Search query: Resistive random access memory (reram) based on metal oxide

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abstract: In this paper, we review the recent progress in the resistive random access memory (ReRAM) technology, one of the most promising emerging nonvolatile memories, in which both electronic and electrochemical effects play important roles in the nonvolatile functionalities. First, we provide a brief historical overview of the research in this field. We also provide a technological overview and the epoch making achievements, followed by an account of the current understanding of both bipolar and unipolar ReRAM operations. Finally, we summarize the challenges facing the ReRAM technology as it moves toward the beyond 2X nm generation of nonvolatile memories and the so called beyond complementary metal oxide semiconductor (CMOS) device.
author_names: Hiroyuki Akinaga

Hisashi Shima
title: Resistive Random Access Memory (ReRAM) Based on Metal Oxides
venue: Proceedings of the IEEE
year: 2010

abstract: Transition Metal Oxide (TMO) Based Resistive Random Access Memory (ReRAM) devices have gathered significant research attention for non volatile data storage applications. The major advantages lie in terms of scalability, low switching voltages, and process compatibility with the CMOS technologies [1, 2] However, to take the complete benefit of this enabling technology there are several challenges that need to be addressed. This talk will present our work towards addressing these challenges for ReRAM devices.
author_names: Rashmi Jha
title: Analog and Digital Switching Characteristics of Transition Metal Oxide Based Resistive Random Access Memory ReRAM Devices
year: 2013

abstract: In this work, we have inspected the theoretical resistive switching properties of two ReRAM models based on heterojunction structures of Cu/SiOx nanoparticles (NPs)/Si and Si/SiOx NPs/Si, in which dielectric layers of the silica nanoparticles present dislocations at bicrystal interfaces. To validate the theoretical model, Cu/SiOx/Si was experimentally done and their ReRAM properties were elucidated. Our examinations on the electrical, thermal and structural aspects of resistive switching uncovered the switching behavior relies upon the material properties and electrical characteristics of the switching layers, as well as the metal electrodes and the interfacial structure of grains within the dielectric materials. We also determined that the application of an external electric field at Grain Boundaries (GB) is crucial to resistive switching behavior. Moreover, we have demonstrated that the switching behavior is influenced by variations in the atomic structure and electronic properties, at the atomic length scale and picosecond timescale. Our findings furnish a useful reference for the future development and optimization of materials used in this technology.
author_names: Yu-Li Chen

Mon-Shu Ho

Wen-Jay Lee

Pei-Fang Chung

Babu Balraj

Chandrasekar Sivakumar
title: The mechanism underlying silicon oxide based resistive random access memory (ReRAM)
venue: Nanotechnology
year: 2019

abstract: Optimization and performance enhancement of low cost and solution processed InGaZnO (IGZO) resistance random access memory (ReRAM) device was demonstrated on the basis of manipulation of global and local oxygen vacancy (Vo) stoichiometry in metal oxide thin films. Controlled overall Ga composition within IGZO thin film reduced the excessive formation of oxygen vacancy for reproducible resistance switching mechanism. Furthermore, local sophisticated control of stoichiometric Vousing 5 nm Ni layer at the interface of IGZO layer consequently serves as an oxygen capturing layer by forming NiOx, consequently facilitating the formation of conductive filaments and also preventing the abrupt degradation of device performance. Additionally, reducing the cell dimension of IGZO based ReRAMs using a cross bar electrode structure appeared to drastically improve their performances such as the operation voltage and resistance distribution due to suppression of excessive conductive filament formation. Optimized ReRAM devices exhibit a stable unipolar resistive switching behavior with an endurance >200 cycles, retention time for 104sec at 85 degC and on/off ratio higher than about 102. Therefore, it can be claimed that our findings address the demanding issues of low cost memory devices with high stability and endurance for next generation data storage technology.
author_names: Dongyun Lee

