Bipolar Resistive RAM Based on &lt;formula formulatype="inline"&gt; &lt;tex Notation="TeX"&gt;${\rm HfO}_{2}$&lt;/tex&gt; &lt;/formula&gt;: Physics, Compact Modeling, and Variability Control

Puglisi, Francesco Maria; Larcher, Luca; Padovani, Andrea; Pavan, Paolo

doi:10.1109/jetcas.2016.2547703

Cited by 35 publications

(4 citation statements)

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“…[ 13 ] Atomic defects enabling the observation of RTN can be formed due to the intrinsic variability of the manufacturing processes at the atomic scale (e.g., heavy‐ion implantation, surface contamination) [ 14 ] or after an electrical stress is applied (e.g., electrical‐field‐driven ionic movement). [ 15,16 ]…”

Section: Introductionmentioning

confidence: 99%

Random Telegraph Noise in Metal‐Oxide Memristors for True Random Number Generators: A Materials Study

Zanotti

Wang

et al. 2021

Adv Funct Materials

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Some memristors with metal/insulator/metal (MIM) structure have exhibited random telegraph noise (RTN) current signals, which makes them ideal to build true random number generators (TRNG) for advanced data encryption. However, there is still no clear guide on how essential manufacturing parameters like materials selection, thicknesses, deposition methods, and device lateral size can influence the quality of the RTN signal. In this paper, an exhaustive statistical analysis on the quality of the RTN signals produced by different MIM‐like memristors is reported, and straightforward guidelines for the fabrication of memristors with enhanced RTN performance are presented, which are: i) Ni and Ti electrodes show better RTN than Au electrodes, ii) the 50 μm × 50 μm devices show better RTN than the 5 μm × 5 μm ones, iii) TiO2 shows better RTN than HfO2 and Al2O3, iv) sputtered‐oxides show better RTN than ALD‐oxides, and v) 10 nm thick oxides show better RTN than 5 nm thick oxides. The RTN signals recorded have been used as entropy sources in high‐throughput TRNG circuits, which have passed the randomness tests of the National Institute of Standards and Technology. The work can serve as a useful guide for materials scientists and electronic engineers when fabricating MIM‐like memristors for RTN applications.

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Section: Introductionmentioning

confidence: 99%

Random Telegraph Noise in Metal‐Oxide Memristors for True Random Number Generators: A Materials Study

Zanotti

Wang

et al. 2021

Adv Funct Materials

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“…In literature OxRAM devices with controlled variability have been proposed by controlling the forming process, material properties and operating conditions [45][46][47]. Use of optimized device stacks may help to mitigate the variability effects to some extent.…”

Section: Discussionmentioning

confidence: 99%

Programming scheme based optimization of hybrid 4T-2R OxRAM NVSRAM

Majumdar

Kingra

2017

Semicond. Sci. Technol.

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In this paper, we present a novel single-cycle programming scheme for 4T-2R NVSRAM, exploiting pulse engineered input signals. OxRAM devices based on 3 nm thick bi-layer active switching oxide and 90 nm CMOS technology node were used for all simulations. The cell design is implemented for real-time non-volatility rather than last-bit, or power-down nonvolatility. Detailed analysis of the proposed single-cycle, parallel RRAM device programming scheme is presented in comparison to the two-cycle sequential RRAM programming used for similar 4T-2R NVSRAM bit-cells. The proposed single-cycle programming scheme coupled with the 4T-2R architecture leads to several benefits such as-possibility of unconventional transistor sizing, 50% lower latency, 20% improvement in SNM and ∼20× reduced energy requirements, when compared against two-cycle programming approach.

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“…In RRAMs, the analog resistance switching is controlled by a reversible, voltage-driven, and ion-based mechanism that allows the geometry of a conductive path within a dielectric layer to be modulated. Different switching mechanisms have been proposed for the implementation of RRAM [26,42,43], with each one requiring the comprehensive knowledge of different physical processes for being thoroughly understood.…”

Section: Methodsmentioning

confidence: 99%

Multiscale Modeling for Application-Oriented Optimization of Resistive Random-Access Memory

Torraca

Puglisi

Padovani³

et al. 2019

Materials

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Memristor-based neuromorphic systems have been proposed as a promising alternative to von Neumann computing architectures, which are currently challenged by the ever-increasing computational power required by modern artificial intelligence (AI) algorithms. The design and optimization of memristive devices for specific AI applications is thus of paramount importance, but still extremely complex, as many different physical mechanisms and their interactions have to be accounted for, which are, in many cases, not fully understood. The high complexity of the physical mechanisms involved and their partial comprehension are currently hampering the development of memristive devices and preventing their optimization. In this work, we tackle the application-oriented optimization of Resistive Random-Access Memory (RRAM) devices using a multiscale modeling platform. The considered platform includes all the involved physical mechanisms (i.e., charge transport and trapping, and ion generation, diffusion, and recombination) and accounts for the 3D electric and temperature field in the device. Thanks to its multiscale nature, the modeling platform allows RRAM devices to be simulated and the microscopic physical mechanisms involved to be investigated, the device performance to be connected to the material’s microscopic properties and geometries, the device electrical characteristics to be predicted, the effect of the forming conditions (i.e., temperature, compliance current, and voltage stress) on the device’s performance and variability to be evaluated, the analog resistance switching to be optimized, and the device’s reliability and failure causes to be investigated. The discussion of the presented simulation results provides useful insights for supporting the application-oriented optimization of RRAM technology according to specific AI applications, for the implementation of either non-volatile memories, deep neural networks, or spiking neural networks.

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Bipolar Resistive RAM Based on <formula formulatype="inline"> <tex Notation="TeX">${\rm HfO}_{2}$</tex> </formula>: Physics, Compact Modeling, and Variability Control

Cited by 35 publications

References 60 publications

Random Telegraph Noise in Metal‐Oxide Memristors for True Random Number Generators: A Materials Study

Random Telegraph Noise in Metal‐Oxide Memristors for True Random Number Generators: A Materials Study

Programming scheme based optimization of hybrid 4T-2R OxRAM NVSRAM

Multiscale Modeling for Application-Oriented Optimization of Resistive Random-Access Memory

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