Journal of Materials Science

, Volume 51, Issue 23, pp 10408–10417 | Cite as

Crystalline coherence length effects on the thermal conductivity of MgO thin films

  • Kelsey E. Meyer
  • Ramez Cheaito
  • Elizabeth Paisley
  • Christopher T. Shelton
  • Jeffrey L. Braun
  • Jon-Paul Maria
  • Jon F. Ihlefeld
  • Patrick E. Hopkins
Original Paper


Phonon scattering in crystalline systems can be strongly dictated by a wide array of defects, many of which can be difficult to observe via standard microscopy techniques. We experimentally demonstrate that the phonon thermal conductivity of MgO thin films is proportional to the crystal’s coherence length, a property of a solid that quantifies the length scale associated with crystalline imperfections. Sputter-deposited films were prepared on (100)-oriented silicon and then annealed to vary the crystalline coherence, as characterized using x-ray diffraction line broadening. We find that the measured thermal conductivity of the MgO films varies proportionally with crystalline coherence length, which is ultimately limited by the grain size. The microstructural length scales associated with crystalline defects, such as small-angle tilt boundaries, dictate this crystalline coherence length, and our results demonstrate the role that this length scale has on the phonon thermal conductivity of thin films. Our results suggest that this crystalline coherence length scale provides a measure of the limiting phonon mean free path in crystalline solids, a quantity that is often difficult to measure and observe with more traditional imagining techniques.

Defect-induced deviations in lattice structure can give rise to phonon scattering processes and changes in the phonon thermal conductivity of crystals. Where ample experimental works have studied and validated classical scattering theories regarding phonon–grain boundary and phonon–impurity thermal resistances on bulk- and nanoscales [1, 2, 3, 4, 5, 6, 7, 8], the phonon scattering mechanisms contributing to thermal resistances at finer scale defects, such as small-angle tilt boundaries and dislocations, have been much less frequently studied [9]. This has led to voids in the depth of understanding of the interplay between, and importance of, phonon and lattice defect scattering relative to the interaction of phonons with other static impurities, such as incoherent grain boundaries, mass impurities, and interfaces between dissimilar materials. Progress in this fundamental understanding of the phonon–lattice defect interaction will have major impacts in the design of novel classes of material systems, such as recently discovered systems synthesized by utilizing screw dislocations [10, 11, 12, 13, 14], high figure of merit thermoelectric materials designed with dense dislocations arrays [15], nanostructures with dislocation dense interfaces that impact the thermal boundary conductance [16, 17, 18], and thermal transport in ferroelectric materials where coherent ferroelastic domain walls affect the phononic resistance [19, 20, 21].

In the present study, we report on measurements of the room temperature thermal conductivity of a series of magnesium oxide (MgO) nanocrystalline thin films in which the crystalline coherence lengths (the characteristic length of crystal devoid of translational symmetry-breaking defects) of the MgO films are varied. Small-angle tilt boundaries defining crystallites of similar dimensions to the measured crystal coherence length were previously identified in identically processed films. These, in addition to grain boundaries and other crystallographic defects, such as dislocations, were attributed to a damping of optical phonons [22]. It is therefore anticipated that these same defects may affect the transport of heat-carrying phonons. We use time domain thermoreflectance (TDTR) [23] to measure the thermal properties of the MgO thin films at room temperature and atmospheric conditions; by utilizing a combination of both the in-phase and the ratio of the in-phase to out-of-phase components of the TDTR response in tandem, we demonstrate the ability to measure the thermal conductivity of the MgO films at a single modulation frequency, while separating the influence of thermal boundary conductance across both the front and back thin film interfaces from this thin film thermal conductivity measurement. We show that the thermal conductivities of the MgO films increase with an increase in crystalline coherence length, which is correlated with the defects that limit the crystalline coherence. Our work demonstrates the ability to quantify the influence of defects on the phonon thermal conductivity by an average length scale of crystal translational symmetry—the crystalline coherence length. Our results suggest that this crystalline coherence length scale provides a measure of the limiting phonon mean free path in crystalline solids, a quantity that can be determined via standard X-ray diffraction and is often difficult to measure and observe with traditional microcopy techniques.

