Modeling Electrical Resistance Drift with Ultrafast Saturation of OTS Selectors

Crossbar array architecture is an essential design element for densely connected Non-Volatile Memory(NVM) applications. To overcome intrinsic sneak current problem of crossbar arrays, each memory unit is serially attached to a selector unit with highly nonlinear current-voltage (I-V) characteristics. Recently, Ovonic Threshold Switching (OTS) materials are preferred as selectors due to their fabrication compatibility with PRAM, MRAM or ReRAM technologies; however, OTS selectors suffer from the temporal drift of its threshold voltage. First, based on Poole-Frenkel conduction, we present time and temperature dependent model that predicts temporally evolving I-V characteristics,including threshold voltage of OTS selectors. Second, we report an ultrafast saturation ($\sim 10^3$ seconds) of the drift and extend the model to predict the time of drift saturation. Our model shows excellent agreement with OTS devices fabricated with 8 nm technology node at 25{\deg}C and 85{\deg}C ambient temperatures. The proposed model plays a significant role in understanding OTS device internals and the development of reliable threshold voltage jump table

Reconfigurable halide perovskite nanocrystal memristors for neuromorphic computing

Many in-memory computing frameworks demand electronic devices with specific switching characteristics to achieve the desired level of computational complexity. Existing memristive devices cannot be reconfigured to meet the diverse volatile and non-volatile switching requirements, and hence rely on tailored material designs specific to the targeted application, limiting their universality. “Reconfigurable memristors” that combine both ionic diffusive and drift mechanisms could address these limitations, but they remain elusive. Here we present a reconfigurable halide perovskite nanocrystal memristor that achieves on-demand switching between diffusive/volatile and drift/non-volatile modes by controllable electrochemical reactions. Judicious selection of the perovskite nanocrystals and organic capping ligands enable state-of-the-art endurance performances in both modes – volatile (2 × 10$^{6}$ cycles) and non-volatile (5.6 × 10$^{3}$ cycles). We demonstrate the relevance of such proof-of-concept perovskite devices on a benchmark reservoir network with volatile recurrent and non-volatile readout layers based on 19,900 measurements across 25 dynamically-configured devices

Beyinden esinlenen hesaplamalar için Ge2Sb2Te5 tabanlı sinaptik cihazların multifizik modellenmesi

Cataloged from PDF version of article.Thesis (M.S.): Bilkent University, Department of Electrical and Electronics Engineering, İhsan Doğramacı Bilkent University, 2018.Includes bibliographical references (leaves 65-76).Modeling nanoscale devices that emulate the functionality of synapses of the biological brain is a fundamental operation for developing brain-inspired computational systems. Phase-change material based synaptic devices offer promising performance in speed, spatial and power efficiency metrics, up to human brain level, when connected in a massively parallel crossbar array architecture. In this work, we modeled electrothermal characteristics of a single synaptic device consisting of phase-change material based memory and its selector. First, we proposed a finite element method based simulation framework for modeling electrical, thermal and probabilistic crystallization dynamics of the memory unit. Gradual phase transitions that form device memory between amorphous and crystalline states are studied under nanosecond voltage pulses. Second, we implemented time and temperature dependent resistance drift saturation model for phase-change material based selector device. Our model is in close agreement with the ultrafast saturation phenomena which is observed for the first time in fabricated devices with 8 nm node technology.by Yiğit Demirağ.M.S

Online Training of Spiking Recurrent Neural Networks with Phase-Change Memory Synapses

Spiking recurrent neural networks (RNNs) are a promising tool for solving a wide variety of complex cognitive and motor tasks, due to their rich temporal dynamics and sparse processing. However training spiking RNNs on dedicated neuromorphic hardware is still an open challenge. This is due mainly to the lack of local, hardware-friendly learning mechanisms that can solve the temporal credit assignment problem and ensure stable network dynamics, even when the weight resolution is limited. These challenges are further accentuated, if one resorts to using memristive devices for in-memory computing to resolve the von-Neumann bottleneck problem, at the expense of a substantial increase in variability in both the computation and the working memory of the spiking RNNs. To address these challenges and enable online learning in memristive neuromorphic RNNs, we present a simulation framework of differential-architecture crossbar arrays based on an accurate and comprehensive Phase-Change Memory (PCM) device model. We train a spiking RNN whose weights are emulated in the presented simulation framework, using a recently proposed e-prop learning rule. Although e-prop locally approximates the ideal synaptic updates, it is difficult to implement the updates on the memristive substrate due to substantial PCM non-idealities. We compare several widely adapted weight update schemes that primarily aim to cope with these device non-idealities and demonstrate that accumulating gradients can enable online and efficient training of spiking RNN on memristive substrates

