A Compact Phase Change Memory Model With Dynamic State Variables

Hu, Huifang; Liu, Dayong; Chen, Xuhui; Dong, Deqi; Cui, Xiaole; Liu, Ming; Lin, Xinnan; Zhang, Lining; Chan, Mansun

doi:10.1109/ted.2019.2956193

Cited by 14 publications

(12 citation statements)

References 23 publications

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“…The structure-based PCM resistance model can be expressed as ( 6) [20]. In the equation, R C is the crystalline resistance outside the active area, and g f represents the dynamic conductance of the active area filament.…”

Section: Spice Modeling Methodsmentioning

confidence: 99%

“…In the equation, R C is the crystalline resistance outside the active area, and g f represents the dynamic conductance of the active area filament. R A represents the static resistance of the active area and it can be calculated by conformal mapping, as shown in ( 7) [20].…”

Section: Spice Modeling Methodsmentioning

confidence: 99%

“…z act is determined by the internal temperature of the active area during the RESET operation. Reference [20] gives an equation for calculating the radius of the active area. The radius depends on the maximum temperature inside the active area.…”

Section: B Modeling the Gradual Reset Processmentioning

confidence: 99%

“…As a result, a voltage-driven model is more important in practical situation to mimic the behavior of a PCM cell. Moreover, current PCM models mostly focus on the application of the memory [20]- [22], neglecting the change of its analog characteristics, such as the partial RESET characteristic. To better implement neuromorphic ciucuits, a voltage-driven PCM SPICE model that can reflect the analog characteristics is urgently needed.…”

Section: Introductionmentioning

confidence: 99%

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A SPICE Model of Phase Change Memory for Neuromorphic Circuits

Chen

Huang

et al. 2020

IEEE Access

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A phase change memory (PCM) model suitable for neuromorphic circuit simulations is developed. A crystallization ratio module is used to track the memory state in the SET process, and an active region radius module is developed to track the continuously varying amorphous region in the RESET process. To converge the simulations with bi-stable memory states, a predictive filament module is proposed using a previous state in iterations of nonlinear circuit matrix under a voltage-driven mode. Both DC and transient analysis are successfully converged in circuits with voltage sources. The spiking-time-dependent-plasticity (STDP) characteristics essential for synaptic PCM are successfully reproduced with SPICE simulations verifying the model's promising applications in neuromorphic circuit designs. Further on, the developed PCM model is applied to propose a neuron circuit topology with lateral inhibitions which is more bionic and capable of distinguishing fuzzy memories. Finally, unsupervised learning of handwritten digits on neuromorphic circuits is simulated to verify the integrity of models in a large-scale-integration circuits. For the first time in literature an emerging memory model is developed and applied successfully in neuromorphic circuit designs, and the model is applicable to flexible designs of neuron circuits for further performance improvements.INDEX TERMS Neuromorphic circuits, phase change memory, SPICE model, spike-time-dependent plasticity, spiking neural networks.

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Section: Spice Modeling Methodsmentioning

confidence: 99%

Section: Spice Modeling Methodsmentioning

confidence: 99%

Section: B Modeling the Gradual Reset Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A SPICE Model of Phase Change Memory for Neuromorphic Circuits

Chen

Huang

et al. 2020

IEEE Access

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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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Exploring Phase‐Change Memory: From Material Systems to Device Physics

Ren

Sun

Chen³

et al. 2021

Physica Rapid Research Ltrs

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To deal with the growing demand for data storage and processing, phase‐change memory (PCM) provides one of the most promising candidates for next‐generation nonvolatile data storage and neuromorphic computing applications. A lot of effort has been made toward optimizing the materials and device design; thus, excellent device performances have been achieved including high density, fast switching speed, great endurance, and retention. In addition, the widely tunable optical characteristics of PCMs are irresistibly attractive for optoelectronic or all‐optical applications with unprecedented bandwidth, low energy consumption, and multilevel data storage. Herein, the materials system and switching mechanisms on experimental and modeling methods for PCM designs and applications are discussed. Electric‐domain and optical‐domain PCM‐based artificial synapses/neurons and their applications in neuromorphic computing are also reviewed. Finally, the future prospects and challenges of PCM‐based applications on materials, devices, algorithms, and system levels are highlighted.

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A Compact Phase Change Memory Model With Dynamic State Variables

Cited by 14 publications

References 23 publications

A SPICE Model of Phase Change Memory for Neuromorphic Circuits

A SPICE Model of Phase Change Memory for Neuromorphic Circuits

Robust Simulations of Nanoscale Phase Change Memory: Dynamics and Retention

Exploring Phase‐Change Memory: From Material Systems to Device Physics

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