged as one of the leading candidates for future high density non-volatile memory. The superior scalability of RRAMs is based on the highly localized active switching region and filamentary conductive path. Coupled with its simple structure and compatibility with complementary metal oxide semiconductor (CMOS) processes; RRAM cells have demonstrated switching performance comparable to volatile memory technologies such as DRAMs and SRAMs. However, there are two serious barriers to RRAM commercialization. The first is the variability of the resistance state which is associated with the inherent randomness of the resistive switching mechanism. The second is the filamentary nature of the conductive path which makes it susceptible to noise. In this experimental thesis, a novel program-verify (P-V) technique was developed with the objective to specifically address the programming errors and to provide solutions to the most challenging issues associated with these intrinsic failures in current RRAM technology. The technique, called Compliance-free Ultra-short Smart Pulse Programming (CUSPP), utilizes sub-nanosecond pulses in a compliance-free setup to minimize the programming energy delivered per pulse. In order to demonstrate CUSPP, a custom-built picosecond pulse generator and feedback control circuit was designed. We achieved high (108 cycles) endurance with state verification for each cycle and established high-speed performance, such as 100 ps write/erase speed and 500 kHz cycling rate of HfO2-based RRAM cells. We also investigate switching failure and the short-term instability of the RRAM using CUSPP

Reliability-aware memory design using advanced reconfiguration mechanisms

Fast and Complex Data Memory systems has become a necessity in modern computational units in today's integrated circuits. These memory systems are integrated in form of large embedded memory for data manipulation and storage. This goal has been achieved by the aggressive scaling of transistor dimensions to few nanometer (nm) sizes, though; such a progress comes with a drawback, making it critical to obtain high yields of the chips. Process variability, due to manufacturing imperfections, along with temporal aging, mainly induced by higher electric fields and temperature, are two of the more significant threats that can no longer be ignored in nano-scale embedded memory circuits, and can have high impact on their robustness. Static Random Access Memory (SRAM) is one of the most used embedded memories; generally implemented with the smallest device dimensions and therefore its robustness can be highly important in nanometer domain design paradigm. Their reliable operation needs to be considered and achieved both in cell and also in architectural SRAM array design. Recently, and with the approach to near/below 10nm design generations, novel non-FET devices such as Memristors are attracting high attention as a possible candidate to replace the conventional memory technologies. In spite of their favorable characteristics such as being low power and highly scalable, they also suffer with reliability challenges, such as process variability and endurance degradation, which needs to be mitigated at device and architectural level. This thesis work tackles such problem of reliability concerns in memories by utilizing advanced reconfiguration techniques. In both SRAM arrays and Memristive crossbar memories novel reconfiguration strategies are considered and analyzed, which can extend the memory lifetime. These techniques include monitoring circuits to check the reliability status of the memory units, and architectural implementations in order to reconfigure the memory system to a more reliable configuration before a fail happens.Actualmente, el diseño de sistemas de memoria en circuitos integrados busca continuamente que sean más rápidos y complejos, lo cual se ha vuelto de gran necesidad para las unidades de computación modernas. Estos sistemas de memoria están integrados en forma de memoria embebida para una mejor manipulación de los datos y de su almacenamiento. Dicho objetivo ha sido conseguido gracias al agresivo escalado de las dimensiones del transistor, el cual está llegando a las dimensiones nanométricas. Ahora bien, tal progreso ha conllevado el inconveniente de una menor fiabilidad, dado que ha sido altamente difícil obtener elevados rendimientos de los chips. La variabilidad de proceso - debido a las imperfecciones de fabricación - junto con la degradación de los dispositivos - principalmente inducido por el elevado campo eléctrico y altas temperaturas - son dos de las más relevantes amenazas que no pueden ni deben ser ignoradas por más tiempo en los circuitos embebidos de memoria, echo que puede tener un elevado impacto en su robusteza final. Static Random Access Memory (SRAM) es una de las celdas de memoria más utilizadas en la actualidad. Generalmente, estas celdas son implementadas con las menores dimensiones de dispositivos, lo que conlleva que el estudio de su robusteza es de gran relevancia en el actual paradigma de diseño en el rango nanométrico. La fiabilidad de sus operaciones necesita ser considerada y conseguida tanto a nivel de celda de memoria como en el diseño de arquitecturas complejas basadas en celdas de memoria SRAM. Actualmente, con el diseño de sistemas basados en dispositivos de 10nm, dispositivos nuevos no-FET tales como los memristores están atrayendo una elevada atención como posibles candidatos para reemplazar las actuales tecnologías de memorias convencionales. A pesar de sus características favorables, tales como el bajo consumo como la alta escabilidad, ellos también padecen de relevantes retos de fiabilidad, como son la variabilidad de proceso y la degradación de la resistencia, la cual necesita ser mitigada tanto a nivel de dispositivo como a nivel arquitectural. Con todo esto, esta tesis doctoral afronta tales problemas de fiabilidad en memorias mediante la utilización de técnicas de reconfiguración avanzada. La consideración de nuevas estrategias de reconfiguración han resultado ser validas tanto para las memorias basadas en celdas SRAM como en `memristive crossbar¿, donde se ha observado una mejora significativa del tiempo de vida en ambos casos. Estas técnicas incluyen circuitos de monitorización para comprobar la fiabilidad de las unidades de memoria, y la implementación arquitectural con el objetivo de reconfigurar los sistemas de memoria hacia una configuración mucho más fiables antes de que el fallo suced