Min Chul Chun

Hyungduk Ko

Bo Soo Kang

Jaekyun Kim
title: Highly stable, solution processed quaternary oxide thin film based resistive switching random access memory devices via global and socal stoichiometric manipulation strategy.
venue: Nanotechnology
year: 2020

abstract: Abstract In this paper, flexible resistive random access memory (ReRAM) operating at 4 V with sol gel based zirconium oxide (ZrO2) film at 200 degC is demonstrated. The memory performance of solution processed ReRAM can be improved by utilizing oxygen annealing, which results in high quality metal oxide dielectric films with suppressed oxygen vacancies. The effects of oxygen annealing on metal oxide bonding states are investigated to explain the origin of the improved switching performance in ReRAM. In addition, the conduction mechanism underlying the charge transport in solution processed ReRAM by oxygen annealing at 200 degC is investigated in a comparison to that of ReRAM annealed at 400 degC in air. The activation energy of solution processed ReRAM is determined by an Arrhenius plot from temperature dependent measurements in the range of 120 300 K. Finally, highly stable and robust flexible ReRAM without a passivation film against cyclic bending tests with curvature radiuses of 10 to 5 mm is demonstrated. We believe that this work provides a practical and effective approach by which to improve the device performance of ReRAM with sol gel based metal oxide dielectrics through oxygen annealing at low temperature, compatible with flexible substrates.
author_names: Sangjoon Park

Byoung-Soo Yu

Jun-Young Jeon

Byoung-Cheol Kang

Tae-Jun Ha
title: Sol gel based zirconium dioxide dielectrics by oxygen annealing at low temperature for highly stable and robust flexible resistive random access memory
year: 2020

abstract: The sparsity in the deep neural networks (DNNs) can be leveraged by methods such as pruning and quantization to assist the energy efficient deployment of large scale deep neural networks onto hardware platforms, such as GPU and ASIC, for better performance and power efficiency. However, for the metal oxide resistive random access memory (ReRAM) architecture, the study of energy efficient methods still shrink the model size or constrain the precision of DNN by leveraging the DNN sparsity. Due to the circuit features of ReRAM, reading bit 0 naturally consumes less energy than reading bit 1. In this paper, we exploit the fine grained tuning method on the bit level to reduce energy consumption of ReRAM. Specifically, we present the gradient search and the weight group update algorithm, which can significantly unbalance the proportion of bit 1 and bit 0 inside the weights of DNN with negligible NN accuracy loss. Experiments demonstrate that the percentage of bit 0, in some typical convolutional neural networks (CNNs) increases to 33.8% with less than 0.5% degradation in NN accuracy. The energy reduction can be up to 65%
author_names: Zhuoran Song

Yilong Zhao

Yanan Sun

Xiaoyao Liang

Li Jiang
title: ESNreram: An Energy Efficient Sparse Neural Network Based on Resistive Random Access Memory
venue: ACM Great Lakes Symposium on VLSI
year: 2020

abstract: The traditional Boolean computing paradigm based on the von Neumann architecture is facing great challenges for future information technology applications such as big data, the Internet of Things (IoT) and wearable devices, due to the limited processing capability issues such as binary data storage and computing, non parallel data processing, and the buses requirement between memory units and logic units. The brain inspired neuromorphic computing paradigm is believed to be one of the promising solutions for realizing more complex functions with a lower cost. To perform such brain inspired computing with a low cost and low power consumption, novel devices for use as electronic synapses are needed. Metal oxide resistive random access memory (ReRAM) devices have emerged as the leading candidate for electronic synapses. This paper comprehensively addresses the recent work on the design and optimization of metal oxide ReRAM based synaptic devices. A performance enhancement methodology and optimized operation scheme to achieve analog resistive switching and low energy training behavior are provided. A three dimensional vertical synapse network architecture is proposed for high density integration and low cost fabrication. The impacts of the ReRAM synaptic device features on the performances of neuromorphic systems are also discussed on the basis of a constructed neuromorphic visual system with a pattern recognition function. Possible solutions to achieve the high recognition accuracy and efficiency of neuromorphic systems are presented.
author_names: Bin Gao

