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arxiv: 2606.10262 · v1 · pith:ECHMBZWTnew · submitted 2026-06-09 · ⚛️ physics.app-ph · cond-mat.mtrl-sci

Filamentary Transport and Thermoelectric Effects in Mushroom Phase Change Memory Cells

Md Samzid Bin Hafiz , Helena Silva , Ali Gokirmak This is my paper

Pith reviewed 2026-06-27 11:16 UTC · model grok-4.3

classification ⚛️ physics.app-ph cond-mat.mtrl-sci
keywords phase change memorythermoelectric effectsfilamentary transportGe2Sb2Te5reset operationelectrothermal simulationmushroom cellspolarity dependence
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The pith

Reset operations in mushroom phase change memory cells require three times less energy when current flows from top electrode to narrow bottom electrode due to thermoelectric effects.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper performs 2D finite-element electrothermal simulations of Ge2Sb2Te5 mushroom cells to examine how thermoelectric effects and filamentary transport influence Reset and Set operations. It establishes that directing current from the top electrode toward the 4 nm bottom electrode cuts required energy and power by a factor of about three and current by a factor of two to reach the same high-resistance state. The simulations show that polarity affects where filaments form, where thermal runaway occurs, and how much material actually crystallizes or amorphizes. The active programming volume can be much smaller than the full mushroom shape and stops scaling once contacts exceed 10 nm.

Core claim

Reset operations with current going from the top electrode to the narrow 4 nm bottom electrode require ∼3x less energy and power, and ∼2x lower current to achieve the same Reset resistance, compared to the opposite polarity, due to thermoelectric effects. Filamentary conduction, electrical breakdown, thermal runaway, and local crystallization of amorphous Ge2Sb2Te5 depend on current polarity and thermal boundary conditions, and determine the location, shape, and volume of the programming region, which may be significantly smaller than the semi-cylindrical mushroom region. The programming volume does not scale with contact dimensions larger than 10 nm.

What carries the argument

2D finite-element electrothermal model incorporating spatial activation energy variations in amorphous Ge2Sb2Te5 together with phase-change dynamics.

Load-bearing premise

The 2D finite-element electrothermal model with spatial activation energy variations accurately captures filamentary conduction, thermal runaway, and phase-change dynamics in the amorphous material.

What would settle it

Fabricate mushroom cells with 4 nm bottom electrodes, measure reset energy and current for both polarities, and check whether one direction consistently requires roughly three times less energy to reach the target resistance.

Figures

Figures reproduced from arXiv: 2606.10262 by Ali Gokirmak, Helena Silva, Md Samzid Bin Hafiz.

