<i>(Invited) </i>Resistive Memories (RRAM) Variability: Challenges and Solutions

Molas, G.; Sassine, Gilbert; Nail, Cécile; Robayo, D. Alfaro; Nodin, J. F.; Cagli, C.; Coignus, J.; Blaise, P.; Nowak, E.

doi:10.1149/08603.0035ecst

Cited by 29 publications

(25 citation statements)

References 22 publications

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“…[ 1–4 ] However, reliability and variability of switching are one of the major obstacles to currently existing RRAM technology. [ 5–8 ] In general, it is considered that filament formation is the way to switch the resistance of RRAM devices. In electrochemical metallization memory type devices a metallic bridge is the reason of switching.…”

Section: Introductionmentioning

confidence: 99%

In Quest of Nonfilamentary Switching: A Synergistic Approach of Dual Nanostructure Engineering to Improve the Variability and Reliability of Resistive Random‐Access‐Memory Devices

Maikap

Banerjee

2020

Adv Elect Materials

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This study demonstrates a synergistic approach of dual nanostructure engineering to improve the performance of AlOx‐based resistive random access memory (RRAM) devices. More precisely, a novel material stack engineering of RRAM with nonlinear area scalable low current switching and extensive improvement of reliability and variability issues is reported. Dual nanostructures (nanodome bottom electrode and nanocrystals in switching layer) in RRAM is an effective way to change the switching from filamentary to a nonfilamentary one, resulting in forming‐free highly stable device. Several methods such as transmission electron microscopy, energy dispersive spectroscopy, and atomic force microscopy have been employed to investigate the morphology of the RRAM devices. Enhanced electric field within the enclave of switching layer is controlling the intrinsic distribution of oxygen vacancy profile with extrinsic operation. As compare to filamentary RRAM, nonfilamentary devices can effectively control dc switching for >103 cycles, ac switching for >106 cycles without any severe fluctuation of resistance states for different levels. Area and temperature‐dependent semiconducting behavior along with time‐dependent high resistance states are certainly confirming the nonfilamentary switching process. In addition, the device yield improvement ≈98% using nonfilamentary switching, is making dual nanostructure devices a very promising candidate for high‐density memory application, is highlighted.

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Section: Introductionmentioning

confidence: 99%

In Quest of Nonfilamentary Switching: A Synergistic Approach of Dual Nanostructure Engineering to Improve the Variability and Reliability of Resistive Random‐Access‐Memory Devices

Maikap

Banerjee

2020

Adv Elect Materials

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“…First, the TiN BE is deposited. Then, a 10 nm-HfO 2 /10 nm-Ti/TiN stack is added to form a capacitor-like structure [15].…”

Section: T1r Structurementioning

confidence: 99%

Non-volatile SRAM memory cells based on ReRAM technology

et al. 2020

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Static Random-Access Memories (SRAMs) are very common in today's chip industry due to their speed and power consumption but are classified as volatile memories. Non-volatile SRAMs (nvSRAMs) combine SRAM features with nonvolatility. This combination has the advantage to retain data after power off or in the case of power failure, enabling energy-efficient and reliable systems under frequent power-off conditions. In this work, several nvSRAMs architectures based on Oxide Random-Access Memory (OxRAM) technology are presented and compared. OxRAMs are non-volatile memories considered as a subset of Resistive RAM (ReRAM) technology.

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“…In the meantime, it should be noted that there may exist trade‐offs in the optimization of device performance. For instance, it might be possible to enlarge the dynamic range and improve retention by applying strong operating conditions, which however may deteriorate the endurance and result in higher power consumption . As a result, a perfect synaptic device that combines all the favorable properties is yet to be demonstrated so far, which apparently demands continued device optimizations, and the device optimization should also be performed according to the requirements in detailed applications.…”

Section: Memristive Synapsesmentioning

confidence: 99%

Memristive Devices and Networks for Brain‐Inspired Computing

Zhang

et al. 2019

Physica Rapid Research Ltrs

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As the era of big data approaches, conventional digital computers face increasing difficulties in performance and power efficiency due to their von Neumann architecture. As a result, there is recently a tremendous upsurge of investigations on brain-inspired neuromorphic hardware with high parallelism and improved efficiency. Memristors are considered as promising building blocks for the realization of artificial synapses and neurons and can therefore be utilized to construct hardware neural networks. Here, a review is provided on existing approaches for the implementation of artificial synapses and neurons based on memristive devices; and the respective advantages and disadvantages of these approaches are evaluated. This is followed by a discussion of hardware accelerators and neuromorphic computing systems that exploit the parallel, in-memory and analog characteristics of memristive crossbar arrays as well as the intrinsic dynamics of memristors. Finally, the outstanding challenges are addressed that have not yet been resolved in the present studies, and future advances are discussed that might be needed for building intelligent and energy efficient neuromorphic systems.

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(Invited) Resistive Memories (RRAM) Variability: Challenges and Solutions

Cited by 29 publications

References 22 publications

In Quest of Nonfilamentary Switching: A Synergistic Approach of Dual Nanostructure Engineering to Improve the Variability and Reliability of Resistive Random‐Access‐Memory Devices

In Quest of Nonfilamentary Switching: A Synergistic Approach of Dual Nanostructure Engineering to Improve the Variability and Reliability of Resistive Random‐Access‐Memory Devices

Non-volatile SRAM memory cells based on ReRAM technology

Memristive Devices and Networks for Brain‐Inspired Computing

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