HRS Instability in Oxide-Based Bipolar Resistive Switching Cells

Wiefels, Stefan; Bengel, Christopher; Kopperberg, Nils; Zhang, Kaihua; Waser, Rainer; Menzel, Stephan

doi:10.1109/ted.2020.3018096

Cited by 37 publications

(34 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Toward the industrial application of ReRAM, its reliability is highly relevant [10]. Along with variability [11] and retention [12], the endurance or maximum number of switching cycles until failure is one of the most important reliability aspects. While 10 4 -10 5 cycles are reported as typical endurance for flash memory [10], several groups reported ReRAM devices with an endurance of 10 7 − 10 12 cycles [13]- [18].…”

Section: Impact Of the Ohmic Electrode On The Endurancementioning

confidence: 99%

Impact of the Ohmic Electrode on the Endurance of Oxide-Based Resistive Switching Memory

Wiefels

Witzleben

Huttemann

et al. 2021

IEEE Trans. Electron Devices

Self Cite

View full text Add to dashboard Cite

As one of the key aspects in the reliability of redox-based resistive switching memories (ReRAMs), maximizing their endurance is of high relevance for industrial applications. The major limitation regarding endurance is considered the excessive generation of oxygen vacancies during cycling, which eventually leads to irreversible RESET failures. Thus, the endurance could be increased by using combinations of switching oxide and ohmic electrode (OE) metal that provides a high barrier for the generation of oxygen vacancies [defect formation energy (DFE)]. In this work, we present a sophisticated programming algorithm that aims to maximize the endurance within reasonable measurement time. Using this algorithm, we compare ReRAM devices with four different OE metals and confirm the theoretically predicted trend. Thus, our work provides valuable information for device engineering toward higher endurance.

show abstract

Section: Impact Of the Ohmic Electrode On The Endurancementioning

confidence: 99%

Impact of the Ohmic Electrode on the Endurance of Oxide-Based Resistive Switching Memory

Wiefels

Witzleben

Huttemann

et al. 2021

IEEE Trans. Electron Devices

Self Cite

View full text Add to dashboard Cite

show abstract

“…[18,24,27,32] In VCM cells, significant progress has also been made regarding the understanding of oxygen vacancy defect formation, migration [15,33] and cell switching stability. [34,35] Multiple approaches such as selecting ohmic electrode material with proper defect formation energy, [36,37] applying multilayer oxides, [21,38,39] have been attempted to improve cell performance. However, in most of the reported works, the conclusions were drawn based on studies containing only one type of variable, either the specie of active (ohmic) electrode material, or the counter (bottom) electrode material, overlooking the comprehensive analysis considering the configuration/combination of the electrode/electrolyte materials as a system.…”

Section: Introductionmentioning

confidence: 99%

Design of Materials Configuration for Optimizing Redox‐Based Resistive Switching Memories