Figure 1
Figure 1. Figure 1: PCM cell with a MOSFET in series for (a) top and (b) bottom bias polarity. Crystal orientation angles for crystalline phase and current density profiles for amorphous phase of PCM are shown. Wheater = 4 nm. PCM can be SET and Reset with ∼ 1–100 ns voltage pulses in the ∼ 1 V range, achieving large resistivity contrast (102–104×) in a CMOS compatible resistance range (kΩ–MΩ) and > 10 years retention at CPU … view at source ↗
Figure 2
Figure 2. Figure 2: Temperature dependent metastable resistivity ρ (blue spheres) of amorphous GST measured at device level along with crystalline resistivity. Adapted from [17], licensed under CC BY 4.0 [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: I-V characteristics of stable amorphous GST line cells with W × L × t ≈ 152 nm × 710 nm × 20 nm measured at 85, 125, 175, 225, and 300 K. Adapted from [18]. the difference between the forward and reverse currents that describe thermionic emission over an energy barrier modulated by the applied voltage [19]: I(V, T) = Iforward − Ireverse = I0e − EA−bωV kBT kBT − I0e − EA−(b−1)ωV kBT kBT (4) where I0 is a cu… view at source ↗
Figure 5
Figure 5. Figure 5: Random EA map with 2 nm×2 nm granularity in a 100 nm× 20 nm GST region (left) and the diffused EA used for simulations which reduces the EA gradients to enable convergence (right). The heater width is 4 nm. respectively [24]. S and QH are temperature dependent. Mod￾eling of QH can be found in [25]. The thermal transport model is solved along with electronic transport and phase change physics model [26] in … view at source ↗
Figure 4
Figure 4. Figure 4 [PITH_FULL_IMAGE:figures/full_fig_p003_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: 3D extruded view of the mushroom cell for (a) x-y plane with 10 nm out-of-plane depth (z direction) and (b) out of mushroom-plane (y–z) with 4 nm out-of-plane depth (x direction) 2D simulations. (a) shows the current density inside the amorphous region. beginning of electrical breakdown [PITH_FULL_IMAGE:figures/full_fig_p004_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: (a) Cell resistances after Reset (Rreset) and Set (Rset) for five cycles. (b) A small increase in Ireset and ISet results in a large increase in Rreset/Rset. (c) (a) (b) (f) (e) (d) (a) (b) (c) (d) (e) (f) [PITH_FULL_IMAGE:figures/full_fig_p004_8.png] view at source ↗
Figure 7
Figure 7. Figure 7: Reset, Set and read pulses voltages (Vapplied, Vg and Vcell) and current. Set pulse has longer fall time compared to Reset pulse. PCM cells go through higher temperature in Reset than Set operation. Reading is performed with Vg = 2 V to keep the n-MOSFET at low resistance, and Vapplied =0.1 V not to disturb the state of the cell. simulations with 4 nm out-of-plane depth ( [PITH_FULL_IMAGE:figures/full_fig… view at source ↗
Figure 9
Figure 9. Figure 9: ). Rreset significantly increases if an amorphous re￾gion completely covers the bottom contact. The increase in Rreset is relatively mild for higher Ireset beyond this level (a) (b) Reset Set [PITH_FULL_IMAGE:figures/full_fig_p004_9.png] view at source ↗
Figure 12
Figure 12. Figure 12: Crystal orientation angles for fcc-GST and current density profiles for a-GST during top and bottom polarity Set of cells Reset with top and bottom polarity, without accounting for thermal boundary resistances. (b) (a) (c) (d) (e) (f) (e)(d) (f) (a) (b) (c) 380 3300 T (K) 300 1400 0 80 θ (0) T (K) 300 1400 0 80 θ (0) [PITH_FULL_IMAGE:figures/full_fig_p005_12.png] view at source ↗
Figure 11
Figure 11. Figure 11: Rreset versus peak Reset power and Reset energy for both polarities. Top bias Reset requires ∼ 3.5× less power and energy than the bottom bias polarity to achieve same Rreset (∼ 48 MΩ). design. The thermoelectric term in Eq. 9, ∇ · (JST), which can be written in terms of its electron and hole contributions as ∇·(JST) = ∇·(JnSnT)+∇·(JpSpT), is very significant for Reset and Set operations due to (i) asymme… view at source ↗
Figure 13
Figure 13. Figure 13: Cell Resistance after Set operation versus Set current for top and bottom bias polarities. (a-f) Set cell crystalline orientation and peak temperature profiles for different Set currents. The cell was previously Reset with top bias polarity with 64 µA. Wheater = 4 nm. very large electron and hole concentrations (n, p). We estimate nliquid = pliquid ∼ 1 × 1022 cm−3 from the latent heat of fusion: latent he… view at source ↗
Figure 15
Figure 15. Figure 15: Cell resistances after Reset (Rreset) and Set (Rset) operation for 18 cycles (shown in [PITH_FULL_IMAGE:figures/full_fig_p006_15.png] view at source ↗
Figure 14
Figure 14. Figure 14: Shorter Reset (∼ 1 ns) and Set pulses (∼ 4 ns) voltage and current waveforms used for the out-of-plane simulations. Vapplied is kept constant, and only Vg is varied for Set/Reset operations. C. Orthogonal-Plane (y–z) Simulations We performed 2D simulations on the (y–z) plane in [PITH_FULL_IMAGE:figures/full_fig_p006_14.png] view at source ↗
read the original abstract

