A Robust and Efficient Compact Model for Phase-Change Memory Circuit Simulations

Chen, Xuhui; Ding, Feilong; Huang, Xiaoqing; Lin, Xinnan; Wang, Runsheng; Chan, Mansun; Zhang, Lining; Huang, Ru

doi:10.1109/ted.2021.3098656

Cited by 6 publications

(1 citation statement)

References 32 publications

(48 reference statements)

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“…From a theoretical perspective, nanoscale modeling and simulations play important roles in PCM technology advancements. Empirical formulas and Johnson-Mehl-Avrami (JMA) models [8] are widely used in compact models [9][10][11]. At the numerical simulation level, a classical nucleation and growth (NG) model proposed by Peng et al [12] is commonly used to reflect the random nucleation and non-uniform distribution of phase states of materials.…”

Section: Introductionmentioning

confidence: 99%

Robust Simulations of Nanoscale Phase Change Memory: Dynamics and Retention

et al. 2021

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A robust simulation framework was developed for nanoscale phase change memory (PCM) cells. Starting from the reaction rate theory, the dynamic nucleation was simulated to capture the evolution of the cluster population. To accommodate the non-uniform critical sizes of nuclei due to the non-isothermal conditions during PCM cell programming, an improved crystallization model was proposed that goes beyond the classical nucleation and growth model. With the above, the incubation period in which the cluster distributions reached their equilibrium was captured beyond the capability of simulations with a steady-state nucleation rate. The implications of the developed simulation method are discussed regarding PCM fast SET programming and retention. This work provides the possibility for further improvement of PCM and integration with CMOS technology.

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Section: Introductionmentioning

confidence: 99%

Robust Simulations of Nanoscale Phase Change Memory: Dynamics and Retention

et al. 2021

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A review of compact modeling for phase change memory

Ding¹,

Peng²,

Li³

et al. 2022

J. Semicond.

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Phase change memory (PCM) attracts wide attention for the memory-centric computing and neuromorphic computing. For circuit and system designs, PCM compact models are mandatory and their status are reviewed in this work. Macro models and physics-based models have been proposed in different stages of the PCM technology developments. Compact modeling of PCM is indeed more complex than the transistor modeling due to their multi-physics nature including electrical, thermal and phase transition dynamics as well as their interactions. Realizations of the PCM operations including threshold switching, set and reset programming in these models are diverse, which also differs from the perspective of circuit simulations. For the purpose of efficient and reliable designs of the PCM technology, open issues and challenges of the compact modeling are also discussed.

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