[1] 王成君, 胡北辰, 杨晓东, 等. 3D集成晶圆键合装备现状及研究进展[J]. 电子工艺技术, 2022, 43(2): 63-67. [2] 田芳. 晶圆叠层3D封装中晶圆键合技术的应用[J]. 电子工业专用设备, 2013, 42(1): 5-7. [3] 赵国强, 赵毅. 晶圆级集成技术研究进展[J]. 功能材料与器件学报, 2023, 29(1): 12-21. [4] 刘逸群, 张宏伟, 戴风伟. 面向三维集成应用的Cu/SiO2晶圆级混合键合技术研究进展[J]. 微电子学, 2022, 52(4): 623-634. [5] FORSBERG F, SAHARIL F, HARALDSSON T, et al. A comparative study of the bonding energy in adhesive wafer bonding[J]. Journal of Micromechanics and Microengineering, 2013, 23(8): 085019. [6] YAN D J, MA L L, LU J Q, et al. Advances of welding technology of glass for electrical applications[J]. Materials, 2025, 18(17): 4096. [7] REICHE M, G?SELE U. Direct wafer bonding[J]. Handbook of Wafer Bonding, 2012: 81-100. [8] RAMM P, LU J J, TAKLO M M V. Handbook of wafer bonding[M]. Hoboken: Wiley, 2012. [9] ZHAO G Q, ZENG Y P, ZHAO Y. Simulation and experimental analysis of thermomechanical stress around interconnects for W2W hybrid bonding[C]// 2024 IEEE 10th Electronics System-Integration Technology Conference (ESTC), Berlin, Germany, 2024: 1-6. [10] LAU J H. Recent advances and trends in Cu–Cu hybrid bonding[J]. IEEE Transactions on Components, Packaging and Manufacturing Technology, 2023, 13(3): 399-425. [11] AONO T, KAZAMA A, OKADA R, et al. Eutectic-based wafer-level-packaging technique for piezoresistive MEMS accelerometers and bond characterization using molecular dynamics simulations[J]. Journal of Micromechanics and Microengineering, 2018, 28(3): 035004. [12] KHAN A A, NGUYEN T K, TRINH Q T, et al. Wafer bonding technologies for microelectromechanical systems and 3D ICs: advances, challenges, and trends[J]. Advanced Engineering Materials, 2025, 27(20): 2500342. [13] LE X B, CHOA S H. Assessment of the risk of crack formation at a hybrid bonding interface using numerical analysis[J]. Micromachines, 2024, 15(11): 1332. [14] JIANG H, XU Y H, RAMACHANDRAN S, et al. Grain morphology effect on interfacial void closure in Cu-Cu bonding for advanced semiconductor packaging[J]. Microelectronics Reliability, 2025, 173: 115864. [15] LIM K, HAN M, JO G, et al. Design and simulation of symmetric wafer-to-wafer bonding compesating a gravity effect[C]// 2020 IEEE 70th Electronic Components and Technology Conference (ECTC), Orlando, FL, USA, 2020: 1480-1485. [16] REN Y Q, WANG Y X, LIN B H, et al. Al-Cu wafer-level bonding microscopic mechanism and its strength study[J]. Japanese Journal of Applied Physics, 2025, 64(6): 065503. [17] WU C D, LIAO C F. Molecular dynamics simulation of the direct bonding of (111)-oriented nanotwinned cu and its related mechanical behavior[J]. Journal of Physics and Chemistry of Solids, 2024, 187: 111872. [18] 张丹青, 韩易, 商庆杰, 等. 有限元仿真优化布局解决金金键合局域化问题[J]. 电子与封装, 2024, 24(4): 040202. [19] CHU W S, RASHIDI S E E, ZHANG Y L, et al. An analytical model for thin film pattern-dependent asymmetric wafer warpage prediction[C]// 2022 IEEE International Memory Workshop (IMW), Dresden, Germany, 2022: 1-4. [20] 谭琳, 王谦, 郑凯, 等. 三维集成堆叠结构的晶圆级翘曲仿真及应用[J]. 电子与封装, 2024, 24(4): 040201. [21] KIM T H, AHN D, LEE M G, et al. Development of wafer bonding system for high precision bonding alignment[J]. International Journal of Precision Engineering and Manufacturing, 2024, 25(9): 1823-1841. [22] OUYANG Y, YANG S H, YIN D D, et al. Excellent reliability of xtacking? bonding interface[C]// 2021 IEEE International Reliability Physics Symposium (IRPS), Monterey, CA, USA, 2021: 1-6. [23] 甘磊, 吴昊, 仲政. 数据驱动的金属疲劳寿命模型研究进展[J]. 