We performed a 2D finite-element electrothermal computational study of thermoelectric effects and filamentary electronic transport in Ge$_2$Sb$_2$Te$_5$ mushroom phase change memory cells during Reset and Set operations, accounting for spatial activation energy variations in amorphous Ge$_2$Sb$_2$Te$_5$ and phase-change dynamics. Reset operations with current going from the top electrode to the narrow 4 nm bottom electrode require $\sim$3x less energy and power, and $\sim$2x lower current to achieve the same Reset resistance, compared to the opposite polarity, due to thermoelectric effects. Filamentary conduction, electrical breakdown, thermal runaway, and local crystallization of amorphous Ge$_2$Sb$_2$Te$_5$ depend on current polarity and thermal boundary conditions, and determine the location, shape, and volume of the programming region, which may be significantly smaller than the semi-cylindrical mushroom region. The programming volume does not scale with contact dimensions larger than 10 nm. Larger contact areas introduce increased device-to-device and cycle-to-cycle variability due to filamentary conduction but are expected to lead to higher reliability and endurance.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript reports a 2D finite-element electrothermal simulation of filamentary electronic transport and thermoelectric effects in Ge₂Sb₂Te₅ mushroom phase-change memory cells. It claims that reset operations with current flowing from the top electrode to the narrow 4 nm bottom electrode require ∼3× less energy and power and ∼2× lower current to reach the same reset resistance than the opposite polarity, due to thermoelectric effects. Additional findings address polarity-dependent filamentary conduction, thermal runaway, local crystallization, the resulting programming volume (which may be smaller than the mushroom region and does not scale for contacts >10 nm), and increased device-to-device variability for larger contacts.

Significance. If the model predictions are reliable, the work supplies concrete design guidance on polarity selection for reduced programming energy in PCM cells and clarifies how filamentary transport governs programming volume and variability, both of which are practically relevant for scaling and endurance of phase-change memory.

major comments (2)
  1. [Abstract] Abstract: the quantitative factors (∼3× energy/power, ∼2× current) are stated as direct outcomes of the simulation, yet the manuscript supplies no mesh-convergence data, parameter-sensitivity study on the spatial activation-energy variations, or uncertainty quantification; these omissions are load-bearing for the central numerical claims.
  2. [Model description (implied by abstract)] The 2D finite-element model is used to predict 3D filamentary behavior and thermal-runaway dynamics without reported validation against experimental reset/set characteristics or against a 3D reference simulation; this directly affects in the predicted polarity asymmetry and programming-volume conclusions.
minor comments (2)
  1. Notation for the bottom-electrode diameter (stated as 4 nm) and contact-area scaling (>10 nm) should be cross-checked for consistency with the figures that illustrate the mushroom geometry.
  2. The abstract states that the programming volume 'may be significantly smaller than the semi-cylindrical mushroom region'; a quantitative comparison (e.g., volume ratio) in the results section would strengthen this statement.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major comment below and indicate the revisions we will make to strengthen the work.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the quantitative factors (∼3× energy/power, ∼2× current) are stated as direct outcomes of the simulation, yet the manuscript supplies no mesh-convergence data, parameter-sensitivity study on the spatial activation-energy variations, or uncertainty quantification; these omissions are load-bearing for the central numerical claims.

    Authors: We agree that explicit mesh-convergence data, sensitivity analysis on activation-energy spatial variations, and uncertainty quantification would strengthen the quantitative claims. In the revised manuscript we will add a mesh-convergence study in the Methods section demonstrating that the reported factors remain stable under successive refinement. We will also include a parameter-sensitivity analysis varying the activation-energy profile within literature-reported bounds and report the resulting variation in the polarity asymmetry factors. Basic uncertainty estimates derived from these variations will be provided alongside the central claims. revision: yes

  2. Referee: [Model description (implied by abstract)] The 2D finite-element model is used to predict 3D filamentary behavior and thermal-runaway dynamics without reported validation against experimental reset/set characteristics or against a 3D reference simulation; this directly affects in the predicted polarity asymmetry and programming-volume conclusions.

    Authors: The 2D axisymmetric model is selected to resolve the essential radial and axial transport governing filament formation and thermoelectric asymmetry while remaining computationally tractable. Model parameters are taken from established GST literature. We acknowledge that direct experimental validation and a full 3D reference simulation are absent. In revision we will add an explicit Limitations subsection discussing the 2D approximation, its expected impact on filamentary volume and thermal-runaway thresholds, and comparisons to any available 3D literature results. We will also clarify that the polarity asymmetry originates from the Seebeck/Peltier terms, which are dimension-independent in the governing equations. revision: partial

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper reports quantitative outcomes (~3x energy reduction, ~2x current reduction) as direct numerical results from a 2D finite-element electrothermal simulation that incorporates thermoelectric effects, spatial activation-energy variations, and phase-change dynamics as inputs. No step in the described chain reduces a claimed prediction to a fitted parameter or self-citation by construction; the model is physics-based and the polarity asymmetry is an emergent output rather than an input. The derivation is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract supplies no explicit free parameters, axioms, or invented entities; ledger entries cannot be populated from available information.