Jinfeng Kang

Zheng Zhou

Zhe Chen

Peng Huang

Lifeng Liu

Xiaohui Liu
title: Metal oxide resistive random access memory based synaptic devices for brain inspired computing
year: 2016

abstract: Display Omitted HighlightsThe leakage current of ReRAM was reduced by the non linear behavior.The influence of the band gap energy of oxides on the non linearity of the device.The non linearity was determined by barrier height between oxide and electrode.Optimized non linearity of ReRAM is applicable for memory cross point array. In this paper, the influence of the band gap energy of metal oxide layers on the non linearity of the device had been investigated. The band gap energy of the metal oxide layer determines barrier height of tunneling between metal oxides and electrodes for tunneling mechanisms. The optimum barrier height between the metal oxides and electrodes exhibits high non linear characteristics of the device for low leakage current of the cross point array applications with excellent switching uniformity.
author_names: Sangheon Lee

Daeseok Lee

Jiyong Woo

Euijun Cha

Jaesung Park

Kibong Moon

Jeonghwan Song

Hyunsang Hwang
title: The band gap energy dependence of metal oxides on non linear characteristics in the HfO2 based resistive random access memory
year: 2015

abstract: Graph algorithms such as graph traversal have been gaining ever increasing importance in the era of big data. However, graph processing on traditional architectures issues many random and irregular memory accesses, leading to a huge number of data movements and the consumption of very large amounts of energy. To minimize the waste of memory bandwidth, we investigate utilizing processing in memory (PIM) combined with non volatile metal oxide resistive random access memory (ReRAM) to improve both computation and I/O performance. We propose a new ReRAM based processing in memory architecture called RPBFS, in which graph data can be persistently stored and processed in place. We study the problem of graph traversal, and we design an efficient graph traversal algorithm in RPBFS. Benefiting from low data movement overhead and high bank level parallel computation, RPBFS shows a significant performance improvement compared with both the CPU based and the GPU based BFS implementations. On a suite of real world graphs, our architecture yields a speedup in graph traversal performance of up to 33.8x, and achieves a reduction in energy over conventional systems of up to 142.8x.
author_names: Lei Han

Zhaoyan Shen

Duo Liu

Zili Shao

H Howie Huang

Tao Li
title: A Novel ReRAM Based Processing in Memory Architecture for Graph Traversal
venue: ACM Trans. Storage
year: 2018

abstract: The stochastic neuron is a key for event based probabilistic neural networks. We propose a stochastic neuron using a metal oxide resistive random access memory (ReRAM) The ReRAM's conducting filament with built in stochasticity is used to mimic the neuron's membrane capacitor, which temporally integrates input spikes. A capacitor less neuron circuit is designed, laid out, and simulated. The output spiking train of the neuron obeys the Poisson distribution. Using the 65 nm CMOS technology node, the area of the neuron is <inline formula> <tex math notation="LaTeX"\text{14} \times \text{5} \mu\text{m}^2$/tex math>/inline formula> which is one ninth the size of a 1 pF capacitor. The average power consumption of the neuron is 1.289 <inline formula> <tex math notation="LaTeX"\mu$/tex math>/inline formula>W. We introduce the neural array A modified one transistor one ReRAM (1T1R) crossbar that integrates the ReRAM neurons with ReRAM synapses to form a compact and energy efficient in memory spiking neural network. A spiking deep belief network (DBN) with a noisy rectified linear unit (NReLU) is trained and mapped to the spiking DBN using the proposed ReRAM neurons. Simulation results show that the ReRAM neuron based DBN is able to recognize the handwritten digits with 94.7% accuracy and is robust against the ReRAM process variation effect.
author_names: Jie Lin

Jiann-Shiun Yuan
title: Analysis and Simulation of Capacitor Less ReRAM Based Stochastic Neurons for the in Memory Spiking Neural Network
venue: IEEE Transactions on Biomedical Circuits and Systems
year: 2018