- Address
- 305-0047 茨城県つくば市千現1-2-1 [アクセス]
研究内容
- Keywords
thermal management, machine learning, materials informatics, thin film, carrier conductivity
thermal management, data mining, machine learning
出版物2004年以降のNIMS所属における研究成果や出版物を表示しています。
論文
- Tianzhuo Zhan, Mao Xu, Zhi Cao, Chong Zheng, Hiroki Kurita, Fumio Narita, Yen-Ju Wu, Yibin Xu, Haidong Wang, Mengjie Song, Wei Wang, Yanguang Zhou, Xuqing Liu, Yu Shi, Yu Jia, Sujun Guan, Tatsuro Hanajiri, Toru Maekawa, Akitoshi Okino, Takanobu Watanabe. Effects of Thermal Boundary Resistance on Thermal Management of Gallium-Nitride-Based Semiconductor Devices: A Review. Micromachines. 14 [11] (2023) 2076 10.3390/mi14112076 Open Access
- Kuen-Chan Lee, Jen-Hsien Huang, Yen-Ju Wu, Kuan-Syun Wang, Er-Chieh Cho, Shih-Chieh Hsu, Ting-Yu Liu. Crystal structure-controlled synthesis of NiMoO4/NiO hierarchical microspheres for high-performance supercapacitors and photocatalysts. Journal of Energy Storage. 97 (2024) 112639 10.1016/j.est.2024.112639 Open Access
- Yin-Pu Huang, Bo-Rui Wu, Soumava Ghosh, Yue-Tong Jheng, Ya-Lun Ho, Yen-Ju Wu, Attaporn Wisessint, Munho Kim, Guo-En Chang. Mid-infrared silicon photonic lasers based on GeSn slab waveguide on silicon. Optics Express. 32 [22] (2024) 39560-39569 10.1364/oe.540223 Open Access
会議録
- WU, Yen-Ju, Yann-Wen LAN, Shih-Chieh HSU, XU, Yibin. Tuning thermal conductance across metal/MoS2 monolayer interface through N-methyl-2-pyrrolidone wet cleaning. Thermophysical Properties 41:The 41th Japan Symposium 2020. (2019) A142
口頭発表
- ウー イェン ルー, 徐 一斌, Zhan, 方 蕾. マテリアルズ・インフォマティクスによるナノ多層系の伝熱制御. European Conference on Thermophysical Properties 2017. 2017
- ウー イェン ルー, 徐 一斌, Zhan, 方 蕾. 界面熱抵抗の予測および設計のための記述子について. IUMRS-ICAM 2017. 2017
その他の文献
- Yen-Ju WU, Yibin XU. Design of transparent thermal insulating thin films of nanoscale-layered oxides. Thermophysical Properties 42:The 42th Japan Symposium 2021. (2021) 1496
- Yen-Ju Wu. Revolutionizing electronics with advanced interfacial heat management. Nature Reviews Electrical Engineering. 1 [8] (2024) 489-490 10.1038/s44287-024-00077-y Open Access
- WU, Yen-Ju, Takashi YAGI, XU, Yibin. Interfacial Thermal Resistance of Metal-nonmetal Interfaces under Bidirectional Heat Fluxes. Thermophysical Properties 43:The 43th Japan Symposium 2022. (2022) . B323 [OS3-V ナノスケール熱物性の評価 5 ] (2022) B323-1-B323-3
所属学会
日本熱物性学会, 日本熱物性学会
受賞履歴
- 2018年日本熱物性学会賞 (奨励賞) (2018)
マテリアル基盤研究センター
Advancing interfacial thermal resistance prediction for thermal management in heterogeneous materials
Interfacial thermal resistance, Thermal management, Predictive modeling, Heterogeneous materials, Machine learning
概要
This research aims to address the challenges associated with interfacial thermal resistance (ITR) and develop predictive models for efficient thermal management in various applications. The background that led to this study is the increasing need for accurate prediction and control of ITR in interfaces between dissimilar materials. In many practical scenarios, such as microelectronics and energy conversion systems, precise understanding and management of ITR are essential for optimal thermal behavior. To fulfill these needs, this study focuses on advancing our understanding of ITR mechanisms and developing predictive models for different material systems.
新規性・独創性
● This research combines machine learning and comprehensive descriptors to understand and predict ITR.
● Advanced Predictive Models: The study achieves a remarkable 96% accuracy with highly accurate predictive models for ITR, surpassing previous limitations.
● Exploration of Asymmetric ITR: The research uncovers the novel phenomenon of asymmetric ITR, revealing the interplay between interfacial electron-phonon couplings and temperature differences.
● Tunability of Thermal Conductance through interfacial chemical modifications.
● Efficient thermal management solutions in Thermal Insulators and Microelectronics
内容
Our research focuses on advancing ITR prediction and control in heterogeneous materials for optimal thermal management. Through innovative machine learning techniques and comprehensive descriptors, we have achieved 96% accuracy in predicting ITR. We can develop ultra-low thermal conductivity thermal insulators and materials with tunable thermal and electrical conductance. This research meets the growing demand for efficient thermal management in industries like electronics, automotive, and energy. Going forward, we aim to collaborate with industry partners to apply our findings and develop cutting-edge thermal management technologies, enhanced insulating materials, and optimized microelectronic devices. This research opens up new possibilities for improved thermal management in diverse industries.
まとめ
● Achievements: Significant advancements have been made in predicting and controlling ITR in heterogeneous materials, achieving a high accuracy rate of 96%.
● Potential Applications: The research holds promise for practical applications in fields such as microelectronics, energy systems, and thermal management.
● Future Goals: The focus will be on advancing thermal management technologies, optimizing thermal insulating materials, and enhancing microelectronic device performance.