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Reference graph

Works this paper leans on

33 extracted references

  1. [1]

    Designing crystalliza- tion in phase-change materials for universal memory and neuro-inspired computing,

    W. Zhang, R. Mazzarello, M. Wuttig, and E. Ma, “Designing crystalliza- tion in phase-change materials for universal memory and neuro-inspired computing,”Nature Reviews Materials, vol. 4, pp. 150–168, 2019

    2019

  2. [2]

    Recent progress in phase-change memory technology,

    G. W. Burr, M. J. BrightSky, A. Sebastian, H.-Y . Cheng, J.-Y . Wu, S. Kim, N. E. Sosa, N. Papandreou, H.-L. Lung, H. Pozidiset al., “Recent progress in phase-change memory technology,”IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 6, no. 2, pp. 146–162, 2016

    2016

  3. [3]

    Material and process engineering challenges in ge-rich gst for embedded pcm,

    A. Redaelli, E. Petroni, and R. Annunziata, “Material and process engineering challenges in ge-rich gst for embedded pcm,”Materials Science in Semiconductor Processing, vol. 137, p. 106184, 2022

    2022

  4. [4]

    Phase change memory for automotive grade embedded NVM applications,

    P. Cappelletti, R. Annunziata, F. Arnaud, F. Disegni, A. Maurelli, and P. Zuliani, “Phase change memory for automotive grade embedded NVM applications,”Journal of Physics D: Applied Physics, vol. 53, no. 19, p. 193002, 2020

    2020

  5. [5]

    Modeling of SET and reset operations of phase-change memory cells,

    A. Faraclas, N. Williams, A. Gokirmak, and H. Silva, “Modeling of SET and reset operations of phase-change memory cells,”IEEE Electron Device Letters, vol. 32, no. 12, pp. 1737–1739, 2011

    2011

  6. [6]

    Modeling filamentary conduction in reset phase change memory devices,

    M. S. B. Hafiz, H. Silva, and A. Gokirmak, “Modeling filamentary conduction in reset phase change memory devices,”physica status solidi (RRL) – Rapid Research Letters, p. 2400416, 2025

    2025

  7. [7]

    Phase change materials and phase change memory,

    S. Raoux, F. Xiong, M. Wuttig, and E. Pop, “Phase change materials and phase change memory,”MRS Bulletin, vol. 39, no. 8, pp. 703–710, 2014

    2014

  8. [8]

    Phase-change random access memory: A scalable technology,

    S. Raoux, G. W. Burr, M. J. Breitwisch, C. T. Rettner, Y .-C. Chen, R. M. Shelby, M. Salinga, D. Krebs, S.-H. Chen, H.-L. Lung, and C. H. Lam, “Phase-change random access memory: A scalable technology,”IBM Journal of Research and Development, vol. 52, no. 4.5, pp. 465–479, 2008

    2008

  9. [9]

    Observation and modelling of homogeneous nucleation in ge 2sb2te5 mushroom cells during set operation,

    Y . Yu and M. Skowronski, “Observation and modelling of homogeneous nucleation in ge 2sb2te5 mushroom cells during set operation,”Journal of Materials Chemistry C, vol. 11, no. 11, pp. 3848–3854, 2023

    2023

  10. [10]

    3d cross-point phase-change memory for storage-class memory,

    H.-Y . Cheng, F. Carta, W.-C. Chien, H.-L. Lung, and M. J. BrightSky, “3d cross-point phase-change memory for storage-class memory,”Jour- nal of Physics D: Applied Physics, vol. 52, no. 47, p. 473002, 2019

    2019

  11. [11]

    Ielmini,Phase Change Memory: Device Physics, Reliability and Applications

    D. Ielmini,Phase Change Memory: Device Physics, Reliability and Applications. Cham: Springer, 2018

    2018

  12. [12]