The 80-nm-thick MgO film series was prepared on (100)-oriented silicon substrates via 30° off-axis RF magnetron sputter deposition within a Kurt J. Lesker Lab 18 instrument. Prior to loading in the load-locked sputter chamber, the substrates underwent a 7:1 buffered HF etch (pH of 5.5) and de-ionized-H2O rinse to remove the native silicon dioxide surface. The film was sputtered from a single-phase sintered MgO target in 5 mTorr of argon at room temperature with a power density of 3.7 W/cm2. The wafer was subsequently divided, and sections were processed between 200 and 800 °C in 200 °C intervals in air for 1 h; varying the annealing temperature directly correlates to a change in crystalline coherence length [22]. Silicon was chosen as a substrate due to its predicted phase stability and chemical inertness with MgO in this temperature range, suggesting the formation of clean interfaces with no secondary phases during the deposition and annealing process [24]; furthermore, the high thermal conductivity of silicon ensures maximum sensitivity to the thermal conductivity of the MgO thin films in our TDTR measurements. Phase purity and crystalline coherence lengths were characterized via X-ray diffraction using a Philips X’Pert MPD with Cu Kα radiation in the Bragg–Brentano geometry. As such, the diffraction vector is normal to the sample surface, and the only lattice planes to which we are collecting diffracted X-rays are those that are parallel to the film surface. Therefore, crystallite dimensions to which this technique is sensitive are those normal to the film surface—the same direction as thermal conductivity is measured. Crystalline coherence lengths were calculated using X-ray line broadening and Scherrer’s formula [25].

The surface roughnesses, thicknesses, and densities of the annealed samples were characterized using a combination of atomic force microscopy (AFM), X-ray reflectivity (XRR), and variable-angle spectroscopy ellipsometry (VASE). Cross-sectional and plan-view microstructural images (Fig. 1a–f) were obtained by scanning electron microscopy with an FEI Verios. Secondary electron images were collected with an electron landing energy of 500–2000 V stage bias. Average grain sizes were determined from the plan-view images using the linear intercept method [26]. Additionally, 78-nm-thick aluminum films were electron beam evaporated onto the samples for use as transducers in subsequent TDTR scans. The precise aluminum film thicknesses were determined using both mechanical profilometry and picosecond acoustic measurements during subsequent TDTR scans [27, 28].
Figure 1

SEM images for varying processing temperatures: a 25 °C, b 200 °C, c 400 °C, d 600 °C, e 800 °C; f cross-section for representative sample; g coherence length as a function of temperature; h lattice parameter as a function of coherence length. The SEM plan-view images show that grain size and porosity are consistent among all of the films, and the cross-sectional images show that all films are 80 nm thick. The coherence length is found to vary in direct proportion to the processing temperature. As the crystallinity increases, the measured lattice parameter decreases and approaches the single crystal value intrinsic to MgO

As shown in the SEM images in Fig. 1a–f, polycrystalline grains with columnar morphology comprise the films. Fig. 1g shows the coherent scattering lengths of X-rays for the MgO samples processed between room temperature and 800 °C, and reveals a trend of increasing length with post-deposition processing temperature, similar to that reported previously in identically processed MgO thin films [22]. Scattering lengths were calculated using the 200 MgO X-ray reflection; the measured coherence lengths varied from 5.4 nm for the as-deposited sample to 19.5 nm for the 800 °C processed sample. Lattice parameters were also determined from the 200 peak position using the silicon 400-reflection as a reference for sample displacement error correction. As shown in Fig. 1h, the lattice parameter is reduced with increasing coherence length. The data indicate that the higher the degree of crystalline perfection, the more closely the lattice parameter approaches the accepted single crystal value of 4.21 Å [29]. In these fine crystallite size films, it is likely that disorder near surfaces, defects, and grain boundaries gives rise to these expanded lattice parameters, as has been suggested previously for fine-grained MgO [30]. SEM images of the sample series shown in Fig. 1a–f indicate that physical grain size, as defined by the distance between well-defined grain boundaries, is constant among the sample series, where the average grain size is 20.9 ± 1.3 nm, indicating that the varying crystalline coherence lengths are not driven by grains separated by large-angle boundaries. Additionally, no obvious change in density or porosity was observed with annealing condition. No statistically significant variation in film thickness was identified (average of measurements from XRR and VASE, as listed in Table 1), further suggesting that film density was constant among the sample series. However, increasing the annealing temperature did lead to changes in the MgO surface roughnesses and the formation of increasingly thick SiO2 layers between the MgO and silicon substrate with increased temperature. More specifically, the MgO surface roughness increased from 2.8 ± 1.2 nm for as-sputtered samples to 9.3 ± 5.3 nm for the sample annealed at 800 °C (average of measurements from XRR, VASE and AFM, and listed in Table 1); also, we detected an increasingly thick SiO2 layer between the MgO and silicon that grew to as thick as 6.1 ± 1.2 nm after the 800 °C anneal (average of measurements from XRR and VASE, as listed in Table 1). We note that the relatively large uncertainties in our reported average values for SiO2 thicknesses could be indicative of the different sensitives of XRR and VASE to this buried SiO2 film, as the uncertainties are determined from the standard deviation among the data collected with each technique. Regardless of the temperature, variations in our samples were confined around the top and bottom MgO film interfaces. Therefore, since the microstructural properties in the MgO films away from the film boundaries are comparable among all of the films, the only factor varying within the bulk of the film is the crystalline coherence length, which has previously been verified [22].
Table 1

