Caractérisation et conception d' architectures basées sur des mémoires à changement de phase

Semiconductor memory has always been an indispensable component of modern electronic systems. The increasing demand for highly scaled memory devices has led to the development of reliable non-volatile memories that are used in computing systems for permanent data storage and are capable of achieving high data rates, with the same or lower power dissipation levels as those of current advanced memory solutions.Among the emerging non-volatile memory technologies, Phase Change Memory (PCM) is the most promising candidate to replace conventional Flash memory technology. PCM offers a wide variety of features, such as fast read and write access, excellent scalability potential, baseline CMOS compatibility and exceptional high-temperature data retention and endurance performances, and can therefore pave the way for applications not only in memory devices, but also in energy demanding, high-performance computer systems. However, some reliability issues still need to be addressed in order for PCM to establish itself as a competitive Flash memory replacement.This work focuses on the study of embedded Phase Change Memory in order to optimize device performance and propose solutions to overcome the key bottlenecks of the technology, targeting high-temperature applications. In order to enhance the reliability of the technology, the stoichiometry of the phase change material was appropriately engineered and dopants were added, resulting in an optimized thermal stability of the device. A decrease in the programming speed of the memory technology was also reported, along with a residual resistivity drift of the low resistance state towards higher resistance values over time.A novel programming technique was introduced, thanks to which the programming speed of the devices was improved and, at the same time, the resistance drift phenomenon could be successfully addressed. Moreover, an algorithm for programming PCM devices to multiple bits per cell using a single-pulse procedure was also presented. A pulse generator dedicated to provide the desired voltage pulses at its output was designed and experimentally tested, fitting the programming demands of a wide variety of materials under study and enabling accurate programming targeting the performance optimization of the technology.Les mémoires à base de semi-conducteur sont indispensables pour les dispositifs électroniques actuels. La demande croissante pour des dispositifs mémoires fortement miniaturisées a entraîné le développement de mémoires non volatiles fiables qui sont utilisées dans des systèmes informatiques pour le stockage de données et qui sont capables d'atteindre des débits de données élevés, avec des niveaux de dissipation d'énergie équivalents voire moindres que ceux des technologies mémoires actuelles.Parmi les technologies de mémoires non-volatiles émergentes, les mémoires à changement de phase (PCM) sont le candidat le plus prometteur pour remplacer la technologie de mémoire Flash conventionnelle. Les PCM offrent une grande variété de fonctions, comme une lecture et une écriture rapide, un excellent potentiel de miniaturisation, une compatibilité CMOS et des performances élevées de rétention de données à haute température et d'endurance, et peuvent donc ouvrir la voie à des applications non seulement pour les dispositifs mémoires, mais également pour les systèmes informatiques à hautes performances. Cependant, certains problèmes de fiabilité doivent encore être résolus pour que les PCM se positionnent comme un remplacement concurrentiel de la mémoire Flash.Ce travail se concentre sur l'étude de mémoires à changement de phase intégrées afin d'optimiser leurs performances et de proposer des solutions pour surmonter les principaux points critiques de la technologie, ciblant des applications à hautes températures. Afin d'améliorer la fiabilité de la technologie, la stœchiométrie du matériau à changement de phase a été conçue de façon appropriée et des dopants ont été ajoutés, optimisant ainsi la stabilité thermique. Une diminution de la vitesse de programmation est également rapportée, ainsi qu'un drift résiduel de la résistance de l'état de faiblement résistif vers des valeurs de résistance plus élevées au cours du temps.Une nouvelle technique de programmation est introduite, permettant d'améliorer la vitesse de programmation des dispositifs et, dans le même temps, de réduire avec succès le phénomène de drift en résistance. Par ailleurs, un algorithme de programmation des PCM multi-bits est présenté. Un générateur d'impulsions fournissant des impulsions avec la tension souhaitée en sortie a été conçu et testé expérimentalement, répondant aux demandes de programmation d'une grande variété de matériaux innovants et en permettant la programmation précise et l’optimisation des performances des PCM