Resistive switching devices with improved control of oxygen vacancies dynamics

L'abstract è presente nell'allegato / the abstract is in the attachmen

Towards Data Reliable, Low-Power, and Repairable Resistive Random Access Memories

A series of breakthroughs in memristive devices have demonstrated the potential of memristor arrays to serve as next generation resistive random access memories (ReRAM), which are fast, low-power, ultra-dense, and non-volatile. However, memristors' unique device characteristics also make them prone to several sources of error. Owing to the stochastic filamentary nature of memristive devices, various recoverable errors can affect the data reliability of a ReRAM. Permanent device failures further limit the lifetime of a ReRAM. This dissertation developed low-power solutions for more reliable and longer-enduring ReRAM systems. In this thesis, we first look into a data reliability issue known as write disturbance. Writing into a memristor in a crossbar could disturb the stored values in other memristors that are on the same memory line as the target cell. Such disturbance is accumulative over time which may lead to complete data corruption. To address this problem, we propose the use of two regular memristors on each word to keep track of the disturbance accumulation and trigger a refresh to restore the weakened data, once it becomes necessary. We also investigate the considerable variation in the write-time characteristics of individual memristors. With such variation, conventional fixed-pulse write schemes not only waste significant energy, but also cannot guarantee reliable completion of the write operations. We address such variation by proposing an adaptive write scheme that adjusts the width of the write pulses for each memristor. Our scheme embeds an online monitor to detect the completion of a write operation and takes into account the parasitic effect of line-shared devices in access-transistor-free memristive arrays. We further investigate the use of this method to shorten the test time of memory march algorithms by eliminating the need of a verifying read right after a write, which is commonly employed in the test sequences of march algorithms.Finally, we propose a novel mechanism to extend the lifetime of a ReRAM by protecting it against hard errors through the exploitation of a unique feature of bipolar memristive devices. Our solution proposes an unorthodox use of complementary resistive switches (a particular implementation of memristive devices) to provide an ``in-place spare'' for each memory cell at negligible extra cost. The in-place spares are then utilized by a repair scheme to repair memristive devices that have failed at a stuck-at-ON state at a page-level granularity. Furthermore, we explore the use of in-place spares in lieu of other memory reliability and yield enhancement solutions, such as error correction codes (ECC) and spare rows. We demonstrate that with the in-place spares, we can yield the same lifetime as a baseline ReRAM with either significantly fewer spare rows or a lighter-weight ECC, both of which can save on energy consumption and area