Chen

Valov

2021

Advanced Materials

View full text Add to dashboard Cite

random access memory (DRAM) and flash memory are reaching the physical scaling limits. To tackle this problem, emerging memory technologies have been proposed during last years. [8][9][10] Among them, redoxbased resistive random access memories (ReRAMs) have received special attention for its CMOS-compatible fabrication, performance, multi-functionality and scaling potential. [1,11,12] It is recognized important building block for next generation storage memories, computation-in-memory architecture and artificial intelligence. [3,8,[10][11][12] ReRAM is a two-terminal metal-insulatormetal cell. The electrical conductivity of the insulating layer, typically a transition metal oxide, can be tuned by ionic modulation, caused by external electrical stimulation. [11] The oxide film has the ability to conduct ions such as metal cation, protonic charge and oxygen ions/vacancies, therefore it is often referred to as solid electrolyte. [13][14][15] Depending on the functional principles, two type ReRAMs are particularly promising-electrochemical metallization memory (ECM) and valencechange memory (VCM). [11,16,17] The resistive switching in ECM cells relies on a metallic filament that forms, and respectively dissolves, between the active electrode and counter electrode. [16] The formation of filament corresponds to a SET process, during which the cell switches from high resistive state (HRS) to low resistive state (LRS). The SET process is accompanied by multiple individual electrochemical processes, i.e., ionization (oxidation) of active electrode, diffusion of metal cation in the oxide electrolyte and nucleation/growth at counter electrode. The application of reverse potential switches the cell back to HRS by oxidizing/dissolving the filament, resulting in a RESET process. Electrochemically active metals, such as Ag, Cu, or their alloys/ compounds are typically employed as active electrodes. [13,18,19] The counter electrode is made by inert materials like Pt, Ir or compounds such as TiN. [18][19][20] VCM cells consist of bottom electrode with high work function (e.g., Pt, TiN) which forms a Schottky interface with the oxide. The top electrode is electrochemically active, typically metal with high oxygen affinity (e.g., Ta, Ti, Hf) which allows redox reactions/ion exchange and forms an ohmic contact to the oxide, favoring the oxygen vacancy defect formation. [21,22] It is widely accepted that the resistive switching for VCM cell is achieved by modulating the electrostatic barrier at Schottky interface by migration and redistribution of oxygen vacancy defects. [11,23] Redox-based resistive random access memories (ReRAMs) are based on electrochemical processes of oxidation and reduction within the devices. The selection of materials and material combinations strongly influence the related nanoscale processes, playing a crucial role in resistive switching properties and functionalities. To date, however, comprehensive studies on device design accounting for a combination of factors such as electrodes, electrolytes, and capp...

show abstract

“…Minor deviations at the lower and upper boundary are noticeable. This behavior has been studied before (Fantini et al, 2015;Wiefels et al, 2020) and can be explained by ionic noise that is typically present in filamentary VCM systems.…”

Section: Device-to-device Variability Characterization On 1 µS Timescalementioning

confidence: 92%

Utilizing the Switching Stochasticity of HfO2/TiOx-Based ReRAM Devices and the Concept of Multiple Device Synapses for the Classification of Overlapping and Noisy Patterns

et al. 2021

Self Cite

View full text Add to dashboard Cite

With the arrival of the Internet of Things (IoT) and the challenges arising from Big Data, neuromorphic chip concepts are seen as key solutions for coping with the massive amount of unstructured data streams by moving the computation closer to the sensors, the so-called “edge computing.” Augmenting these chips with emerging memory technologies enables these edge devices with non-volatile and adaptive properties which are desirable for low power and online learning operations. However, an energy- and area-efficient realization of these systems requires disruptive hardware changes. Memristor-based solutions for these concepts are in the focus of research and industry due to their low-power and high-density online learning potential. Specifically, the filamentary-type valence change mechanism (VCM memories) have shown to be a promising candidate In consequence, physical models capturing a broad spectrum of experimentally observed features such as the pronounced cycle-to-cycle (c2c) and device-to-device (d2d) variability are required for accurate evaluation of the proposed concepts. In this study, we present an in-depth experimental analysis of d2d and c2c variability of filamentary-type bipolar switching HfO2/TiOx nano-sized crossbar devices and match the experimentally observed variabilities to our physically motivated JART VCM compact model. Based on this approach, we evaluate the concept of parallel operation of devices as a synapse both experimentally and theoretically. These parallel synapses form a synaptic array which is at the core of neuromorphic chips. We exploit the c2c variability of these devices for stochastic online learning which has shown to increase the effective bit precision of the devices. Finally, we demonstrate that stochastic switching features for a pattern classification task that can be employed in an online learning neural network.

show abstract

HRS Instability in Oxide-Based Bipolar Resistive Switching Cells

Cited by 37 publications

References 25 publications

Impact of the Ohmic Electrode on the Endurance of Oxide-Based Resistive Switching Memory

Impact of the Ohmic Electrode on the Endurance of Oxide-Based Resistive Switching Memory

Design of Materials Configuration for Optimizing Redox‐Based Resistive Switching Memories

Utilizing the Switching Stochasticity of HfO2/TiOx-Based ReRAM Devices and the Concept of Multiple Device Synapses for the Classification of Overlapping and Noisy Patterns

Contact Info

Product

Resources

About