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説明:

Efficient heat dissipation is crucial for electronics. Interfacial thermal resistance (ITR) poses significant challenges, requiring innovative solutions. Machine learning enhances ITR predictions by analyzing large datasets. Inorganic, amorphous, and 2D materials offer advanced thermal management. Future research could benefit from improved data quality and hybrid models to further optimize next-generation electronic devices.

権利情報:

• Creative Commons BY Attribution 4.0 International
  

キーワード: Interfacial thermal resistance, thermal management, electronics, machine learning

刊行年月日: 2024-07-17

出版者: Springer Science and Business Media LLC

掲載誌:

• Nature Reviews Electrical Engineering (ISSN: 29481201) vol. 1 issue. 8 p. 489-490

研究助成金:

原稿種別: 査読前原稿 (Author's original)

MDR DOI: https://doi.org/10.48505/nims.4696

公開URL: https://doi.org/10.1038/s44287-024-00077-y

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更新時刻: 2024-08-27 16:30:21 +0900

MDRでの公開時刻: 2024-08-27 16:30:22 +0900