    Threshold switching mechanism by high-field energy gain in the hopping transport of chalcogenide glasses,

    D. Ielmini, “Threshold switching mechanism by high-field energy gain in the hopping transport of chalcogenide glasses,”Physical Review B, vol. 78, no. 3, p. 035308, 2008

    2008

  13. [13]

    Recent progress in resistive random access memories: Materials, switching mechanisms, and performance,

    F. Pan, S. Gao, C. Chen, C. Song, and F. Zeng, “Recent progress in resistive random access memories: Materials, switching mechanisms, and performance,”Materials Science and Engineering: R: Reports, vol. 83, pp. 1–59, 2014

    2014

  14. [14]

    Electrical conduction in chalcogenide glasses of phase change memory,

    M. Nardone, M. Simon, I. V . Karpov, and V . G. Karpov, “Electrical conduction in chalcogenide glasses of phase change memory,”Journal of Applied Physics, vol. 112, no. 7, p. 071101, 2012

    2012

  15. [15]

    High speed, high temperature electrical characterization of phase change materi- als: metastable phases, crystallization dynamics, and resistance drift,

    F. Dirisaglik, G. Bakan, Z. Jurado, S. Muneer, M. Akbulut, J. Rarey, L. Sullivan, M. Wennberg, A. King, L. Zhanget al., “High speed, high temperature electrical characterization of phase change materi- als: metastable phases, crystallization dynamics, and resistance drift,” Nanoscale, vol. 7, no. 40, pp. 16 625–16 630, 2015

    2015

  16. [16]

    Electrical resistivity of liquid Ge2Sb2Te5 based on thin-film and nanoscale device measurements,

    K. Cilet al., “Electrical resistivity of liquid Ge2Sb2Te5 based on thin-film and nanoscale device measurements,”IEEE Transactions on Electron Devices, vol. 60, no. 1, pp. 433–437, 2013

    2013

  17. [17]

    Activation energy of metastable amorphous Ge2Sb2Te5 from room temperature to melt,

    S. Muneer, J. Scoggin, F. Dirisaglik, L. Adnane, A. Cywar, G. Bakan, K. Cil, C. Lam, H. Silva, and A. Gokirmak, “Activation energy of metastable amorphous Ge2Sb2Te5 from room temperature to melt,”AIP Advances, vol. 8, no. 6, p. 065314, 2018

    2018

  18. [18]

    Accelerating and stopping resistance drift in phase change memory cells via high electric field stress,

    R. S. Khan, A. H. Talukder, F. Dirisaglik, H. Silva, and A. Gokirmak, “Accelerating and stopping resistance drift in phase change memory cells via high electric field stress,”arXiv preprint arXiv:2002.12487, 2020

    arXiv 2002

  19. [19]

    Stopping resistance drift in phase change memory cells and analysis of charge transport in stable amorphous Ge2Sb2Te5,

    M. Tashfiq, B. Kashem, R. S. Khan, A. H. Talukder, F. Dirisaglik, and A. Gokirmak, “Stopping resistance drift in phase change memory cells and analysis of charge transport in stable amorphous Ge2Sb2Te5,”arXiv preprint arXiv:2210.14035, 2022

    arXiv 2022

  20. [20]

    Amorphous silicon thin film transistors,

    J. L. Lin and S. C. Lee, “Amorphous silicon thin film transistors,” Journal of the Chinese Institute of Engineers, vol. 18, no. 4, pp. 451– 460, 1995

    1995

  21. [21]

    Measurements of charge carrier mobilities and drift velocity saturation in bulk silicon of <111> and <100> crystal orientation at high electric fields,

    J. Becker, E. Fretwurst, and R. Klanner, “Measurements of charge carrier mobilities and drift velocity saturation in bulk silicon of <111> and <100> crystal orientation at high electric fields,”Solid-State Electronics, vol. 56, no. 1, pp. 104–110, 2010

    2010

  22. [22]

    Electrical measurement setup for phase-change materials,

    L. Adnane, A. Gokirmak, and H. Silva, “Electrical measurement setup for phase-change materials,”Review of Scientific Instruments, vol. 87, p. 075117, 2016

    2016

  23. [23]

    Modeling of phase-change memory: Nucleation, growth, and amorphization dynamics during SET and reset,