Stochastic Memory Devices for Security and Computing

With the widespread use of mobile computing and internet of things, secured communication and chip authentication have become extremely important. Hardware-based security concepts generally provide the best performance in terms of a good standard of security, low power consumption, and large-area density. In these concepts, the stochastic properties of nanoscale devices, such as the physical and geometrical variations of the process, are harnessed for true random number generators (TRNGs) and physical unclonable functions (PUFs). Emerging memory devices, such as resistive-switching memory (RRAM), phase-change memory (PCM), and spin-transfer torque magnetic memory (STT-MRAM), rely on a unique combination of physical mechanisms for transport and switching, thus appear to be an ideal source of entropy for TRNGs and PUFs. An overview of stochastic phenomena in memory devices and their use for developing security and computing primitives is provided. First, a broad classification of methods to generate true random numbers via the stochastic properties of nanoscale devices is presented. Then, practical implementations of stochastic TRNGs, such as hardware security and stochastic computing, are shown. Finally, future challenges to stochastic memory development are discussed

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

Les nouvelles approches de technologies mémoires permettront une intégration dite back-end, où les cellules élémentaires de stockage seront fabriquées lors des dernières étapes de réalisation à grande échelle du circuit. Ces approches innovantes sont souvent basées sur l'utilisation de matériaux actifs présentant deux états de résistance distincts. Le passage d'un état à l'autre est contrôlé en courant ou en tension donnant lieu à une caractéristique I-V hystérétique. Nos mémoires résistives sont composées d'argent en métal électrochimiquement actif et de sulfure amorphe agissant comme électrolyte. Leur fonctionnement repose sur la formation réversible et la dissolution d'un filament conducteur. Le potentiel d'application de ces nouveaux dispositifs n'est pas limité aux mémoires ultra-haute densité mais aussi aux circuits embarqués. En empilant ces mémoires dans la troisième 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 à basse énergie, à haute vitesse d'écriture/lecture et de haute performance telles que l'endurance et la rétention. Dans cette thèse, en se concentrant sur les aspects de la technologie de mémoire en vue de développer de nouvelles architectures, l'introduction d'une fonctionnalité non-volatile au niveau logique est démontrée 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 réseau neuronal. Pour améliorer les solutions existantes, les limitations de la performances des dispositifs mémoires sont identifiés et résolus avec des nouveaux empilements ou en fournissant des défauts de circuits tolérants.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.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF

Hybrid Memristor-CMOS Computer for Artificial Intelligence: from Devices to Systems

Neuromorphic computing systems, which aim to mimic the function and structure of the human brain, is a promising approach to overcome the limitations of conventional computing systems such as the von-Neumann bottleneck. Recently, memristors and memristor crossbars have been extensively studied for neuromorphic system implementations due to the ability of memristor devices to emulate biological synapses, thus providing benefits such as co-located memory/logic operations and massive parallelism. A memristor is a two-terminal device whose resistance is modulated by the history of external stimulation. The principle of the resistance modulation, or resistance switching, for a typical oxide-based memristor, is based on oxygen vacancy migration in the oxide layer through ion drift and diffusion. When applied in computing systems, the memristor is often formed in a crossbar structure and used to perform vector-matrix multiplication operations. Since the values in the matrix can be stored as the device conductance values of the crossbar array, when an input vector is applied as voltage pulses with different pulse amplitudes or different pulse widths to the rows of the crossbar, the currents or charges collected at the columns of the crossbar correspond to the resulting VMM outputs, following Ohm’s law and Kirchhoff’s current law. This approach makes it possible to use physics to execute direct computing of this data-intensive task, both in-memory and in parallel in a single step. First of all, I will present a comprehensive physical model of the TaOx-based memristor device where the internal parameters including electric field, temperature, and VO concentration are self-consistently solved to accurately describe the device operation. Starting from the initial Forming process, the model quantitatively captures the dynamic RS behavior, and can reliably reproduce Set/Reset cycling in a self-consistent manner. Beyond clarifying the nature of the Forming and Set/Reset processes, a bulk-like doping effect was revealed by the model during Set and supported by experimental results. This phenomenon can lead to linear analog conductance modulation with a large dynamic range, which is very beneficial for low-power neuromorphic computing applications. Second, an integrated memristor/CMOS system consisting of a 54×108 passive memristor crossbar array directly fabricated on a CMOS chip is presented. The system includes all necessary analog/digital circuitry (including analog-digital converters and digital-analog converters), digital buses, and a programmable processor to control the digital and analog components to form a complete hardware system for neuromorphic computing applications. With the fully-integrated and reprogrammable chip, we experimentally demonstrated three popular models – a perceptron network, a sparse coding network, and a bilayer principal component analysis system with an unsupervised feature extraction layer and a supervised classification layer – all on the same chip. Beyond VMM operations, the internal dynamics of memristors allow the system to natively process temporal features in the input data. Specifically, a WOx-based memristor with short-term memory effect caused by spontaneous oxygen vacancy diffusion was utilized to implement a reservoir computing system to process temporal information. The spatial information of a digit image can be converted into streaming inputs fed into the memristor reservoir, leading to 100% accuracy for simple 4×5 digit recognition and 88.1% accuracy for the MNIST data set. The system was also employed for solving other nonlinear tasks such as emulating a second-order nonlinear system.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155040/1/seulee_1.pd