MgO film thickness (average of measurements made with XRR and VASE), MgO surface roughness (average of measurements made with XRR, VASE and AFM), and thickness of SiO2 layer between MgO and Si substrate (average of measurements made with XRR and VASE)

Anneal temperature (°C)






MgO film thickness (nm)

84.0 ± 2.8

78.9 ± 4.9

79.3 ± 3.5

83.6 ± 1.7

82.4 ± 1.6

MgO surface roughness (nm)

2.8 ± 1.1

5.8 ± 3.4

4.72 ± 1.8

5.1 ± 2.3

9.3 ± 5.3

SiO2 layer thickness (nm)


3.8 ± 3.8

3.4 ± 3.1

3.5 ± 2.1

6.1 ± 1.2

The uncertainty reported in these measurements represents the standard deviation among all the values determined from the different techniques

The thermal conductivities of the MgO films were determined using TDTR by fitting the data to a multi-layer thermal model described in detail in the literature [31, 32, 33, 34]. Briefly, TDTR is a non-contact optical pump–probe technique that uses a short-pulsed laser to both produce and monitor modulated heating events on the surface of a sample. The laser output from a sub-picosecond oscillator is separated into pump and probe paths, in which the relative optical path lengths are adjusted with a mechanical delay stage. The pump path is modulated to create a frequency dependent temperature variation on the surface of the sample, and the in-phase and out-of-phase signals of the probe beam locked into the modulation frequency of the pump are monitored with a lock-in amplifier. Prior to TDTR measurements, the sample surfaces were coated with a thin aluminum film so that the changes in reflectivity of the surfaces were indications of the change in temperature within the optical penetration depth of the aluminum; this change in reflectivity is driven by the thermal properties of the MgO film, the silicon substrate, and the thermal boundary conductances across the Al/MgO and MgO/Si interfaces. We assume literature values for the heat capacities of the aluminum [35], MgO [36], and silicon [37], leaving the unknowns in our thermal model as the thermal boundary conductances across the Al/MgO and MgO/Si interfaces (hK,Al/MgO and hK,MgO/Si, respectively) and the thermal conductivity of the MgO film, κMgO [31].

Typical TDTR analyses on thick films or substrates can analyze the ratio of the in-phase (X) to out-of-phase (Y) signals from the lock-in amplifier to determine the thermal conductivity and thermal boundary conductance across the metal/sample interface (assuming a relatively high thermal effusivity). For example, Fig. 2a shows a typical TDTR data set collected on our samples, with the inset showing the corresponding thermal model fit. However, in the case of our MgO thin films, our TDTR measurements are also sensitive to the thermal boundary conductance across the MgO/Si interface, even at relatively high modulation frequencies (~10 MHz). Therefore, we cannot use the ratio signal alone to measure the thermal conductivity of the MgO since we cannot uniquely separate this from the two thermal boundary conductances. To overcome this experimental limitation, we modify our analysis approach by utilizing a combination of both the in-phase signal and the ratio of the in-phase to out-of-phase components of the TDTR response in tandem. The in-phase component of the lock-in frequency response is sensitive to the Al/MgO interface during the first nanosecond of a TDTR scan, since the in-phase component is related to the single pulse response in the time domain, as shown in Fig. 2b. Exploiting this sensitivity, we fit the data for the front-side boundary conductance using the in-phase component (X) and then apply this value when using the ratio (−X/Y) to fit for both the back-side conductance and MgO thermal conductivity. As shown in Fig. 2c, the ratio is highly sensitive to the thermal conductivity of the MgO and the thermal boundary conductance at the MgO/Si interface. While the ratio is only minorly sensitive to the thermal boundary conductance across the Al/MgO interface, this sensitivity can vary based on the thermal conductivity of the MgO, which changes by a factor of ~3 among the films studied in this work. Therefore, this approach is necessary to accurately measure the thermal conductivity of the MgO, while also evaluating the corresponding uncertainty in our measurements. We discuss this approach in more detail, including sensitivity analyses in our previous work [38].
Figure 2

a Typical TDTR data on our MgO films for the largest coherence length sample (blue line/higher data set) and lowest coherence length sample (black line/lower data set); (inset) corresponding thermal model fits to the largest coherence length sample’s representative TDTR data. Sensitivities of our thermal model for determining the thermal boundary conductances across the Al/MgO and MgO/Si interfaces (hK,Al/MgO and hK,MgO/Si, respectively) and the thermal conductivity of the MgO (κMgO) from analyzing the b in-phase and c ratio of in-phase to out-of-phase TDTR data

