Page 24 - IJAMD-2-1
P. 24

International Journal of AI for
            Materials and Design
                                                                             Predicting thermal conductivity of sintered Ag


            Investigation: Jiahui Wei, Daowei Wu, Yuting Zhang, Kui      doi: 10.1007/s11664-019-06984-3
               Li, Fei Qin                                     7.   Qin F, Hu Y, Dai Y, An T, Chen P. Evaluation of thermal
            Methodology: Yanwei Dai, Libo Zhao, Jiahui Wei        conductivity for  sintered  silver  considering aging  effect
            Writing – original draft: Libo Zhao, Jiahui Wei, Yanwei Dai   with microstructure based model.  Microelectron Reliab.
            Writing – review & editing: Libo Zhao, Yanwei Dai, Jiahui   2020;108:113633.
               Wei                                                doi: 10.1016/j.microrel.2020.113633

            Ethics approval and consent to participate         8.   Qin F, Hu Y, Dai Y,  et al. Crack effect on the equivalent
                                                                  thermal conductivity of porously sintered silver. J Electron
            Not applicable.                                       Mater. 2020;49:5994-6008.

            Consent for publication                               doi: 10.1007/s11664-020-08325-1
                                                               9.   Qin F., Zhao S, Dai Y, Hu Y, An T, Gong Y. Mud-cracking
            Not applicable.                                       effect of  sintered  silver  layer  on  quantifying  heat  transfer

            Availability of data                                  behavior  of  SiC  devices  under  power  cycling:  Voronoi
                                                                  tessellation model.  IEEE Trans Compon Packag Manuf
            The data presented in this study are available upon request   Technol. 2022;12(6):964-972.
            from the corresponding author due to data protection.     doi: 10.1109/TCPMT.2022.3178226
            References                                         10.  Kim YJ, Park BH, Hyun SK, Nishikawa H. The influence
                                                                  of porosity and pore shape on the thermal conductivity of
            1.   Herboth T, Guenther M, Fix A, Wilde J. Failure Mechanisms   silver sintered joint for die attach. Mater Today Commun.
               of Sintered Silver  Interconnections for  Power  Electronic   2021;29:102772.
               Applications. In:  IEEE  63   Electronic  Components  and      doi: 10.1016/j.mtcomm.2021.102772
                                    rd
               Technology Conference. Las Vegas, NV, USA; 2013.
               p. 1621-1627.                                   11.  Chen H, Du Z, Li X, Zhou H, Liu Z. Identification of pipe
                                                                  inner surface in heat conduction problems by deep learning
               doi: 10.1109/ECTC.2013.6575789                     and effective thermal conductivity transform. Eng Comput.
            2.   Ordonez-Miranda J, Hermens M, Nikitin I, Kouznetsova VG,   2020;37(9):3505-3523.
               van der Sluis O, Ras MA, Volz S. Measurement and modeling      doi: 10.1108/EC-01-2020-0012
               of the effective thermal conductivity of sintered silver pastes.
               Int J Therm Sci. 2016;108:185-194.              12.  Huang Q, Hong D, Niu B, Long D, Zhang Y. An interpretable
                                                                  deep  learning strategy  for  effective thermal  conductivity
               doi: 10.1016/j.ijthermalsci.2016.05.014            prediction of  porous  materials.  Int J Heat Mass Transfer.

            3.   Signor L, Kumar P, Tressou B,  et al. Evolution of the   2024;221:125064.
               thermal conductivity of Sintered silver joints with their      doi: 10.1016/j.ijheatmasstransfer.2023.125064
               porosity predicted by the finite element analysis of real 3D   13.  Qin  G,  Wei  Y,  Yu  L,  et al.  Predicting  lattice  thermal
               microstructures. J Electron Mater. 2018;47:4170-4176.
                                                                  conductivity from fundamental material properties
               doi: 10.1007/s11664-018-6253-2.                    using machine learning techniques.  J  Mater Chem A.
                                                                  2023;11(11):5801-5810.
            4.   Sghuri A, Billaud Y, Signor L, Saury D, Milhet X.
               Experimental investigation of thermal conductivity      doi: 10.1039/D2TA08721A
               during aging of nanoporous sintered silver.  Acta Mater.   14.  Li RY, Lee E, Luo TF. A unified deep neural network potential
               2023;257:119109.                                   capable of predicting thermal conductivity of silicon in
               doi: 10.1016/j.actamat.2023.119109                 different phases. Mater Today Phys. 2020;12:100181.
            5.   Hu X, Martin HA, Poelma R, et al. Exploring the process-     doi: 10.1016/j.mtphys.2020.100181
               microstructure-thermal  properties  relationship  of  15.  Yang ZH, Wu XX, He XD, Guan XF. A  multiscale
               resin-reinforced Ag sintering material for high-power   analysis-assisted two-stage  reduced-order  deep learning
               applications via 3D FIB-SEM nanotomography. Mater Des.   approach for effective thermal conductivity of arbitrary
               2024;244:113185.                                   contrast heterogeneous materials.  Eng Appl Artif Intell.
               doi: 10.1016/j.matdes.2024.113185                  2024;136:108916.
            6.   Zhao Z, Zhang H, Zou G,  et al.  A  predictive model for      doi: 10.1016/j.engappai. 2024.108916
               thermal conductivity of nano-Ag sintered interconnect for   16.  Kim TH, Park JH, Jung KW, Kim J, Lee EH. Application
               a SiC die. J Electron Mater. 2019;48:2811-2825.    of  convolutional  neural  network  to predict  anisotropic



            Volume 2 Issue 1 (2025)                         18                             doi: 10.36922/ijamd.5744
   19   20   21   22   23   24   25   26   27   28   29