Gestión de jerarquías de memoria híbridas a nivel de sistema

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Arquitectura de Computadoras y Automática y de Ku Leuven, Arenberg Doctoral School, Faculty of Engineering Science, leída el 11/05/2017.In electronics and computer science, the term ‘memory’ generally refers to devices that are used to store information that we use in various appliances ranging from our PCs to all hand-held devices, smart appliances etc. Primary/main memory is used for storage systems that function at a high speed (i.e. RAM). The primary memory is often associated with addressable semiconductor memory, i.e. integrated circuits consisting of silicon-based transistors, used for example as primary memory but also other purposes in computers and other digital electronic devices. The secondary/auxiliary memory, in comparison provides program and data storage that is slower to access but offers larger capacity. Examples include external hard drives, portable flash drives, CDs, and DVDs. These devices and media must be either plugged in or inserted into a computer in order to be accessed by the system. Since secondary storage technology is not always connected to the computer, it is commonly used for backing up data. The term storage is often used to describe secondary memory. Secondary memory stores a large amount of data at lesser cost per byte than primary memory; this makes secondary storage about two orders of magnitude less expensive than primary storage. There are two main types of semiconductor memory: volatile and nonvolatile. Examples of non-volatile memory are ‘Flash’ memory (sometimes used as secondary, sometimes primary computer memory) and ROM/PROM/EPROM/EEPROM memory (used for firmware such as boot programs). Examples of volatile memory are primary memory (typically dynamic RAM, DRAM), and fast CPU cache memory (typically static RAM, SRAM, which is fast but energy-consuming and offer lower memory capacity per are a unit than DRAM). Non-volatile memory technologies in Si-based electronics date back to the 1990s. Flash memory is widely used in consumer electronic products such as cellphones and music players and NAND Flash-based solid-state disks (SSDs) are increasingly displacing hard disk drives as the primary storage device in laptops, desktops, and even data centers. The integration limit of Flash memories is approaching, and many new types of memory to replace conventional Flash memories have been proposed. The rapid increase of leakage currents in Silicon CMOS transistors with scaling poses a big challenge for the integration of SRAM memories. There is also the case of susceptibility to read/write failure with low power schemes. As a result of this, over the past decade, there has been an extensive pooling of time, resources and effort towards developing emerging memory technologies like Resistive RAM (ReRAM/RRAM), STT-MRAM, Domain Wall Memory and Phase Change Memory(PRAM). Emerging non-volatile memory technologies promise new memories to store more data at less cost than the expensive-to build silicon chips used by popular consumer gadgets including digital cameras, cell phones and portable music players. These new memory technologies combine the speed of static random-access memory (SRAM), the density of dynamic random-access memory (DRAM), and the non-volatility of Flash memory and so become very attractive as another possibility for future memory hierarchies. The research and information on these Non-Volatile Memory (NVM) technologies has matured over the last decade. These NVMs are now being explored thoroughly nowadays as viable replacements for conventional SRAM based memories even for the higher levels of the memory hierarchy. Many other new classes of emerging