Finite Element Electro-Thermal Modeling of Interfacial Phase Change Memory

When and Where

Nov 29, 2022
9:00am - 9:15am

Sheraton, 2nd Floor, Independence West

Presenter

Md Tashfiq Bin Kashem

Jake Scoggin

Ali Gokirmak

Helena Silva

Co-Author(s)

Md Tashfiq Bin Kashem¹,Jake Scoggin¹,Ali Gokirmak¹,Helena Silva¹

University of Connecticut¹

Abstract

Md Tashfiq Bin Kashem¹,Jake Scoggin¹,Ali Gokirmak¹,Helena Silva¹

University of Connecticut¹

Phase change memory (PCM) is a high-speed non-volatile memory that utilizes the reversible and rapid transition between conductive crystalline phase and resistive amorphous phase of the phase change material to store information. One major bottleneck for PCM is the large power requirement to heat the active region above crystallization or melting temperature. To counteract this issue, a device engineering technique is to use thin film periodic structure of layers of two phase change materials known as superlattice or interfacial phase change memory (iPCM) [1,2]. The mechanisms behind the improved performance of iPCM are still under investigation, one experimental work indicates similar crystallization and melt-quench based amorphization of these devices as conventional PCM [2]. iPCM structures benefit from accelerated amorphization through increased number of material interfaces and reduced thermal conduction due to thermal boundary resistances (TBR). Moreover, because of difference in melting temperature, electrical conductivity and Seebeck coefficient of different materials constituting the superlattice, such layered structures may have the advantage of melting of only one of the alternating layers assisted by local heating or cooling due to Peltier effect at the interfaces. If the alternating layers are lattice matched, recrystallization (set) time is also expected to be much faster as crystallization can be achieved though templated growth at the interfaces. In this work, we utilize our finite element phase change model [3-6] in COMSOL Multiphysics platform to perform electro-thermal simulations of reset and set operations on iPCM structures consisting of alternately stacked Ge2Sb2Te5 (GST) and GeTe layers [7]. Electric current and heat transfer physics are employed together to account for Joule heating and thermoelectric effects (Thomson heat within a single material and Peltier heat at material interfaces) with temperature dependent Seebeck coefficients, thermal conductivities, electrical resistivities, heat capacities and TBR for each material / material pairs. Latent heat of crystallization and fusion are included in the amorphous-crystalline and solid-liquid transitions respectively [5], causing heat release at the crystal-amorphous boundaries during crystal growth and heat absorption at the grain boundaries during amorphization. High energy sites: grain boundaries and material interfaces are easier to melt, described as heterogeneous melting [6]. Updated current density function [8] and electrical conductivity model of GST [9] are incorporated in the simulation framework. Our results on iPCM and conventional PCM structures of same dimensions and geometry (20 nm wide, 70 nm long pillar cells) show ~ 50% reduction in reset time and energy and more consistent set for iPCM. We explored the effect of the total number of layers and the thickness of each material in a period on the time and power requirements for memory operations. Increased number of layers improve speed and power efficiency. However, thinner layers may be less stable and more prone to mixing. Acknowledgment: This work is partially supported by the National Science Foundation under award DMR-1710468. References: 1. J. Tominaga et al., phys. status solidi (RRL), 13.4 (2019), DOI: 10.1002/pssr.201800539 2. K. L. Okabe et al., J Appl. Phys., 125 (2019), DOI: 10.1063/1.5093907 3. Z. Woods et al., IEEE T. Electron Devices, 64 (2017), DOI: 10.1109/TED.2017.2745506 4. Z. Woods et al., IEEE T. Electron Devices, 64 (2017), DOI: 10.1109/TED.2017.2745500 5. J. Scoggin et al., Appl. Phys. Lett., 112 (2018), DOI: 10.1063/1.5025331 6. J. Scoggin et al., Appl. Phys. Lett., 114 (2019), DOI: 10.1063/1.5067397 7. M. T. B. Kashem et al., 241st ECS meet., (2022) 8. M. T. B. Kashem et al., ECS Transactions, 108 (2022), DOI: 10.1149/10801.0003ecst 9. R. S. Khan et al. arXiv preprint arXiv:2002.12487 (2020), DOI: 10.48550/arXiv.2002.12487