High-Performance Energy-Efficient and Reliable Design of Spin-Transfer Torque Magnetic Memory

In this dissertation new computing paradigms, architectures and design philosophy are proposed and evaluated for adopting the STT-MRAM technology as highly reliable, energy efficient and fast memory. For this purpose, a novel cross-layer framework from the cell-level all the way up to the system- and application-level has been developed. In these framework, the reliability issues are modeled accurately with appropriate fault models at different abstraction levels in order to analyze the overall failure rates of the entire memory and its Mean Time To Failure (MTTF) along with considering the temperature and process variation effects. Design-time, compile-time and run-time solutions have been provided to address the challenges associated with STT-MRAM. The effectiveness of the proposed solutions is demonstrated in extensive experiments that show significant improvements in comparison to state-of-the-art solutions, i.e. lower-power, higher-performance and more reliable STT-MRAM design

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

Spin-transfer torques in MgO-based magnetic tunnel junctions

This thesis discusses spin-transfer torques in MgO-based magnetic tunnel junctions. The voltage-field switching phase diagrams have been experimentally determined for in-plane CoFeB/MgO/CoFeB magnetic tunnel junctions. In order to limit the effect of thermal activation, experiments have been carried out using nanosecond voltage pulses, as well as at low-temperature (4.2 K). The bias-dependence of the two spin-torque terms (Slonczewski-like and field-like) has been determined from thermally-excited ferromagnetic resonance measurements, yielding values which are in good agreement with previous reports. Additionally, material parameters such as the effective magnetisation and the damping factor have also been extracted. Using these values as input, the switching voltages as function of the applied magnetic field have been calculated numerically and analytically by solving the modified Landau-Lifshitz-Gilbert equation. Unlike previous studies, the field-like spin-torque has also been included. Moreover, different configurations have been considered for the magnetic anisotropy directions of the reference and free layer, respectively.:1 Introduction 2 Fundamentals 2.1 Magnetoresistance 2.1.1 Giant magnetoresistance 2.1.2 Tunnel magnetoresistance 2.2 Spin-transfer torque effect 2.2.1 Physical picture of the STT 2.2.2 In-plane and perpendicular STT 2.3 Equation of motion for the magnetisation 2.3.1 The Landau-Lifshitz-Gilbert equation 2.3.2 Extension including spin-transfer-torque (LLGS) 2.4 Applications of MR and spin-transfer torque 2.4.1 Read heads in hard disk drives 2.4.2 Spin-transfer torque magnetic random access memory 2.5 STT effects in magnetic tunnel junctions 2.5.1 Current-induced switching 2.5.2 Magnetisation precession 2.5.3 Bias-dependence of STT 2.5.4 Back-hopping 3 Experimental 3.1 Samples 3.1.1 Stack composition 3.1.2 Properties of samples used in this work 3.2 Experimental setup 3.2.1 Overview of equipment for the different measurement techniques 3.2.2 Electromagnet and Kepco power supply 3.2.3 Contacting of the sample 3.2.4 Principle specifications of equipment 3.3 Experimental techniques 3.3.1 Measurement of DC R-H and R-I loops 3.3.2 Measurement of phase diagrams: off and on-pulse 3.3.3 Thermally-excited ferromagnetic resonance 4 Results and discussion 4.1 Switching phase diagrams of MTJs 4.1.1 Theory: Calculating the phase diagram 4.1.2 Experimental phase diagrams 4.2 Thermally excited ferromagnetic resonance 4.2.1 Smoothing and fitting of raw data 4.2.2 Determination of Ms 4.2.3 Signal evolution with bias voltage 4.2.4 Analysis of peak position: perpendicular STT 4.2.5 Analysis of peak linewidth 5 Summary and outlook A Appendix List of figures List of tables BibliographyDiese Arbeit befasst sich mit Spin-Transfer-Torque-Effekten in MgO-basierten magnetischen Tunnelstrukturen. Die Phasendiagramme als Funktion von Spannung und Magnetfeld von CoFeB/MgO/CoFeB-Tunnelstrukturen mit Magnetisierung in der Ebene wurden experimentell bestimmt. Um thermische Anregungseffekte zu limitieren, wurden die Experimente einerseits mit nanosekundenlangen Spannungspulsen und andererseits bei niedrigen Temperaturen (4.2 K) durchgeführt. Die Spannungsabhängigkeit der beiden Spin-Torque-Parameter (in-plane und senkrechter Spin-Transfer-Torque) wurde aus Messungen der thermisch angeregten ferromagnetischen Resonanz bestimmt, wobei sich Werte ergaben, die gut mit vorangegangenen Untersuchungen übereinstimmen. Zusätzlich wurden Werte für Materialparameter wie die effektive Magnetisierung und den Dämpfungsparameter gewonnen. Unter Verwendung der erhaltenen Werte wurden die Schaltspannungen als Funktion des angelegten Magnetfeldes analytisch und numerisch berechnet, indem die erweiterte Landau-Lifshitz-Gilbert-Gleichung gelöst wurde. Im Gegensatz zu vorangegangenen Untersuchungen wurde der senkrechte Spin-Transfer-Torque dabei mit einbezogen. Darüber hinaus wurden verschiedene Konfigurationen für die Richtung der magnetischen Anisotropie der freien und fixierten Schicht berücksichtigt.:1 Introduction 2 Fundamentals 2.1 Magnetoresistance 2.1.1 Giant magnetoresistance 2.1.2 Tunnel magnetoresistance 2.2 Spin-transfer torque effect 2.2.1 Physical picture of the STT 2.2.2 In-plane and perpendicular STT 2.3 Equation of motion for the magnetisation 2.3.1 The Landau-Lifshitz-Gilbert equation 2.3.2 Extension including spin-transfer-torque (LLGS) 2.4 Applications of MR and spin-transfer torque 2.4.1 Read heads in hard disk drives 2.4.2 Spin-transfer torque magnetic random access memory 2.5 STT effects in magnetic tunnel junctions 2.5.1 Current-induced switching 2.5.2 Magnetisation precession 2.5.3 Bias-dependence of STT 2.5.4 Back-hopping 3 Experimental 3.1 Samples 3.1.1 Stack composition 3.1.2 Properties of samples used in this work 3.2 Experimental setup 3.2.1 Overview of equipment for the different measurement techniques 3.2.2 Electromagnet and Kepco power supply 3.2.3 Contacting of the sample 3.2.4 Principle specifications of equipment 3.3 Experimental techniques 3.3.1 Measurement of DC R-H and R-I loops 3.3.2 Measurement of phase diagrams: off and on-pulse 3.3.3 Thermally-excited ferromagnetic resonance 4 Results and discussion 4.1 Switching phase diagrams of MTJs 4.1.1 Theory: Calculating the phase diagram 4.1.2 Experimental phase diagrams 4.2 Thermally excited ferromagnetic resonance 4.2.1 Smoothing and fitting of raw data 4.2.2 Determination of Ms 4.2.3 Signal evolution with bias voltage 4.2.4 Analysis of peak position: perpendicular STT 4.2.5 Analysis of peak linewidth 5 Summary and outlook A Appendix List of figures List of tables Bibliograph

In-memory computing with emerging memory devices: Status and outlook

Supporting data for "In-memory computing with emerging memory devices: status and outlook", submitted to APL Machine Learning