memory technologies such as transparent and plastic, three-dimensional(3-D), and quantum dot memory technologies have also gained tremendous popularity in recent years...En el campo de la informática, el término ‘memoria’ se refiere generalmente a dispositivos que son usados para almacenar información que posteriormente será usada en diversos dispositivos, desde computadoras personales (PC), móviles, dispositivos inteligentes, etc. La memoria principal del sistema se utiliza para almacenar los datos e instrucciones de los procesos que se encuentre en ejecución, por lo que se requiere que funcionen a alta velocidad (por ejemplo, DRAM). La memoria principal está implementada habitualmente mediante memorias semiconductoras direccionables, siendo DRAM y SRAM los principales exponentes. Por otro lado, la memoria auxiliar o secundaria proporciona almacenaje(para ficheros, por ejemplo); es más lenta pero ofrece una mayor capacidad. Ejemplos típicos de memoria secundaria son discos duros, memorias flash portables, CDs y DVDs. Debido a que estos dispositivos no necesitan estar conectados a la computadora de forma permanente, son muy utilizados para almacenar copias de seguridad. La memoria secundaria almacena una gran cantidad de datos aun coste menor por bit que la memoria principal, siendo habitualmente dos órdenes de magnitud más barata que la memoria primaria. Existen dos tipos de memorias de tipo semiconductor: volátiles y no volátiles. Ejemplos de memorias no volátiles son las memorias Flash (algunas veces usadas como memoria secundaria y otras veces como memoria principal) y memorias ROM/PROM/EPROM/EEPROM (usadas para firmware como programas de arranque). Ejemplos de memoria volátil son las memorias DRAM (RAM dinámica), actualmente la opción predominante a la hora de implementar la memoria principal, y las memorias SRAM (RAM estática) más rápida y costosa, utilizada para los diferentes niveles de cache. Las tecnologías de memorias no volátiles basadas en electrónica de silicio se remontan a la década de1990. Una variante de memoria de almacenaje por carga denominada como memoria Flash es mundialmente usada en productos electrónicos de consumo como telefonía móvil y reproductores de música mientras NAND Flash solid state disks(SSDs) están progresivamente desplazando a los dispositivos de disco duro como principal unidad de almacenamiento en computadoras portátiles, de escritorio e incluso en centros de datos. En la actualidad, hay varios factores que amenazan la actual predominancia de memorias semiconductoras basadas en cargas (capacitivas). Por un lado, se está alcanzando el límite de integración de las memorias Flash, lo que compromete su escalado en el medio plazo. Por otra parte, el fuerte incremento de las corrientes de fuga de los transistores de silicio CMOS actuales, supone un enorme desafío para la integración de memorias SRAM. Asimismo, estas memorias son cada vez más susceptibles a fallos de lectura/escritura en diseños de bajo consumo. Como resultado de estos problemas, que se agravan con cada nueva generación tecnológica, en los últimos años se han intensificado los esfuerzos para desarrollar nuevas tecnologías que reemplacen o al menos complementen a las actuales. Los transistores de efecto campo eléctrico ferroso (FeFET en sus siglas en inglés) se consideran una de las alternativas más prometedores para sustituir tanto a Flash (por su mayor densidad) como a DRAM (por su mayor velocidad), pero aún está en una fase muy inicial de su desarrollo. Hay otras tecnologías algo más maduras, en el ámbito de las memorias RAM resistivas, entre las que cabe destacar ReRAM (o RRAM), STT-RAM, Domain Wall Memory y Phase Change Memory (PRAM)...Depto. de Arquitectura de Computadores y AutomáticaFac. de InformáticaTRUEunpu