Modeling the effects of different forming conditions on RRAM conductive filament stability

Butcher, B.; Bersuker, G.; Vandelli, Luca; Padovani, Andrea; Larcher, Luca; Kalantarian, A.; Geer, Robert; Gilmer, D. C.

doi:10.1109/imw.2013.6582096

Cited by 32 publications

(24 citation statements)

References 4 publications

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“…The beneficial effects of a high-temperature forming process have been thoroughly reported in the literature, and have been associated with lower forming voltages and variability of the low resistance state, while improving the stability and reliability of the device [76,77,78]. The external temperature affects the forming process assisting the defect generation and promoting the oxygen ion diffusion in the device, leading to a lower density of oxygen ions near the conductive path after the forming process.…”

Section: Resultsmentioning

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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Section: Resultsmentioning

confidence: 99%

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

Torraca

Puglisi

Padovani³

et al. 2019

Materials

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“…5.17, based on the oxygen migration model [138,201,214]. The proposed explanation is guided by two recent findings: (1) observation of an oxygendepleted filament via atomic-scale electron-energy loss spectroscopy [143]; and (2) observation, by simulation, of oxygen ions released from the filament site during formation, in the vicinity of the filament [215]. Fig.…”

Section: Mechanism Of the Light-induced Disruption Of The Cfmentioning

confidence: 91%

“…It has been proposed [210] that optical reset proceeded with the photo-excitation of interstitial O ions surrounding the breakdown path, resulting in their migration and recombination with the vacancy defects in the path. Besides the electric-field directed migration of O ions (released from Si-O bond dissociation during the breakdown process) towards the anode, simulation has shown that these O ions may also propagate laterally outwards from the breakdown site due to Joule heating, and some of them remained in the proximity when the process is aborted [211]. The migration barrier for interstitial O ions within the oxide network is ~0.11-0.27 Ev [212], which corresponds to ~1.8-4.3×10 −20 J.…”

Section: Methodsmentioning

confidence: 99%

“…Repeated voltage sweeps applied to the breakdown path may cause progression towards more severe hard breakdown, due to further dissociation of weak Si-O bonds within the breakdown region and propagation of the released oxygen ions further away from the breakdown site by Joule heating [211]. Consequently, the path may become less responsive to illumination because of reduced availability of interstitial oxygen in its proximity.…”

Section: 15(a)mentioning

confidence: 99%

“…Fig. 5.17 process is interrupted when the current reaches a compliance limit, some of the released oxygen ions remain in the interstitial positions in the vicinity of the filament [215] and may be subsequently driven back to the filament by the applied electrical field and temperature gradient during the electrically controlled reset [201]. The diffusion of interstitial oxygen ions entails the overcoming of an energy barrier of 0.3-0.6 eV [216].…”

Section: Mechanism Of the Light-induced Disruption Of The Cfmentioning

confidence: 99%

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Characterization of nanoscale conducting filament in high-k oxides by scanning tunneling microscopy and conductive atomic force microscopy

Zhou¹

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By virtue of its simple structure, high operating speed and scalability, the resistive memory or RRAM is deemed a promising alternative to the charge-based memory, which is now facing severe scaling challenges. Of particular interest is the HfO 2 RRAM due to its immediate compatibility with mainstream integrated-circuit technology. A major problem is, however, the relatively high switching current.Currents on the order of 10 -3 A are typically observed in large-area cells (~10 -8 cm 2 ).In a recent work, a substantial reduction of the switching current to ~10 -5 A was achieved by scaling the cell area down to 100 nm 2 .Since one of the main strengths of RRAM is scalability, a further reduction of the switching current is deemed necessary in order to make ultra-high density memory application viable. At present, it is unclear to what extent could the switching current be reduced with cell area scaling. To address this question, resistance switching in HfO 2 is examined using a conductive atomic force microscope (C-AFM) and scanning tunneling microscope (STM) in this thesis. The excellent spatial resolution of C-AFM and STM enables the electrical properties of a thin dielectric to be probed over an extremely localized region. Through the C-AFM/STM technique, in this thesis, we examined resistance switching in a 4-nm thick HfO 2 , within a region of ~2 nm in diameter and achieved an ultra-low current switching capability. Furthermore, the unique abrupt reset behavior is observed in nanoscale RRAM with nanometer-level conducting filament (CF), on the contrary, the gradual current reduction trend is typically observed during the reset process in relatively large area

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Modeling the VCM‐ and ECM‐Type Switching Kinetics

Menzel¹,

Hur²

2016

Resistive Switching

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Modeling the effects of different forming conditions on RRAM conductive filament stability

Cited by 32 publications

References 4 publications

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

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

Characterization of nanoscale conducting filament in high-k oxides by scanning tunneling microscopy and conductive atomic force microscopy

Modeling the VCM‐ and ECM‐Type Switching Kinetics

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