Experimental and theoretical understanding of Forming, SET and RESET operations in Conductive Bridge RAM (CBRAM) for memory stack optimization

J, Guy; Molas, G.; Blaise, P.; Carabasse, C.; Bernard, M.; Roule, A.; Carval, G. Le; Sousa, V.; Grampeix, H.; Delaye, V.; Toffoli, A.; Cluzel, J.; Brianceau, P.; Pollet, Olivier; Balan, V.; Barraud, S.; Cueto, O.; Ghibaudo, G.; Clermidy, Fabien; Salvo, B. De; Perniola, L.

doi:10.1109/iedm.2014.7046997

Cited by 25 publications

(16 citation statements)

References 4 publications

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“…When a galvanic contact is present the current is high enough to enable Joule heating and the dissolution becomes thermally-assisted. [24][25][26][27] The shape of the filament has been investigated using scanning electron microcscopy (SEM) 28 , transmission electron microscopy (TEM) [29][30][31] and conductive atomic force microscope (C-AFM) tomography. 32 A dendritic filament structure was observed in a Ag/H 2 O/Pt cell, 28 whereas the filament was found to be a small solid crystalline Ag wire in a Ag/SiO 2 /Pt cell.…”

Section: Introductionmentioning

confidence: 99%

Understanding filamentary growth in electrochemical metallization memory cells using kinetic Monte Carlo simulations

2015

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We report on a 2D kinetic Monte Carlo model that describes the resistive switching in electrochemical metallization cells. To simulate the switching process, we consider several different processes on the atomic scale: electron-transfer reactions at the boundaries, ion migration, adsorption/desorption from/to interfaces, surface diffusion and nucleation. These processes result in a growth/dissolution of a metallic filament within an insulating matrix. In addition, the model includes electron tunneling between the growing filament and the counter electrode, which allows for simulating multilevel switching. It is shown that the simulation model can reproduce the reported switching kinetics, switching variability and multilevel capabilities of ECM devices. As a major result, the influence of mechanical stress working on the host matrix due to the filamentary growth is investigated. It is demonstrated that the size and shape of the filament depend on the Young's modulus of the insulating matrix. For high values a wire-like structure evolves, whereas the shape is dendritic if the Young's modulus is negligible.

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Section: Introductionmentioning

confidence: 99%

Understanding filamentary growth in electrochemical metallization memory cells using kinetic Monte Carlo simulations

2015

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“…11 The existence of an OV on a grid site yields favorable energy conditions for the migration of Cu. 15,16 It was shown in Ref. 9 that "Hot Spots" (HS), created during the early forming stages within the RSL, serve as agglomeration centers attracting Cu ions to form an initial stem, out of which a CF evolves and grows from the TE toward the HS and eventually to the BE.…”

Section: Resultsmentioning

confidence: 99%

“…The existence of an OV on a grid site yields favorable energy conditions for the migration of Cu. 15,16 In ST2, where the TE is in direct contact with the RSL, the Cu ion oxidation and reduction probabilities are calculated according to…”

Section: Formulationmentioning

confidence: 99%

A numerical analysis and experimental demonstration of a low degradation conductive bridge resistive memory device

Berco

Chand

Fariborzi

2017

Journal of Applied Physics

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This study investigates a low degradation metal-ion conductive bridge RAM (CBRAM) structure. The structure is based on placing a diffusion blocking layer (DBL) between the device's top electrode (TE) and the resistive switching layer (RSL), unlike conventional CBRAMs, where the TE serves as a supply reservoir for metallic species diffusing into the RSL to form a conductive filament (CF) and is kept in direct contact with the RSL. The properties of a conventional CBRAM structure (Cu/HfO 2 /TiN), having a Cu TE, 10 nm HfO 2 RSL, and a TiN bottom electrode, are compared with a 2 nm TaN DBL incorporating structure (Cu/TaN/HfO 2 /TiN) for 10 3 programming and erase simulation cycles. The low and high resistive state values for each cycle are calculated and the analysis reveals that adding the DBL yields lower degradation. In addition, the 2D distribution plots of oxygen vacancies, O ions, and Cu species within the RSL indicate that oxidation occurring in the DBL-RSL interface results in the formation of a sub-stoichiometric tantalum oxynitride with higher blocking capabilities that suppresses further Cu insertion beyond an initial CF formation phase, as well as CF lateral widening during cycling. The higher endurance of the structure with DBL may thus be attributed to the relatively low amount of Cu migrating into the RSL during the initial CF formation. Furthermore, this isomorphic CF displays similar cycling behavior to neural ionic channels. The results of numerical analysis show a good match to experimental measurements of similar device structures as well.

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“…Finally, good scalability was shown: scaled memory devices include: 10 nm crossbar OxRAM [53] and 5 nm liner CBRAM [54]; further density improvements could be obtained with vertical RRAM architecture [43].…”

Section: Filamentary Memorymentioning

confidence: 96%

Advances in Emerging Memory Technologies: From Data Storage to Artificial Intelligence

Molas

Nowak

2021

Applied Sciences

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This paper presents an overview of emerging memory technologies. It begins with the presentation of stand-alone and embedded memory technology evolution, since the appearance of Flash memory in the 1980s. Then, the progress of emerging memory technologies (based on filamentary, phase change, magnetic, and ferroelectric mechanisms) is presented with a review of the major demonstrations in the literature. The potential of these technologies for storage applications addressing various markets and products is discussed. Finally, we discuss how the rise of artificial intelligence and bio-inspired circuits offers an opportunity for emerging memory technology and shifts the application from pure data storage to storage and computing tasks, and also enlarges the range of required specifications at the device level due to the exponential number of new systems and architectures.

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Experimental and theoretical understanding of Forming, SET and RESET operations in Conductive Bridge RAM (CBRAM) for memory stack optimization

Cited by 25 publications

References 4 publications

Understanding filamentary growth in electrochemical metallization memory cells using kinetic Monte Carlo simulations

Understanding filamentary growth in electrochemical metallization memory cells using kinetic Monte Carlo simulations

A numerical analysis and experimental demonstration of a low degradation conductive bridge resistive memory device

Advances in Emerging Memory Technologies: From Data Storage to Artificial Intelligence

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