Using this aforementioned analysis approach in an interactive fashion, we measure hK,Al/MgO, hK,MgO/Si, and κMgO at a single TDTR scan at a single frequency. To ensure accuracy of this approach, we measure hK,Al/MgO, hK,MgO/Si, and κMgO at different pump modulation frequencies. Our measured results of the thermal conductivities and thermal boundary conductances are shown in Fig. 3a and b respectively, for two representative samples (those with the largest and smallest coherence lengths: 800 °C annealed and as-deposited samples, respectively). We measured hK,MgO/Si to be relatively constant across all the samples (~200–300 MW m−2 K−1), indicating the negligible influence of the change in SiO2 thickness at the MgO/Si boundary on hK,MgO/Si. We find no statistically significant and appreciable change in the thermal conductivity or boundary conductances with varying frequencies, indicating the robust ability of our approach to measure the intrinsic thermal conductivity of thin films with a single TDTR measurement at one modulation frequency when thermal boundary conductance could influence the thermal response. This elucidates a unique analysis procedure when using TDTR to measure the thermal properties of thin films.
Figure 3

a Thermal conductivities and b thermal boundary conductances as a function of modulation frequency. Analyzing the in-phase signal in tandem with the ratio of the in-phase to out-of-phase signal, we find that, within a standard deviation, the thermal conductivities and thermal boundary conductances for the maximum and minimum coherence lengths are constant among varying modulation frequencies. This is confirmation that our analysis technique enables us to determine hK,Al/MgO, hK,MgO/Si, and κMgO from a single TDTR scan at any given modulation frequency

Our reported uncertainties in the values reported for thermal conductivities and thermal boundary conductances are determined by considering three different sources of error. First, we calculate the standard deviation among the entire set of measurements for each sample (multiple measurements on each sample). Second, we assume a ~10 % uncertainty in the aluminum transducer film thickness. Finally, we determine a 95 % confidence interval for each measurement. We take the square root of the sum of the squares of each deviation from the mean values resulting from these sources of uncertainties to construct our error bars. We note that largest uncertainties in our reported values lie in the samples with the highest thermal boundary conductances and highest thermal conductivities. This is consistent with the fact that as the thermal conductivity of the MgO thin films increase (or the interface conductances increase), and hence, the corresponding thermal resistances decrease, our TDTR measurements become less sensitive to these thermophysical properties. However, our reported values still lie within a 95 % confidence bound. Along these lines, it is worth noting that the apparent observed frequency dependence in the thermal conductivity measurements of the MgO samples with the maximum coherence length (Fig. 3a) are nearly constant when considering our aforementioned confidence interval, and still only deviate ~20 % about the mean; in other words, this fluctuation in our measured data for thermal conductivity with frequency is not physical, but just an artifact of the sensitivity of TDTR for measuring relatively thermally conductive thin films (i.e., films with relatively low thermal resistance), especially when using lower pump modulation frequencies where the thermal penetration depth is increased and therefore sampling more of the underlying substrate relative to the thin film. However, as the coherence length in the MgO thin film is decreased, and the thermal conductivity is lowered, TDTR measurements are much more robust and sensitive in measuring thermal conductivity, consistent with the relatively minor uncertainty associated with our fits.

Measuring the thermal conductivity as a function of crystallinity, we find that thermal conductivity varies with the coherence length and plateaus as the crystalline coherence length approaches the average grain size (20.9 ± 1.3 nm), as depicted in Fig. 4. The thermal conductivity begins to plateau at larger crystalline coherence lengths because the coherence length of these samples is ultimately limited by the large-angle grain boundaries. This is consistent with Matthiessen’s Rule, which asserts that the shortest phonon scattering length scale will dominate the average mean free path. Therefore, the large reduction in the measured thermal conductivities of these MgO samples compared to bulk single crystalline MgO (Ref. [39]) is due to the grain boundaries for the largest coherence lengths and limited by various imperfections in the crystal as the coherence length is decreased. Because the film thickness, density, grain size, and porosity are consistent among the sample series, the only factor that changes in these polycrystalline films is the crystalline coherence length, further supporting this observation of a transition from imperfection-limited thermal conductivity at small coherence lengths to grain boundary limited thermal conductivity at the larger coherence lengths.
Figure 4

