Analytic Model for Statistical State Instability and Retention Behaviors of Filamentary Analog RRAM Array and Its Applications in Design of Neural Network

Huang, Pei; Xiang, Yachen; Zhao, Ying; Liu, C.; Gao, Bin; Wu, Huaqiang; Qian, He; Liu, X. Y.; Kang, Jinfeng

doi:10.1109/iedm.2018.8614567

Cited by 16 publications

(22 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This widening can be further confirmed by looking at the increase in the standard deviation of the current distribution in Figure c. This increase in the standard deviation fits to the experimental results in Figure b as well as to the observations of other groups. , …”

Section: Resultssupporting

confidence: 83%

A Consistent Model for Short-Term Instability and Long-Term Retention in Filamentary Oxide-Based Memristive Devices

Kopperberg

Wiefels

Liberda

et al. 2021

ACS Appl. Mater. Interfaces

View full text Add to dashboard Cite

Major challenges concerning the reliability of resistive switching random access memories based on the valence change mechanism (VCM) are short-term instability and long-term retention failure of the programmed resistance state, particularly in the high resistive state. On the one hand, read noise limits the reliability of VCMs via comparatively small current jumps especially when looking at the statistics of millions of cells that are needed for industrial applications. Additionally, shaping algorithms aiming for an enlargement of the read window are observed to have no lasting effect. On the other hand, long-term retention failures limiting the lifetime of the programmed resistance states need to be overcome. The physical origin of these phenomena is still under debate and needs to be understood much better. In this work, we present a three-dimensional kinetic Monte Carlo simulation model where we implemented diffusion-limiting domains to the oxide layer of the VCM cell. We demonstrate that our model can explain both instability and retention failure consistently by the same physical processes. Further, we find that the random diffusion of oxygen vacancies plays an important role regarding the reliability of VCMs and can explain instability phenomena as the shaping failure as well as the long-term retention failure in our model. Additionally, the results of the simulations are compared with experimental data of read noise and retention investigations on ZrO2-based VCM devices.

show abstract

Section: Resultssupporting

confidence: 83%

A Consistent Model for Short-Term Instability and Long-Term Retention in Filamentary Oxide-Based Memristive Devices

Kopperberg

Wiefels

Liberda

et al. 2021

ACS Appl. Mater. Interfaces

View full text Add to dashboard Cite

show abstract

“…For the analog RRAM, the distribution of multi-level resistance states is widely spread. The wide conductance distribution causes the overlap of neighboring conductance states, resulting in retention degradation (Huang et al, 2018). In addition, after programming the device to the target conductance state, the conductance of the device may experience a notable change in a short time scale, forming tail bits (Xu et al, 2020).…”

Section: Device Optimization With Multifunctional Assistant Layermentioning

confidence: 99%

“…Although analog RRAM shows great potential in weight storage and weight updating, it suffers from serious state instability and retention degradation issues, which greatly affect the performance of neural network. A physics-based analytic model is developed to describe the statistical state instability and retention behaviors of analog RRAM (Huang et al, 2018). In the model, the diffusion of Vo, the Brownian-like hopping of Vo during diffusion, and the recombination of Vo are considered.…”

Section: Analog Resistive Switching Random Access Memorymentioning

confidence: 99%

“…With the feature of inherent variability, RRAM shows great potential to be used as low-cost and energy-efficient stochastic number generator enabling stochastic computing, which emulates the generation of neural spikes processed by the human brain in the form of long sequences of noisy voltage spikes (Gaba et al, 2013;Suri et al, 2013;Knag et al, 2014;Moons and Verhelst, 2014;Ielmini and Wong, 2018;Wang et al, 2018;Carboni and Ielmini, 2019;Zhao et al, 2019). For the design and optimization of these brain-inspired computing systems, related physics-based models and simulation platforms have been developed to bridge the link between device, circuit, and system, which aims to meet the requirement for the device-circuitsystem co-design (Gao et al, 2011;Guan et al, 2012;Huang et al, 2013Huang et al, , 2017Huang et al, , 2018Chen et al, 2017;Larcher et al, 2017;Pedretti et al, 2017;Zhao et al, 2019;Cai et al, 2020;Liao et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modeling-Based Design of Memristive Devices for Brain-Inspired Computing

Zhao

Chen

Huang

et al. 2021

Front. Nanotechnol.

