Emerging resistive switching memory technologies: Overview and current status

Marinella, Matthew

doi:10.1109/iscas.2014.6865264

Cited by 10 publications

(2 citation statements)

References 35 publications

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“…2(c) depicts a schematic of interface transitions within non-filamentary devices, which are also based on the mechanism of ion migration. When subjected to an external electric field, variations in its conductivity occur as a result of ion migration, altering the configuration of defect structures at the interface between the metal electrode and the conversion layer, subsequently modifying the Schottky barrier at the interface [49,51,52] . At presented in Fig.…”

Section: Memristors Based On Different Operation Principlesmentioning

confidence: 99%

Neuromorphic circuits based on memristors: endowing robots with a human-like brain

Wang,

Yang,

Liu

et al. 2024

J. Semicond.

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Robots are widely used, providing significant convenience in daily life and production. With the rapid development of artificial intelligence and neuromorphic computing in recent years, the realization of more intelligent robots through a profound intersection of neuroscience and robotics has received much attention. Neuromorphic circuits based on memristors used to construct hardware neural networks have proved to be a promising solution of shattering traditional control limitations in the field of robot control, showcasing characteristics that enhance robot intelligence, speed, and energy efficiency. Starting with introducing the working mechanism of memristors and peripheral circuit design, this review gives a comprehensive analysis on the biomimetic information processing and biomimetic driving operations achieved through the utilization of neuromorphic circuits in brain-like control. Four hardware neural network approaches, including digital-analog hybrid circuit design, novel device structure design, multi-regulation mechanism, and crossbar array, are summarized, which can well simulate the motor decision-making mechanism, multi-information integration and parallel control of brain at the hardware level. It will be definitely conductive to promote the application of memristor-based neuromorphic circuits in areas such as intelligent robotics, artificial intelligence, and neural computing. Finally, a conclusion and future prospects are discussed.

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Section: Memristors Based On Different Operation Principlesmentioning

confidence: 99%

Neuromorphic circuits based on memristors: endowing robots with a human-like brain

Wang,

Yang,

Liu

et al. 2024

J. Semicond.

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“…Emerging memories like charge trap flash, phase change, ionic migration switching oxides, magnetic switching, or ferroelectric switching memories are being extensively explored for machine-learning accelerators or dense conventional memory applications. − Of these, resistive random-access memory (RRAM) devices have proven to be the most versatile with applications ranging from nonvolatile storage and logic gates , to analog synapses − and integrate-fire and stochastic neurons − for neuromorphic networks. Moreover, they typically have a simple metal-oxide-metal structure and are hence capable of dense packing in cross-bar arrays .…”

Section: Introductionmentioning

confidence: 99%

Highly Deterministic One-Shot Set–Reset Programming Scheme in PCMO Resistive Random-Access Memory

Phadke

Saraswat

Ganguly

2022

ACS Appl. Electron. Mater.

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Resistive random-access memory (RRAM) devices are very versatile with applications ranging from digital nonvolatile memories to analog synapses and integrate-fire neurons. Recently, RRAM based stochastic neurons have also been proposed for optimization networks that solve NP-hard problems. These applications rely on reliably writing a given conductance state repeatedly. A Pr1–x Ca x MnO3 (PCMO) based RRAM device is a nonfilamentary, bulk switching RRAM which demonstrates low device-to-device variations compared to filamentary RRAMs. However, a low complexity programming scheme to demonstrate low cycle-to-cycle variations (C2CV) as well is needed. In this work, we propose a one-shot Set (initialize) and Reset (program) scheme to experimentally demonstrate low C2CV (<15%) for multiple devices. Compared to one-shot programming on a filamentary device, there is a 2–4× increment in the number of levels that can be stored per device using PCMO RRAM. Further, one-shot programming in PCMO RRAM leads to a 35× (10×) reduction in the number of pulses for 3 (2) bits per device, respectively. This greatly simplifies the memory controller design for these devices compared to any iterative write-verify schemes. This scheme gives a further insight into the self-heating limited Set operation controlled by a series resistor while reinforcing the analog and precise nature of the Reset operation. We studied the impact of the C2CV in programming RRAM-based stochastic neurons on the Max-Cut optimization problem using Boltzmann machines. PCMO RRAM combined with the one-shot programming scheme emerges as the clear choice for a well-controlled RRAM device with minimal peripheral complexity.

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Circuit Emulating Neural Response Based on Ga₂O₃ Photomemristor

Sparvoli,

Chubaci,

Jorge

et al. 2024

Physica Status Solidi (b)

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Gallium oxide (Ga2O3), a transparent semiconductor material with wide bandgap between 4.5 and 4.9 eV, has attracted increasing interest lately due to multifunctionality. In this work, Ga2O3 films are deposited by ion‐beam‐assisted deposition method on p‐type silicon at ≈800 °C. Films deposited at lower temperatures predominantly have β‐phase structure, while those deposited at higher temperatures have dominant ε‐phase structure. Memristors testing devices are fabricated by depositing aluminum contacts over the Ga2O3 surface and the back surface of the substrate. In the Rutherford backscattering structural analysis, it is indicated that there is aluminum diffusion from the top contact into the film. The current–voltage measurement of the isolated device provides key information for understanding the memristor operation, revealing its bipolar characteristic and responsiveness to visible‐light soaking. The resistive switching behavior involving filament formation due to oxygen vacancies is discussed; however, since there is a light response, the most plausible explanation for the resistive switching is the trapping/detrapping mechanism. In the proposed neuromorphic circuit measurement, it is aimed to use a circuit that emulates a neuronal membrane and study the behavior of memristors response to light stimulus. This approach proves to be an excellent tool for observing action potentials, which mimic human vision.

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Emerging resistive switching memory technologies: Overview and current status

Cited by 10 publications

References 35 publications

Neuromorphic circuits based on memristors: endowing robots with a human-like brain

Neuromorphic circuits based on memristors: endowing robots with a human-like brain

Highly Deterministic One-Shot Set–Reset Programming Scheme in PCMO Resistive Random-Access Memory

Circuit Emulating Neural Response Based on Ga₂O₃ Photomemristor

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Emerging resistive switching memory technologies: Overview and current status

Cited by 10 publications

References 35 publications

Neuromorphic circuits based on memristors: endowing robots with a human-like brain

Neuromorphic circuits based on memristors: endowing robots with a human-like brain

Highly Deterministic One-Shot Set–Reset Programming Scheme in PCMO Resistive Random-Access Memory

Circuit Emulating Neural Response Based on Ga2O3 Photomemristor

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Circuit Emulating Neural Response Based on Ga₂O₃ Photomemristor