力学进展, 2025, 55(1): 30-79. [24] ROSHANGHIAS A, KACZYNSKI J, HANGEN U. Cu pumping analysis during Cu/SiO2 hybrid bonding using in-situ SPM imaging[J]. IMAPSource Proceedings, 2023, 2023: 302-305. [25] MORICEAU H, RIEUTORD F, FOURNEL F, et al. Low temperature direct bonding: an attractive technique for heterostructures build-up[J]. Microelectronics Reliability, 2012, 52(2): 331-341. [26] TONG Q Y, LEE T H, G?SELE U, et al. The role of surface chemistry in bonding of standard silicon wafers[J]. Journal of the Electrochemical Society, 1997, 144(1): 384-389. [27] LITTON D A, GAROFALINI S H. Modeling of hydrophilic wafer bonding by molecular dynamics simulations[J]. Journal of Applied Physics, 2001, 89(11): 6013-6023. [28] WU C D, LIAO C F. Atomistic simulations of effects of nanostructure on bonding mechanism and mechanical response of direct bonding of (111)-oriented nanotwinned Cu[J]. Journal of Applied Physics, 2024, 136(5): 054501. [29] LIU S C, ZHAO S, ZHANG D L, et al. Molecular dynamics analysis of the solid-state bonding mechanism and high strain rate response for (1 1 1)-oriented nanotwinned silver[J]. ACS Applied Materials & Interfaces, 2025, 17(15): 23308-23321. [30] KIM T H, HOWLADER M M R, ITOH T, et al. Room temperature Cu-Cu direct bonding using surface activated bonding method[J]. Journal of Vacuum Science & Technology A: Vacuum, Surfaces, and Films, 2003, 21(2): 449-453. [31] KAGAWA Y, FUJII N, AOYAGI K, et al. Novel stacked CMOS image sensor with advanced Cu2Cu hybrid bonding[C]// 2016 IEEE International Electron Devices Meeting (IEDM), San Francisco, CA, USA, 2016. [32] CHOI S, CHEN C M, HWANG B. Cu-Cu mechanical bonding for 3D integration of the next generation electronic chips: interfacial mechanisms, surface engineering, and emerging low-temperature strategies[J]. Facta Universitatis, Series: Mechanical Engineering, 2025, 23(4): 921. [33] TATSUMI H, KAO C R, NISHIKAWA H. Impact of crystalline orientation on Cu-Cu solid-state bonding behavior by molecular dynamics simulations[J]. Scientific Reports, 2023, 13: 23030. [34] SATO T, KUBOTA A, SAITOH K I, et al. Molecular dynamics study on SiO2 interfaces of nonfiring solids[J]. Journal of Nanomaterials, 2020, 2020(1): 8857101. [35] WHALEY S D. Nano-bonding of silicon oxides-based surfaces at low temperature: bonding interphase modeling via molecular dynamics and characterization of bonding surfaces topography, hydro-affinity and free energy[D]. Tempe: Arizona State University, 2017. [36] TIAN Z Q, WANG S Z, LI R, et al. Study on polishing mechanisms of BEOL metal interconnects based on chemical and mechanical synergy[J]. Microsystems & Nanoengineering, 2025, 11: 109. [37] LI P, CHEN Z H, YAO P, et al. First-principles study of defects in amorphous-SiO2/Si interfaces[J]. Applied Surface Science, 2019, 483: 231-240. [38] SUN J W, REMSING R C, ZHANG Y B, et al. Accurate first-principles structures and energies of diversely bonded systems from an efficient density functional[J]. Nature Chemistry, 2016, 8(9): 831-836. [39] PELMENSCHIKOV A, STRANDH H, PETTERSSON L G M, et al. Lattice resistance to hydrolysis of Si–O–Si bonds of silicate minerals: ab initio calculations of a single water attack onto the (001) and (111) β-cristobalite surfaces[J]. The Journal of Physical Chemistry B, 2000, 104(24): 5779-5783. [40] YANG J J, MENG S, XU L F, et al. Water adsorption on hydroxylated silica surfaces studied using the density functional theory[J]. Physical Review B, 2005, 71(3): 035413. [41] BARNETTE A L, ASAY D B, KIM D, et al. Experimental and density functional theory study of the tribochemical wear behavior of SiO2 in humid and alcohol vapor environments[J]. Langmuir, 2009, 25(22): 13052-13061. [42] HOWLADER M M R, SUGA T, ITOH H, et al. Role of heating on plasma-activated silicon wafers bonding[J]. Journal of the Electrochemical Society, 2009, 156(11): H846. [43] MASTEIKA V, KOWAL J, BRAITHWAITE N St J, et al. A review of hydrophilic silicon wafer bonding[J]. ECS Journal of Solid State Science and Technology, 2014, 3(4): Q42-Q54. [44] NAGAO K, NEATON J B, ASHCROFT N W. First-principles study of adhesion at Cu/SiO2 interfaces[J]. Physical Review B, 2003, 68(12): 125403. [45] YU W, CHENG S C, LI Z Y, et al. The application of multi-scale simulation in advanced electronic packaging[J]. Fundamental Research, 