Thermal conductivity as a function of coherence length. The bulk value for MgO given from Touloukian et al. is shown for comparison [39]. We observe an increase in thermal conductivity with increasing crystalline coherence length, which is ultimately limited by the grain size of the polycrystalline MgO films. The reduction in crystalline coherence length leads to MgO samples with thermal conductivities that are roughly a factor of four higher than the predicted minimum limit (Eq. 1), a factor of three lower than the largest coherence length sample (limited by grain boundary scattering, which remains constant for all coherence lengths), and a factor of ten lower than bulk MgO [39]

We note that the surfaces of the MgO film are changing among the samples processed at different temperatures; as previously mentioned, with increasing MgO processing temperature, the MgO surface becomes more rough and the SiO2 layer between the MgO and silicon becomes thicker. However, we measure an increasing thermal conductivity with increased temperature, which would imply that the increased surface roughness and increase in SiO2 thickness, which would add thermal resistance to the system, play only a minor role in our thermal conductivity measurements of MgO compared to the changing crystalline coherence length. This also gives further support to our data representing the intrinsic thermal conductivity of the MgO, and our ability to separate the resistances at the MgO interfaces from our reported values of κ.

To put the magnitude of the reduction in thermal conductivity due to the crystalline coherence length scales into perspective, we turn to the minimum limit to thermal conductivity [40]. Assuming an isotropic solid, the minimum limit is given by
$$ \kappa_{\hbox{min} } = \frac{{\hbar^{2} }}{{6\pi^{2} k_{\text{B}} T^{2} }}\sum_{j} \int_{0}^{{\omega_{c,j} }} {\tau_{\hbox{min} ,j} } \frac{{\omega^{4} }}{{v_{j} }}\frac{{\exp \left[ {\frac{\hbar \omega }{{k_{\text{B}} T}}} \right]}}{{\left( {\exp \left[ {\frac{\hbar \omega }{{k_{\text{B}} T}}} \right] - 1} \right)^{2} }}{\text{d}}\omega, $$
where κmin is the minimum thermal conductivity, j is the phonon polarization index, τmin is the minimum scattering time, ω is the angular frequency, ωc,j is the cut-off frequency, and vj is the phonon group velocity. To evaluate Eq. (1) for this material system, we use a Debye assumption with sound velocities of the acoustic branches taken from the experimentally determined dispersion [41] in the [100] direction. While the lowest thermal conductivity sample is a factor of 4 higher than the thermal conductivity predicted from the minimum limit, it is lower than the bulk thermal conductivity by an order of magnitude [39].
To confirm that this variation in thermal conductivity is due to the change in crystalline coherence length, and not simply a result of changing lattice parameter, we compare our experimental results to the Leibfried–Schlomann equation [42] given by
$$ \kappa = \frac{{\beta Ma\theta_{D}^{3} }}{{T\gamma^{2} }}, $$
where β is a factor inversely related to γ (the Grüneisen parameter), M is the average atomic mass, a is the lattice constant, θD is the Debye temperature, T is temperature, and γ is the Grüneisen parameter, given by
$$ \gamma^{2} = \frac{{a^{6} }}{{\omega^{2} }}\left( {\frac{\partial \omega }{{\partial (a^{3} )}}} \right)^{2}. $$

We determine ∂ω/∂(a3) from the experimentally measured transverse optical frequencies, which depend on unit cell volume [22], and scale the Debye temperature for each film assuming θD,film = θD,lit(afilm/alit). Through this analysis, we observe opposite trends when comparing our experimental results to the predicted variation of thermal conductivity due to lattice spacing. This implies that the change in lattice parameter among the sample series is not responsible for the variation in thermal conductivity; rather, that defects responsible for the crystalline coherence are the driving force impacting phonon scattering.

This demonstrates the ability to quantify the influence of defects on the phonon thermal conductivity by the crystalline coherence length of the crystal. This has the advantage of offering limiting length scales for phonon transport in crystalline systems in which imperfections are difficult to characterize and/or model. For example, using molecular dynamics simulations, Ni et al. [9] showed that localized strain fields, varying atomic spacings, and modifications to the intrinsic anharmonic phonon–phonon interaction strength near defects, such as dislocation cores, must be accounted for to properly model the phonon–lattice defect dynamics in the thermal conductivity. Unlike phonon–grain boundary and phonon–mass impurity-limited thermal transport [1, 2, 3, 4, 5, 6, 7, 8], modeling these processes is not easily or accurately feasible with simplified kinetic theory-type approaches, and therefore, predictions of changes in thermal conductivity due to these imperfections can be daunting. However, our work suggests that the characterization of a crystalline coherence length gives insight to qualitatively compare changes in thermal conductivity of similar materials with different degrees of crystalline imperfections.