View full text Add to dashboard Cite

Resistive switching random access memory (RRAM) has emerged for non-volatile memory application with the features of simple structure, low cost, high density, high speed, low power, and CMOS compatibility. In recent years, RRAM technology has made significant progress in brain-inspired computing paradigms by exploiting its unique physical characteristics, which attempts to eliminate the energy-intensive and time-consuming data transfer between the processing unit and the memory unit. The design of RRAM-based computing paradigms, however, requires a detailed description of the dominant physical effects correlated with the resistive switching processes to realize the interaction and optimization between devices and algorithms or architectures. This work provides an overview of the current progress on device-level resistive switching behaviors with detailed insights into the physical effects in the resistive switching layer and the multifunctional assistant layer. Then the circuit-level physics-based compact models will be reviewed in terms of typical binary RRAM and the emerging analog synaptic RRAM, which act as an interface between the device and circuit design. After that, the interaction between device and system performances will finally be addressed by reviewing the specific applications of brain-inspired computing systems including neuromorphic computing, in-memory logic, and stochastic computing.

show abstract

“…Read disturb describes the fact that over multiple readouts of a VCM device its resistance might drift from its initial value or even completely switch to the opposite state [16][17][18]. Read noise describes the fact that when reading out a VCM device the read-out current is not constant but will show random jumps whose height depends on the resistance value [19][20][21][22]. This noise is often referred to as random telegraph noise (RTN) [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Reliability aspects of binary vector-matrix-multiplications using ReRAM devices

Bengel

Mohr

Wiefels

et al. 2022

Neuromorph. Comput. Eng.

View full text Add to dashboard Cite

Computation-In-Memory (CIM) using memristive devices is a promising approach to overcome the performance limitations of conventional computing architectures introduced by the von Neumann bottleneck which are also known as memory wall and power wall. It has been shown that accelerators based on memristive devices can deliver higher energy efficiencies and data throughputs when compared with conventional architectures. In the vast multitude of memristive devices, bipolar resistive switches (BRS) based on the valence change mechanism (VCM) are particularly interesting due to their low power operation, non-volatility, high integration density and their CMOS compatibility. While a wide range of possible applications is considered, many of them such as artificial neural networks heavily rely on Vector-Matrix-Multiplications (VMMs) as a mathematical operation. These VMMs are made up of large numbers of Multiplication and Accumulation (MAC) operations. The MAC operation can be realised using memristive devices in an analog fashion using Ohm’s law and Kirchhoff’s law. However, VCM devices exhibit a range of non-idealities, affecting the VMM performance, which in turn impacts the overall accuracy of the application. Those non-idealities can be classified into time-independent (programming variability) and timedependent (read disturb and read noise). Additionally, peripheral circuits such as Analog to Digital Converters (ADCs) can introduce errors during the digitalization. In this work, we experimentally and theoretically investigate the impact of deviceand circuit-level effects on the VMM in a VCM crossbars. Our analysis shows that the variability of the Low Resistive State (LRS) plays a key role and that reading in the RESET direction should be favored to reading in the SET direction.

show abstract

Analytic Model for Statistical State Instability and Retention Behaviors of Filamentary Analog RRAM Array and Its Applications in Design of Neural Network

Cited by 16 publications

References 4 publications

A Consistent Model for Short-Term Instability and Long-Term Retention in Filamentary Oxide-Based Memristive Devices

A Consistent Model for Short-Term Instability and Long-Term Retention in Filamentary Oxide-Based Memristive Devices

Modeling-Based Design of Memristive Devices for Brain-Inspired Computing

Reliability aspects of binary vector-matrix-multiplications using ReRAM devices

Contact Info

Product

Resources

About