2024, 4(6): 1442-1454. [46] CHEN C I, WU S C, LIU D S, et al. Global-to-local modeling and experiment investigation of HFCBGA package board-level solder joint reliability[J]. Journal of Microelectronics and Electronic Packaging, 2007, 4(4): 186-194. [47] CHANG S, LIU K, YANG M, et al. Theory and implementation of sub-model method in finite element analysis[J]. Heliyon, 2022, 8(11): e11427. [48] GUEDES J, KIKUCHI N. Preprocessing and postprocessing for materials based on the homogenization method with adaptive finite element methods[J]. Computer Methods in Applied Mechanics and Engineering, 1990, 83(2): 143-198. [49] MENANTEAU L, PANTALé O, CAPERAA S. A methodology for large scale finite element models, including multi-physic, multi-domain and multi-timestep aspects[J]. European Journal of Computational Mechanics, 2006, 15(7/8): 799-824. [50] FISH J. Multiscale methods: bridging the scales in science and engineering[M]. Oxford: Oxford University Press, 2010. [51] MILLER R E, TADMOR E B. The quasicontinuum method: overview, applications and current directions[J]. Journal of Computer-Aided Materials Design, 2002, 9(3): 203-239. [52] PENG Q, ZHANG X, HUNG L, et al. Quantum simulation of materials at micron scales and beyond[J]. Physical Review B, 2008, 78(5): 054118. [53] PENG Q, LU G. A comparative study of fracture in Al: Quantum mechanical vs. empirical atomistic description[J]. Journal of the Mechanics and Physics of Solids, 2011, 59(4): 775-786. [54] LI H, WANG Z, ZOU N L, et al. Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation[J]. Nature Computational Science, 2022, 2(6): 367-377. [55] ZHANG L F, HAN J Q, WANG H, et al. Deep potential molecular dynamics: a scalable model with the accuracy of quantum mechanics[J]. Physical Review Letters, 2018, 120(14): 143001. [56] DONG H K, SHI Y B, YING P H, et al. Molecular dynamics simulations of heat transport using machine-learned potentials: a mini-review and tutorial on GPUMD with neuroevolution potentials[J]. Journal of Applied Physics, 2024, 135(16): 161101. [57] FAN Z Y, WANG Y Z, YING P H, et al. GPUMD: a package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations[J]. The Journal of Chemical Physics, 2022, 157(11): 114801. [58] XU K, WANG G, LIANG T, et al. Device-scale atomistic simulations of heat transport in advanced field-effect transistors[J]. arXiv, 2025. [59] LIU Z, SHAN G B, CHEN Z Y, et al. Physics-guided neural surrogate model with particle swarm-based multi-objective optimization for quasi-coaxial TSV interconnect design[J]. Micromachines, 2025, 16(10): 1134. [60] JIANG Z L, WANG Z Q. Adaptive machine learning-enabled evolutionary optimization for reliability-based design of through silicon via (TSV) structures under uncertainty[J]. IEEE Transactions on Components, Packaging and Manufacturing Technology, 2025, 15(2): 387-398. [61] RAISSI M, PERDIKARIS P, KARNIADAKIS G E. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations[J]. Journal of Computational Physics, 2019, 378: 686-707. [62] HAGHIGHAT E, RAISSI M, MOURE A, et al. A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics[J]. Computer Methods in Applied Mechanics and Engineering, 2021, 379: 113741. [63] HARANDI A, MOEINEDDIN A, KALISKE M, et al. Mixed formulation of physics-informed neural networks for thermo-mechanically coupled systems and heterogeneous domains[J]. International Journal for Numerical Methods in Engineering, 2024, 125(4): e7388. [64] YAMAGUCHI T, OKUDA H. Prediction of stress concentration at fillets using a neural network for efficient finite element analysis[J]. Mechanical Engineering Letters, 2020(6): 20-318. [65] SHAN Y C, CAO L T, WANG Y, et al. AI-driven generative and reinforcement learning for mechanical optimization of 2D patterned hollow structures[J]. Materials Futures, 2025, 4(3): 035001.
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