As a final note, our experimental measurements in Fig. 3b show a relatively negligible dependence of thermal boundary conductance with crystalline coherence length of the MgO. We have previously observed that interfacial imperfections can lead to changes in thermal boundary conductance [16, 43]. Given that we do not observe any substantial structural changes at the surfaces of the MgO films, we would not expect any changes in thermal boundary conductance, which is consistent with our measurements of hK at each interface. Furthermore, it is interesting to note that the Al/MgO thermal boundary conductance is consistently lower than the MgO/Si thermal boundary conductance regardless of the MgO crystalline coherence length. While more work must be done that specifically focuses on the role of interface defects, our results highlight the potential impact of our previously discussed TDTR analysis to extract the thermal boundary conductance across thin films interfaces and, using this, to assess the role of changes in atomic-scale defects at material interfaces on changes (or lack thereof) in thermal boundary conductance.

In conclusion, we have investigated the effects of crystallinity changes on the thermal conductivity of MgO thin films. We find a systematic increase in thermal conductivity with increasing coherence length. Our thermal model, while sufficient for many other material systems and phonon scattering processes, fails to account for this crystallinity effect. This is consistent with previous studies and implies that much more complex modeling is necessary to understand the effects of dislocations on phonon scattering.



The authors would like to thank J. T. Gaskins for electron beam evaporation of the aluminum transducers. The authors acknowledge the use of the Analytical Instrument Facility (AIF) at North Carolina State University, which is supported by NSF contracts DMR 1337694 and DMR 1108071. This work was supported by the Laboratory Directed Research and Development (LDRD) program at Sandia National Laboratories, the Office of Naval Research (N00014-15-12769), and the National Science Foundation (EECS-1509362). Sandia National Laboratories is a multiprogram laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Company, for the United States Department of Energy’s National Nuclear Security Administration under contract DE-AC04–94AL85000.