Fast and reliable storage using a 5 bit, nonvolatile photonic memory cell

This is the final version. Available from Optical Society of America via the DOI in this record.Optically storing and addressing data on photonic chips is of particular interest as such capability would eliminate optoelectronic conversion losses in data centers. It would also enable on-chip non-von Neumann photonic computing by allowing multinary data storage with high fidelity. Here, we demonstrate such an optically addressed, multilevel memory capable of storing up to 34 nonvolatile reliable and repeatable levels (over 5 bits) using the phase change material Ge2Sb2Te5 integrated on a photonic waveguide. Crucially, we demonstrate for the first time, to the best of our knowledge, a technique that allows us to program the device with a single pulse regardless of the previous state of the material, providing an order of magnitude improvement over previous demonstrations in terms of both time and energy consumption. We also investigate the influence of write-and-erase pulse parameters on the single-pulse recrystallization, amorphization, and readout error in our multilevel memory, thus tailoring pulse properties for optimum performance. Our work represents a significant step in the development of photonic memories and their potential for novel integrated photonic applications.Engineering and Physical Sciences Research Council (EPSRC)European CommissionDeutsche Forschungsgemeinschaft (DFG)Horizon 2020 Framework Programme (H2020

High-Density Solid-State Memory Devices and Technologies

This Special Issue aims to examine high-density solid-state memory devices and technologies from various standpoints in an attempt to foster their continuous success in the future. Considering that broadening of the range of applications will likely offer different types of solid-state memories their chance in the spotlight, the Special Issue is not focused on a specific storage solution but rather embraces all the most relevant solid-state memory devices and technologies currently on stage. Even the subjects dealt with in this Special Issue are widespread, ranging from process and design issues/innovations to the experimental and theoretical analysis of the operation and from the performance and reliability of memory devices and arrays to the exploitation of solid-state memories to pursue new computing paradigms

Small business innovation research. Abstracts of 1988 phase 1 awards

Non-proprietary proposal abstracts of Phase 1 Small Business Innovation Research (SBIR) projects supported by NASA are presented. Projects in the fields of aeronautical propulsion, aerodynamics, acoustics, aircraft systems, materials and structures, teleoperators and robots, computer sciences, information systems, data processing, spacecraft propulsion, bioastronautics, satellite communication, and space processing are covered

2022 roadmap on neuromorphic computing and engineering

Modern computation based on von Neumann architecture is now a mature cutting-edge science. In the von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 10$^{18}$ calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community

Small business innovation research. Abstracts of completed 1987 phase 1 projects

Non-proprietary summaries of Phase 1 Small Business Innovation Research (SBIR) projects supported by NASA in the 1987 program year are given. Work in the areas of aeronautical propulsion, aerodynamics, acoustics, aircraft systems, materials and structures, teleoperators and robotics, computer sciences, information systems, spacecraft systems, spacecraft power supplies, spacecraft propulsion, bioastronautics, satellite communication, and space processing are covered

Analog Spiking Neuromorphic Circuits and Systems for Brain- and Nanotechnology-Inspired Cognitive Computing

Human society is now facing grand challenges to satisfy the growing demand for computing power, at the same time, sustain energy consumption. By the end of CMOS technology scaling, innovations are required to tackle the challenges in a radically different way. Inspired by the emerging understanding of the computing occurring in a brain and nanotechnology-enabled biological plausible synaptic plasticity, neuromorphic computing architectures are being investigated. Such a neuromorphic chip that combines CMOS analog spiking neurons and nanoscale resistive random-access memory (RRAM) using as electronics synapses can provide massive neural network parallelism, high density and online learning capability, and hence, paves the path towards a promising solution to future energy-efficient real-time computing systems. However, existing silicon neuron approaches are designed to faithfully reproduce biological neuron dynamics, and hence they are incompatible with the RRAM synapses, or require extensive peripheral circuitry to modulate a synapse, and are thus deficient in learning capability. As a result, they eliminate most of the density advantages gained by the adoption of nanoscale devices, and fail to realize a functional computing system. This dissertation describes novel hardware architectures and neuron circuit designs that synergistically assemble the fundamental and significant elements for brain-inspired computing. Versatile CMOS spiking neurons that combine integrate-and-fire, passive dense RRAM synapses drive capability, dynamic biasing for adaptive power consumption, in situ spike-timing dependent plasticity (STDP) and competitive learning in compact integrated circuit modules are presented. Real-world pattern learning and recognition tasks using the proposed architecture were demonstrated with circuit-level simulations. A test chip was implemented and fabricated to verify the proposed CMOS neuron and hardware architecture, and the subsequent chip measurement results successfully proved the idea. The work described in this dissertation realizes a key building block for large-scale integration of spiking neural network hardware, and then, serves as a step-stone for the building of next-generation energy-efficient brain-inspired cognitive computing systems