Measurement of Interfacial Thermal Resistance by Periodic Heating and a Thermo-Reflectance Technique

Xu, Yibin; Wang, Haitao; Tanaka, Yoshihisa; Shimono, Masato; Yamazaki, Masayoshi

doi:10.2320/matertrans.48.148

Cited by 40 publications

(21 citation statements)

References 10 publications

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“…Similar to prior reported graphene interfaces and metal/dielectrics interfaces (including our data in Figure b), we observe a weak dependence of thermal conductance on temperature. The temperature dependence of the interfacial thermal conductance G could be used to differentiate whether phonons or electrons are the dominant heat carriers across the Pd/graphene interfaces.…”

Section: Resultssupporting

confidence: 91%

Negligible Electronic Contribution to Heat Transfer across Intrinsic Metal/Graphene Interfaces

Huang

Koh

2017

Adv Materials Inter

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Despite the importance of high thermal conductance (i.e., low thermal resistance) of metal contacts to thermal management of graphene devices, prior reported thermal conductance (G) of metal/graphene interfaces are all relatively low, only 20-40 MW m -2 K -1 . One possible route to improve the thermal conductance of metal/graphene interfaces is through additional heat conduction by electrons, since graphene can be easily doped by metals. In this paper, we evaluate the electronic heat conduction across metal/graphene interfaces by measuring the thermal conductance of Pd/transferred graphene (trG)/Pd interfaces, prepared by either thermal evaporation or radio-frequency (rf) magnetron sputtering, over a wide temperature range of 80 ≤ T ≤ 500 K. We find that for the samples prepared by thermal evaporation, the thermal conductance of Pd/trG/Pd is 42 MW m -2 K -1 . The thermal conductance only weakly depends on temperature, which suggests that heat is predominantly carried by phonons across the intrinsic Pd/graphene interface. However, for Pd/trG/Pd samples with the top Pd films deposited by rf magnetron sputtering, we observe a significant increment of thermal conductance from the intrinsic value of 42 MW m -2 K -1 to 300 MW m -2 K -1 , and G is roughly proportional to T. We attribute the enhancement of thermal conductance to an additional channel of heat transport by electrons via atomic-scale pinholes formed in the graphene during the sputtering process. We

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Section: Resultssupporting

confidence: 91%

Negligible Electronic Contribution to Heat Transfer across Intrinsic Metal/Graphene Interfaces

Huang

Koh

2017

Adv Materials Inter

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“…We measured the ITR of Bi/Si interface by frequency-domain thermoreflectance 9 as 51.8 ± 4.5 (10 −9 m 2 K/W), which is in the range of the predicted ITR of 50.68-61.13 (10 −9 m 2 K/W) as shown in Fig. 6.…”

Section: Itr Predictionmentioning

confidence: 69%

Predicting interfacial thermal resistance by machine learning

Fang

2019

npj Comput Mater

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108

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Various factors affect the interfacial thermal resistance (ITR) between two materials, making ITR prediction a high-dimensional mathematical problem. Machine learning is a cost-effective method to address this. Here, we report ITR predictive models based on experimental data. The physical, chemical, and material properties of ITR are categorized into three sets of descriptors, and three algorithms are used for the models. Those descriptors assist the models in reducing the mismatch between predicted and experimental values and reaching high predictive performance of 96%. Over 80,000 material systems composed of 293 materials were inputs for predictions. Among the top-100 high-ITR predictions by the three different algorithms, 25 material systems are repeatedly predicted by at least two algorithms. One of the 25 material systems, Bi/Si achieved the ultra-low thermal conductivity in our previous work. We believe that the predicted high-ITR material systems are potential candidates for thermoelectric applications. This study proposed a strategy for material exploration for thermal management by means of machine learning.

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“…at a temperature of 300 K, 9,10 which is low compared to other metal-dielectric interfaces 10,11 . The Kapitza length, defined as κ/G , represents the thickness of material with the thermal conductivity κ that has equivalent resistance to the interface.…”

Section: Introductionmentioning

confidence: 90%

Enhancement of Thermal Conductance at Metal-Dielectric Interfaces Using Subnanometer Metal Adhesion Layers

et al. 2016

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We show that the use of sub-nm adhesion layers significantly enhances the thermal interface conductance at metal-dielectric interfaces. A metal-dielectric interface between Au and sapphire (Al 2 O 3 ) was considered using Cu (low optical loss) and Cr (high optical loss) as adhesion layers. To enable high throughput measurements each adhesion layer was deposited as a wedge such that a continuous range of thickness could be sampled. Our measurements of thermal interface conductance at the metal-Al 2 O 3 interface made using frequency domain thermoreflectance show that a 1 nm thick adhesion layer of Cu or Cr is sufficient to enhance the thermal interface conductance by more than a factor of 2 or 4, respectively, relative to the pure Au-Al 2 O 3 interface. The enhancement agrees with the Diffuse Mismatch Model-based predictions of accumulated thermal conductance versus adhesion layer thickness assuming that it contributes phonons with wavelengths less than its adhesion layer thickness, while those with longer wavelengths transmit directly from the Au.

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Measurement of Interfacial Thermal Resistance by Periodic Heating and a Thermo-Reflectance Technique

Cited by 40 publications

References 10 publications

Negligible Electronic Contribution to Heat Transfer across Intrinsic Metal/Graphene Interfaces

Negligible Electronic Contribution to Heat Transfer across Intrinsic Metal/Graphene Interfaces

Predicting interfacial thermal resistance by machine learning

Enhancement of Thermal Conductance at Metal-Dielectric Interfaces Using Subnanometer Metal Adhesion Layers

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