Quest for a solution to drift in phase change memory devices

The goal of this thesis is to gain new insights into the drift phenomenon and identify strategies to mitigate it. An extensive experimental characterization of PCM devices and in particular drift forms the foundation of each chapter. With respect to time-scales, ambient temperature, device dimensions, and combinations thereof, drift is studied under unprecedented conditions. In three studies, different aspects of drift are examined. (1) The origin of structural relaxation: Drift measurements over 9 orders of magnitude in time reveal the onset of relaxation in a melt-quenched state. The data is used to appraise two models, the Gibbs relaxation model and the collective relaxation model. Additionally, a refined version of the collective relaxation model is introduced and the consequences of a limited number of structural defects are discussed. (2) Exploiting nanoscale effects in phase change memories: Scaling devices to ever-smaller dimensions is incentivized by the requirement to achieve higher storage densities and less power consumption. Eventually, confinement and interfacial effects will govern the device characteristics. Anticipating these consequences, the feasibility to use a single element, Antimony, is assessed for the first time. The power efficiency, stability against crystallization, and drift are characterized under different degrees of confinement. (3) State-dependent drift in a projected memory cell: New device concepts are aiming to reduce drift by decoupling the cell resistance from the electronic properties of the amorphous phase. A shunt resistor scaling with the amount of amorphous material is added. Simulations and the drift characteristics of a projected device put the idealized concept to the test. The contact resistance between the phase change material and the shunt resistor is identified as a decisive parameter to achieve the desired device properties.Comment: PhD thesi

Electronic mechanism for resistance drift in phase-change memory materials: Link to persistent photoconductivity

‘Phase-change’ memory materials, such as the canonical composition Ge2Sb2Te5, are being actively researched for non-volatile resistive random-access memory applications. In these devices, ultra-rapid reversible transformations between metastable highly electrically conducting (degenerate-semiconducting) crystalline and more electrically resistive (semiconducting) glassy phases are produced by the application of appropriate voltage pulses. Multilevel programming, wherein more than two metastable resistance states can be stored in the memory material as different proportions of partially glassy/crystalline regions, allows more than one bit to be stored per memory cell. However, this route to increasing data density, without recourse to device-size down-scaling, is threatened by the phenomenon of ‘resistance drift’, wherein the electrical resistance of the glassy phase slowly increases with time, following a weak power-law dependence, after being written with a voltage pulse. In this paper, we propose an intrinsic electronic mechanism for the resistance drift by identifying it with the phenomenon of persistent photoconductivity that is commonly observed in a wide range of disordered semiconductors. We develop a model for it in terms of the long-time, deep-trap release and subsequent recombination of charge carriers, akin to that which is believed to be responsible for the long-time photocurrent decay in amorphous semiconductors, such as hydrogenated amorphous silicon. In this case, the parameters controlling the resistance drift are the widths of the (localized) valence- and conduction-band tails in the vicinity of the bandgap. Hence, there is the potential for mitigating resistance drift in the amorphous state of phase-change memory materials by suitable material engineering (e.g. via compositional or fabricational control) to control the extent of band-tailing, thereby facilitating the future introduction of multistate memory

Accumulation-based computing using phasechange memories with FET access devices

Copyright © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Phase-change materials and devices have received much attention as a potential route to the realization of various types of unconventional computing paradigms. In this letter, we present non-von Neumann arithmetic processing that exploits the accumulative property of phase-change memory (PCM) cells. Using PCM cells with integrated FET access devices, we perform a detailed study of accumulation-based computation. We also demonstrate efficient factorization using PCM cells, a technique that could pave the way for massively parallelized computations.Engineering and Physical Sciences Research Council (EPSRC

Collective signal processing in cluster chemotaxis: roles of adaptation, amplification, and co-attraction in collective guidance

Single eukaryotic cells commonly sense and follow chemical gradients, performing chemotaxis. Recent experiments and theories, however, show that even when single cells do not chemotax, clusters of cells may, if their interactions are regulated by the chemoattractant. We study this general mechanism of "collective guidance" computationally with models that integrate stochastic dynamics for individual cells with biochemical reactions within the cells, and diffusion of chemical signals between the cells. We show that if clusters of cells use the well-known local excitation, global inhibition (LEGI) mechanism to sense chemoattractant gradients, the speed of the cell cluster becomes non-monotonic in the cluster's size - clusters either larger or smaller than an optimal size will have lower speed. We argue that the cell cluster speed is a crucial readout of how the cluster processes chemotactic signal; both amplification and adaptation will alter the behavior of cluster speed as a function of size. We also show that, contrary to the assumptions of earlier theories, collective guidance does not require persistent cell-cell contacts and strong short range adhesion to function. If cell-cell adhesion is absent, and the cluster cohesion is instead provided by a co-attraction mechanism, e.g. chemotaxis toward a secreted molecule, collective guidance may still function. However, new behaviors, such as cluster rotation, may also appear in this case. Together, the combination of co-attraction and adaptation allows for collective guidance that is robust to varying chemoattractant concentrations while not requiring strong cell-cell adhesion.Comment: This article extends some results previously presented in arXiv:1506.0669

Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses

Spiking neural networks (SNN) are artificial computational models that have been inspired by the brain's ability to naturally encode and process information in the time domain. The added temporal dimension is believed to render them more computationally efficient than the conventional artificial neural networks, though their full computational capabilities are yet to be explored. Recently, computational memory architectures based on non-volatile memory crossbar arrays have shown great promise to implement parallel computations in artificial and spiking neural networks. In this work, we experimentally demonstrate for the first time, the feasibility to realize high-performance event-driven in-situ supervised learning systems using nanoscale and stochastic phase-change synapses. Our SNN is trained to recognize audio signals of alphabets encoded using spikes in the time domain and to generate spike trains at precise time instances to represent the pixel intensities of their corresponding images. Moreover, with a statistical model capturing the experimental behavior of the devices, we investigate architectural and systems-level solutions for improving the training and inference performance of our computational memory-based system. Combining the computational potential of supervised SNNs with the parallel compute power of computational memory, the work paves the way for next-generation of efficient brain-inspired systems

Memristors for the Curious Outsiders

We present both an overview and a perspective of recent experimental advances and proposed new approaches to performing computation using memristors. A memristor is a 2-terminal passive component with a dynamic resistance depending on an internal parameter. We provide an brief historical introduction, as well as an overview over the physical mechanism that lead to memristive behavior. This review is meant to guide nonpractitioners in the field of memristive circuits and their connection to machine learning and neural computation.Comment: Perpective paper for MDPI Technologies; 43 page