    Z. Woods and A. Gokirmak, “Modeling of phase-change memory: Nucleation, growth, and amorphization dynamics during SET and reset,” IEEE Transactions on Electron Devices, vol. 64, no. 11, pp. 4466–4471, 2017

    2017

  24. [24]

    Phonon and electron transport through Ge2Sb2Te5 films and interfaces bounded by metals,

    J. Lee, E. Bozorg-Grayeli, S. Kim, M. Asheghi, H.-S. P. Wong, and K. E. Goodson, “Phonon and electron transport through Ge2Sb2Te5 films and interfaces bounded by metals,”Applied Physics Letters, vol. 102, no. 19, p. 191911, 2013

    2013

  25. [25]

    Modeling and impacts of the latent heat of phase change and specific heat for phase change materials,

    J. Scoggin, R. S. Khan, H. Silva, and A. Gokirmak, “Modeling and impacts of the latent heat of phase change and specific heat for phase change materials,”Applied Physics Letters, vol. 112, no. 19, p. 193502, 2018

    2018

  26. [26]

    Modeling hetero- geneous melting in phase change memory devices,

    J. Scoggin, Z. Woods, H. Silva, and A. Gokirmak, “Modeling hetero- geneous melting in phase change memory devices,”Applied Physics Letters, vol. 114, no. 4, p. 043502, 2019

    2019

  27. [27]

    Impact of thermoelectric effects on phase change memory characteristics,

    N. Ciocchini, M. Laudato, A. Leone, P. Fantini, A. L. Lacaita, and D. Ielmini, “Impact of thermoelectric effects on phase change memory characteristics,”IEEE Transactions on Electron Devices, vol. 62, no. 10, pp. 3264–3271, 2015

    2015

  28. [28]

    Modeling of thermoelectric effects in phase change memory cells,

    A. Faraclas, G. Bakan, L. Adnane, F. Dirisaglik, N. E. Williams, A. Gokirmak, and H. Silva, “Modeling of thermoelectric effects in phase change memory cells,”IEEE Transactions on Electron Devices, vol. 61, no. 2, pp. 372–378, 2014

    2014

  29. [29]

    (digital presentation) finite element modeling of thermoelectric effects in phase change memory cells,

    M. T. B. Kashem, J. Scoggin, H. Silva, and A. Gokirmak, “(digital presentation) finite element modeling of thermoelectric effects in phase change memory cells,”ECS Transactions, vol. 108, no. 1, pp. 3–15, 2022

    2022

  30. [30]

    High-temperature thermoelectric transport at small scales: Thermal generation, transport and recombination of minority carriers,

    G. Bakan, N. Khan, H. Silva, and A. Gokirmak, “High-temperature thermoelectric transport at small scales: Thermal generation, transport and recombination of minority carriers,”Scientific Reports, vol. 3, no. 1, p. 2724, 2013

    2013

  31. [31]

    Phonon and electron transport through Ge2Sb2Te5 films and interfaces bounded by metals,

    J. Lee, E. Bozorg-Grayeli, S. Kim, M. Asheghi, H.-S. P. Wong, and K. E. Goodson, “Phonon and electron transport through Ge2Sb2Te5 films and interfaces bounded by metals,”Applied Physics Letters, vol. 102, p. 191911, 2013

    2013

  32. [32]

    Thermal conductivity of phase-change memory materials from cryogenic temperatures to 400 k,

    J. Lee, J. P. Reifenberg, E. Bozorg-Grayeli, L. Hom, Z. Li, S. B. Kim, M. Asheghi, H.-S. P. Wong, and K. E. Goodson, “Thermal conductivity of phase-change memory materials from cryogenic temperatures to 400 k,” in12th IEEE Intersociety Conference on Thermal and Thermome- chanical Phenomena in Electronic Systems, 2010, pp. 1–8

    2010

  33. [33]

    Thermal boundary resistance measurements for phase-change memory devices,

    J. P. Reifenberg, K.-W. Chang, M. A. Panzer, S. Kim, J. A. Rowlette, M. Asheghi, H.-S. P. Wong, and K. E. Goodson, “Thermal boundary resistance measurements for phase-change memory devices,”IEEE Electron Device Letters, vol. 31, no. 1, pp. 56–58, 2010

    2010