Physical feature-based machine learning of BEOL thermal conductivity in 3D ICs
文献类型：期刊论文
| 作者 | Liu, Yunting1,2,3; Fu, Rong1,2,3; Zhu, Jixiang4; Zhang, Kun4; Chen, Chuan1,2,3; Li, Jun1,2,3; Cao, Liqiang1,2,3 |
| 刊名 | MICROELECTRONICS JOURNAL
 |
| 出版日期 | 2026-02-01 |
| 卷号 | 168页码:11 |
| 关键词 | 3D integrated circuits Advanced back-end-of-line interconnects Anisotropic thermal conductivity Thermal management |
| ISSN号 | 0959-8324 |
| DOI | 10.1016/j.mejo.2025.107024 |
| 英文摘要 | Accurate prediction of effective thermal conductivity in back-end-of-line (BEOL) stacks is crucial for 3D memory-on-logic design. We propose a physics-informed machine-learning framework that converts GDSII layouts into representative volume elements (RVEs), labels their directional conductivities using a matrix-based finite-volume solver with preconditioned conjugate gradients (PCG), and learns topology-conductivity mappings via a 50-dimensional hybrid embedding (HYB) combining 25 physics-informed (PH) descriptors and 25 principal components (PCs). The PCG solver reproduces finite-element results within 4 % deviation while reducing labeling time by over 90 %. Across independent validation regions, LS-Boost achieves approximate to 5 % mean directional MAPE and Ridge <10 %, with PH features showing dominant importance over PCs. The trained models generalize well to unseen layouts and modules, maintaining anisotropic trends and spatial fidelity. Full-window inference over a 540 x 540 mu m(2) BEOL block completes within minutes, yielding interpretable, direction-resolved conductivity maps for fast and physically consistent thermal analysis of stacked systems. |
| 资助项目 | National Natural Science Foundation of China[92373116] ; Youth Innovation Promotion Association, Chinese Academy of Sciences[2023126] |
| WOS研究方向 | Engineering ; Science & Technology - Other Topics |
| 语种 | 英语 |
| WOS记录号 | WOS:001647992700002 |
| 出版者 | ELSEVIER SCI LTD |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/42963]  |
| 专题 | 中国科学院计算技术研究所 |
| 通讯作者 | Chen, Chuan; Li, Jun |
| 作者单位 | 1.Chinese Acad Sci, Inst Microelect, Microsyst Packaging Res Ctr, Beijing 100029, Peoples R China 2.Univ Chinese Acad Sci, Sch Integrated Circuits, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Key Lab Fabricat Technol Integrated Circuits, Beijing 100029, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Res Ctr Intelligent Comp Syst, SKLP, Beijing 100029, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Yunting,Fu, Rong,Zhu, Jixiang,et al. Physical feature-based machine learning of BEOL thermal conductivity in 3D ICs[J]. MICROELECTRONICS JOURNAL,2026,168:11. |
| APA | Liu, Yunting.,Fu, Rong.,Zhu, Jixiang.,Zhang, Kun.,Chen, Chuan.,...&Cao, Liqiang.(2026).Physical feature-based machine learning of BEOL thermal conductivity in 3D ICs.MICROELECTRONICS JOURNAL,168,11. |
| MLA | Liu, Yunting,et al."Physical feature-based machine learning of BEOL thermal conductivity in 3D ICs".MICROELECTRONICS JOURNAL 168(2026):11. |
入库方式： OAI收割
来源：计算技术研究所
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