  1. 1.
    Klemens PG (1955) The scattering of low-frequency lattice waves by static imperfections. Proc Phys Soc Lond Sect A 68 1113–1128Google Scholar
  2. 2.
    Srivastava GP (1990) The physics of phonons. Taylor and Francis, New YorkGoogle Scholar
  3. 3.
    Marconet AM, Asheghi M, Goodson KE (2013) From the casimir limit to phononic crystals: twenty years of phonon transport studies using silicon-on-insulator technology. J Heat Transfer 135:061601CrossRefGoogle Scholar
  4. 4.
    Abeles B (1963) Lattice thermal conductivity of disordered semiconductor alloys at high temperatures. Phys Rev 131:1906–1911CrossRefGoogle Scholar
  5. 5.
    Cheaito R, Duda JC, Beechem TE, Hattar K, Ihlefeld JF, Medlin DL, Rodriguez MA, Campion MJ, Piekos ES, Hopkins PE (2012) Experimental investigation of size effects on the thermal conductivity of silicon-germanium alloy thin films. Phys Rev Lett 109:195901CrossRefGoogle Scholar
  6. 6.
    Wang Z, Alaniz JE, Jang W, Garay JE, Dames C (2011) Thermal conductivity of nanocrystalline silicon: importance of grain size and frequency-dependent mean free paths. Nano Lett 11:2206–2213CrossRefGoogle Scholar
  7. 7.
    Donovan BF, Foley BM, Ihlefeld JF, Maria J-P, Hopkins PE (2014) Spectral phonon scattering effects on the thermal conductivity of nano-grained barium titanate. Appl Phys Lett 105:082907CrossRefGoogle Scholar
  8. 8.
    Foley BM, Brown-Shaklee HJ, Duda JC, Cheaito R, Gibbons BJ, Medlin D, Ihlefeld JF, Hopkins PE (2012) Thermal conductivity of nano-grained SrTiO3 thin films. Appl Phys Lett 101:231908CrossRefGoogle Scholar
  9. 9.
    Ni Y, Xiong S, Volz S, Dumitrica T (2014) Thermal transport along the dislocation line in silicon carbide. Phys Rev Lett 113:124301CrossRefGoogle Scholar
  10. 10.
    Bierman MJ, Lau YKA, Kvit AV, Schmitt AL, Jin S (2008) Dislocation-driven nanowire growth and Eshelby twist. Science 320:1060–1063CrossRefGoogle Scholar
  11. 11.
    Jacobs BW, Crimp MA, McElroy K, Ayres VM (2008) Nanopipes in gallium nitride nanowires and rods. Nano Lett 8:4353–4358CrossRefGoogle Scholar
  12. 12.
    Zhu J, Peng H, Marshall AF, Barnett DM, Nix WD, Cui Y (2008) Formation of chiral branched nanowires by the Eshelby twist. Nat Nanotechnol 3:477–481CrossRefGoogle Scholar
  13. 13.
    Morin SA, Jin S (2010) Screw dislocation-driven epitaxial solution growth of ZnO nanowires seeded by dislocations in GaN substrates. Nano Lett 10:3459–3463CrossRefGoogle Scholar
  14. 14.
    Meng F, Morin SA, Forticaux A, Jin S (2013) Screw dislocation driven growth of nanomaterials. Acc Chem Res 46:1616–1626CrossRefGoogle Scholar
  15. 15.
    Kim SI, Lee KH, Mun HA, Kim HS, Hwang SW, Roh JW, Yang DJ, Shin WH, Li XS, Lee YH, Snyder GJ, Kim SW (2015) Dense dislocation arrays embedded in grain boundaries for high-performance bulk thermoelectrics. Science 348:109–114CrossRefGoogle Scholar
  16. 16.
    Hopkins PE (2013) Thermal transport across solid interfaces with nanoscale imperfections: effects of roughness, disorder, dislocations, and bonding on thermal boundary conductance. ISRN Mech Eng 2013:682586CrossRefGoogle Scholar
  17. 17.
    Hopkins PE, Duda JC, Clark SP, Hains CP, Rotter TJ, Phinney LM, Balakrishnan G (2011) Effect of dislocation density on thermal boundary conductance across GaSb/GaAs interfaces. Appl Phys Lett 98:161913CrossRefGoogle Scholar
  18. 18.
    Su Z, Huang L, Liu F, Freedman JP, Porter LM, Davis RF, Malen JA (2012) Layer-by-layer thermal conductivities of the Group III nitride films in blue/green light emitting diodes. Appl Phys Lett 100:201106CrossRefGoogle Scholar
  19. 19.
    Ihlefeld JF, Foley BM, Scrymgeour DA, Michael JR, McKenzie BB, Medlin DL, Wallace M, Trolier-McKinstry S, Hopkins PE (2015) Room temperature voltage tunable thermal conductivity via reconfigurable interfaces in ferroelectric thin films. Nano Lett 15:1791–1795CrossRefGoogle Scholar
  20. 20.
    Hopkins PE, Adamo C, Ye L, Huey BD, Lee SR, Schlom DG, Ihlefeld JF (2013) Effects of coherent ferroelastic domain walls on the thermal conductivity and kapitza conductance in bismuth ferrite. Appl Phys Lett 102:121903CrossRefGoogle Scholar
  21. 21.
    Mante AJH, Volger J (1966) The thermal conductivity of BaTiO3 in the neighbourhood of its ferroelectric transition temperatures. Phys Lett 24A:139–140Google Scholar
  22. 22.
    Ihlefeld JF, Ginn JC, Shelton DJ, Matias V, Rodriguez MA, Kotula PG, Carroll JF, Boreman GD, Clem PG, Sinclair MB (2010) Crystal coherence length effects on the infrared optical response of MgO thin films. Appl Phys Lett 97:191913CrossRefGoogle Scholar
  23. 23.