1923 Lozan Antlaşması'nda imzalanan Türk - Yunan nüfus mübadelesinin, Girit mübadelesi odaklı incelenmesi"

Ankara : İhsan Doğramacı Bilkent Üniversitesi İktisadi, İdari ve Sosyal Bilimler Fakültesi, Tarih Bölümü, 2012.This work is a student project of the The Department of History, Faculty of Economics, Administrative and Social Sciences, İhsan Doğramacı Bilkent University.by Yasemin Başaran Doğan.Doğan, Yasemin Başaran. HIST 200-19DOĞAN HIST 200-19/3 2011-1

Mosaic: in-memory computing and routing for small-world spike-based neuromorphic systems

International audienceAbstract The brain’s connectivity is locally dense and globally sparse, forming a small-world graph—a principle prevalent in the evolution of various species, suggesting a universal solution for efficient information routing. However, current artificial neural network circuit architectures do not fully embrace small-world neural network models. Here, we present the neuromorphic Mosaic: a non-von Neumann systolic architecture employing distributed memristors for in-memory computing and in-memory routing, efficiently implementing small-world graph topologies for Spiking Neural Networks (SNNs). We’ve designed, fabricated, and experimentally demonstrated the Mosaic’s building blocks, using integrated memristors with 130 nm CMOS technology. We show that thanks to enforcing locality in the connectivity, routing efficiency of Mosaic is at least one order of magnitude higher than other SNN hardware platforms. This is while Mosaic achieves a competitive accuracy in a variety of edge benchmarks. Mosaic offers a scalable approach for edge systems based on distributed spike-based computing and in-memory routing

DenRAM: neuromorphic dendritic architecture with RRAM for efficient temporal processing with delays

Abstract Neuroscience findings emphasize the role of dendritic branching in neocortical pyramidal neurons for non-linear computations and signal processing. Dendritic branches facilitate temporal feature detection via synaptic delays that enable coincidence detection (CD) mechanisms. Spiking neural networks highlight the significance of delays for spatio-temporal pattern recognition in feed-forward networks, eliminating the need for recurrent structures. Here, we introduce DenRAM, a novel analog electronic feed-forward spiking neural network with dendritic compartments. Utilizing 130 nm technology integrated with resistive RAM (RRAM), DenRAM incorporates both delays and synaptic weights. By configuring RRAMs to emulate bio-realistic delays and exploiting their heterogeneity, DenRAM mimics synaptic delays and efficiently performs CD for pattern recognition. Hardware-aware simulations on temporal benchmarks show DenRAM’s robustness against hardware noise, and its higher accuracy over recurrent networks. DenRAM advances temporal processing in neuromorphic computing, optimizes memory usage, and marks progress in low-power, real-time signal processin

Reconfigurable halide perovskite nanocrystal memristors for neuromorphic computing

Many in-memory computing frameworks demand electronic devices with specific switching characteristics to achieve the desired level of computational complexity. Existing memristive devices cannot be reconfigured to meet the diverse volatile and non-volatile switching requirements, and hence rely on tailored material designs specific to the targeted application, limiting their universality. “Reconfigurable memristors” that combine both ionic diffusive and drift mechanisms could address these limitations, but they remain elusive. Here we present a reconfigurable halide perovskite nanocrystal memristor that achieves on-demand switching between diffusive/volatile and drift/non-volatile modes by controllable electrochemical reactions. Judicious selection of the perovskite nanocrystals and organic capping ligands enable state-of-the-art endurance performances in both modes – volatile (2 × 106 cycles) and non-volatile (5.6 × 103 cycles). We demonstrate the relevance of such proof-of-concept perovskite devices on a benchmark reservoir network with volatile recurrent and non-volatile readout layers based on 19,900 measurements across 25 dynamically-configured devices.ISSN:2041-172