Jake Scoggin1 Zachary Woods1 Helena Silva1 Ali Gokirmak1

1, University of Connecticut, Storrs, Connecticut, United States

Finite element models are useful for understanding and designing phase change materials and devices. Models capturing crystal nucleation, growth, and amorphization; phase, temperature, and electric field dependent material parameters; and interfacial effects have been reported with each model achieving some level of accuracy and efficiency [1]–[6]. 2D simulations with a fixed out-of-plane depth can capture stochastic nucleation and dynamic grain boundary effects in thin structures but may not appropriately model current densities or thermal profiles. 2D rotational simulations correctly model electrothermal profiles in inherently symmetric scenarios but cannot model discrete nucleation. 3D simulations can model crystallization dynamics or electrothermal profiles most accurately, but computational complexity limits fully coupled simulations of both. We explore the role of dimensionality on accuracy and computational cost with our finite element model which captures phase change, stochastic nucleation, temperature and electric field dependent material parameters, and interfacial effects. We analyze 2D, 2D rotational, and 3D simulations of crystallization, electrical reset and set, and ovonic switching. For 2D modeling, we introduce a variable out-of-plane depth which improves eletrothermal accuracy. We find 2D rotational simulations appropriate for ovonic switching in some scenarios but show that 3D simulations are required to properly model filament dynamics even in some apparently symmetric cases. Specifically, we observe a filament form in the center of an ovonic threshold switch, migrate to the outside of the device, and continuously revolve around the device edge, suggesting instability in a symmetric device.

[1] Z. Woods and A. Gokirmak, “Modeling of Phase-Change Memory: Nucleation, Growth, and Amorphization Dynamics During Set and Reset: Part I—Effective Media Approximation,” IEEE Trans. Electron Devices, vol. 64, no. 11, pp. 4466–4471, Nov. 2017.
[2] Z. Woods et al., “Modeling of Phase-Change Memory: Nucleation, Growth, and Amorphization Dynamics During Set and Reset: Part II--Discrete Grains,” IEEE Trans. Electron Devices, pp. 1–7, 2017.
[3] A. Faraclas et al., “Modeling of Thermoelectric Effects in Phase Change Memory Cells,” IEEE Trans. Electron Devices, vol. 61, no. 2, 2014.
[4] J. P. Reifenberg et al., “The Impact of Thermal Boundary Resistance in Phase-Change Memory Devices,” IEEE Electron Device Lett., vol. 29, no. 10, pp. 1112–1114, Oct. 2008.
[5] H. L. Lung et al., “A novel low power phase change memory using inter-granular switching,” in 2016 IEEE Symposium on VLSI Technology, 2016, pp. 1–2.
[6] Y. Yin et al., “Finite Element Analysis of Dependence of Programming Characteristics of Phase-Change Memory on Material Properties of Chalcogenides,” 2006.