    Cahill DG (2004) Analysis of heat flow in layered structures for time-domain thermoreflectance. Rev Sci Instrum 75:5119–5122CrossRefGoogle Scholar
  24. 24.
    Hubbard KJ, Schlom DG (1996) Thermodynamic stability of binary oxides in contact with silicon. J Mater Res 11:2757–2776CrossRefGoogle Scholar
  25. 25.
    Scherrer P (1918) Bestimmung der Größe und der inneren Struktur von Kolloidteilchen mittels Röntgenstrahlen. Nachrichten von der Gesellschaft der Wissenschaften zu Göttingen, Mathematisch-Physikalische Klasse 1918:98–100Google Scholar
  26. 26.
    ASTM (2014) Standard test methods for determining average grain size, ASTM International designation: E112–13. ASTM, West Conshohocken, pp 1–28Google Scholar
  27. 27.
    Thomsen C, Strait J, Vardeny Z, Maris HJ, Tauc J, Hauser JJ (1984) Coherent phonon generation and detection by picosecond light pulses. Phys Rev Lett 53:989–992CrossRefGoogle Scholar
  28. 28.
    Thomsen C, Grahn HT, Maris HJ, Tauc J (1986) Surface generation and detection of phonons by picosecond light pulses. Phys Rev B 34:4129–4138CrossRefGoogle Scholar
  29. 29.
    Madelung O, Rossler U, Schulz M (1999) Magnesium oxide (MgO) crystal structure, lattice parameters, thermal expansion. In: Landolt-Börnstein—Group III Condensed matter. Springer, BerlinGoogle Scholar
  30. 30.
    Luxon JT, Montgomery DJ, Summitt R (1969) Effect of particle size and shape on the infrared absorption of magnesium oxides powders. Phys Rev 188:1345–1356CrossRefGoogle Scholar
  31. 31.
    Schmidt Aaron J, Chen Xiaoyuan, Chen Gang (2008) Pulse accumulation, radial heat conduction, and anisotropic thermal conductivity in pump-probe transient thermoreflectanc. Rev Sci Instrum 79(11):114902CrossRefGoogle Scholar
  32. 32.
    Cahill DG (2004) Analysis of heat flow in layered structures for time-domain thermoreflectance. Rev Sci Instrum 75(12):5119–5122CrossRefGoogle Scholar
  33. 33.
    Cahill DG, Goodson K, Majumdar A (2002) J Heat Transf 124(2):223–241CrossRefGoogle Scholar
  34. 34.
    Hopkins Patrick E, Serrano Justin R, Phinney Leslie M, Kearney Sean P, Grasser Thomas W, Thomas Harris C (2010) Criteria for cross-plane dominated thermal transport in multilayer thin film systems during modulated laser heating. J Heat Transf 132(8):081302CrossRefGoogle Scholar
  35. 35.
    Touloukian YS, Buyco EH (1970) Thermophysical properties of matter—specific heat: metallic elements and alloys. IFI/Plenum, New YorkGoogle Scholar
  36. 36.
    Parks GS, Kelley KK (1926) The heat capacities of some metallic oxides. J Phys Chem 30:47–55CrossRefGoogle Scholar
  37. 37.
    Touloukian Y, Powell R, Ho C, Klemens P (1970) Thermophysical properties of matter—specific heat: nonmetallic solids. IFI/Plenum, New YorkGoogle Scholar
  38. 38.
    Giri A, Niemelä J-P, Tynell T, Gaskins JT, Donovan BF, Karppinen M, Hopkins PE (2016) Heat-transport mechanisms in molecular building blocks of inorganic/organic hybrid superlattices. Phys Rev B 93:115310CrossRefGoogle Scholar
  39. 39.
    Touloukian YS, Powell RW, Ho CY, Klemens PG (1970) Thermophysical properties of matter—Thermal conductivity: nonmetallic solids. IFI/Plenum, New YorkGoogle Scholar
  40. 40.
    Cahill DG, Watson SK, Pohl RO (1992) Lower limit to the thermal conductivity of disordered crystals. Phys Rev B 46:6131–6140CrossRefGoogle Scholar
  41. 41.
    Sangster MJL, Peckham G, Saunderson DH (1970) Lattice dynamics of magnesium oxide. J Phys C 3:1026–1036CrossRefGoogle Scholar
  42. 42.
    Morelli DT, Slack GA (2006) High lattice thermal conductivity solids. In: Shinde SL, Goela JS (eds) High thermal conductivity materials. Springer, NewYork, pp 37–68CrossRefGoogle Scholar
  43. 43.
    Gorham CS, Hattar K, Cheaito R, Duda JC, Gaskins JT, Beechem TE, Ihlefeld JF, Biedermann LB, Piekos ES, Medlin DL, Hopkins PE (2014) Ion irradiation of the native oxide/silicon surface increases the thermal boundary conductance across aluminum/silicon interfaces. Phys Rev B 90:024301CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Kelsey E. Meyer
    • 1
  • Ramez Cheaito
    • 1
    • 2
  • Elizabeth Paisley
    • 3
  • Christopher T. Shelton
    • 4
  • Jeffrey L. Braun
    • 1
  • Jon-Paul Maria
    • 4
  • Jon F. Ihlefeld
    • 3
  • Patrick E. Hopkins
    • 1
  1. 1.Department of Mechanical and Aerospace EngineeringUniversity of VirginiaCharlottesvilleUSA
  2. 2.Department of Mechanical EngineeringStanford UniversityStanfordUSA
  3. 3.Electronic, Optical, and Nano Materials DepartmentSandia National LaboratoriesAlbuquerqueUSA
  4. 4.Department of Materials Science and EngineeringNorth Carolina State UniversityRaleighUSA

Personalised recommendations