Improving Ge-rich GST ePCM reliability through BEOL engineering

Redaelli, A.; Gandolfo, A.; Samanni, G.; Gomiero, E.; Petroni, Elisa; Scotti, L.; Lippiello, A.; Mattavelli, Paolo; Jasse, J.; Codegoni, Davide; Serafini, Andrea; Ranica, R.; Boccaccio, C.; Sandrini, Jury; Berthelon, R.; Grenier, JC.; Weber, O.; Turgis, David; Valéry, Alexia; Medico, S. Del; Caubet, V.; Reynard, J. P.; Dutartre, D.; Favennec, L.; Conte, A.; Disegni, F.; Tomasi, M. De; Ventre, A.; Baldo, M.; Ielmini, Daniele; Maurelli, A.; Ferreira, Paulo; Arnaud, F.; Piazza, F.; Cappelletti, P.; Annunziata, R.; Gonella, R.

doi:10.1109/essderc53440.2021.9631807

Cited by 7 publications

(2 citation statements)

References 6 publications

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“…4 to Fig. 7 show the active material evolution during different process steps of the integration flow through TEM and EELS analyses, as previously reported by Redaelli et al in [4]. Ge segregation in the Ge-rich GST is highlighted by ASTAR technique [5] (Fig.…”

Section: Introductionsupporting

confidence: 66%

BEOL Process Effects on ePCM Reliability

Redaelli

Gandolfo

Samanni

et al. 2022

IEEE J. Electron Devices Soc.

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The effect of back-end of line (BEOL) process on cell performance and reliability of Phase-Change Memory embedded in a 28nm FD-SOI platform (ePCM) is discussed. The microscopic evolution of the Ge-rich GST alloy during process is the focus of the first part of the paper. A new metric for quantification of active material modifications is introduced to better follow its evolution with process sequence. Ge clustering has been shown to occur during the fabrication, impacting the pristine resistance and the after forming cell performance. Two different BEOL processes are then benchmarked in terms of key performance. An optimized process is identified, and an extensive electrical characterization of array performance and reliability is done on the full 16MB chip. The optimized BEOL process results in a memory cell fully compatible with the requirements for demanding automotive applications.

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Section: Introductionsupporting

confidence: 66%

BEOL Process Effects on ePCM Reliability

Redaelli

Gandolfo

Samanni

et al. 2022

IEEE J. Electron Devices Soc.

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“…In the past decade, researchers have made great efforts to "slow down" the crystallization, and doping is one of the most effective approaches. For example, the introduction of Ga effectively improves the thermal stability of the PCM glass [33,34], and the optimization of GST stoichiometry, such as Gerich GST material, could also enhance the amorphous phase [35][36][37][38].…”

Section: Fabrication and Characterization Of Ggs Filmsmentioning

confidence: 99%

Design of all-phase-change-memory spiking neural network enabled by Ge-Ga-Sb compound

et al. 2023

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The implementation of artificial spiking neural network (SNN) usually takes advantage of multiple heterogeneous circuits to mimic either neurons which generate spiking pulses, or synapses which store the weights of event correlations. Here, we design a homogeneous device using Ge-Ga-Sb (GGS) as a phase-change-memory (PCM) material which can do both jobs. The GGS compound shows high stability when used in data storage, such as high working temperature (281°C) and high 10-years data retention temperature (230°C), as well as low resistance drift. Interestingly, when the as-fabricated GGS device is set by iterative narrow-width electric pulses, it first experiences an abrupt resistance drop by two orders of magnitude, followed by a continuous resistance decrease. This unique abrupt-to-progressive transition can be used to mimic both neuronal and synaptic functions, mechanistically enabled by the formation of conductive channels and the continuous growth with the phase separation of crystalline areas. To this end, we propose an all-PCM SNN, which is emulated to have high accuracy (90%) in the standard pattern recognition.

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Compact Modeling of Phase Change Memory with Parameter Extractions

Ding

Chen

et al. 2022

ESSDERC 2022 - IEEE 52nd European Solid-State Device Research Conference (ESSDERC)

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Improving Ge-rich GST ePCM reliability through BEOL engineering

Cited by 7 publications

References 6 publications

BEOL Process Effects on ePCM Reliability

BEOL Process Effects on ePCM Reliability

Design of all-phase-change-memory spiking neural network enabled by Ge-Ga-Sb compound

Compact Modeling of Phase Change Memory with Parameter Extractions

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