Current Status of Electronic Packaging Materials Using Machine Learning

doi:10.3901/JME.2023.22.222

Abstract

Abstract: With the advent of “post-Moore era”, advanced packaging technology improves the performance of chips, and traditional materials development and preparation and research of packaging interconnect are difficult to meet the needs of the era. According to the vision of industry 4.0, it is one of the future development trends to use machine learning algorithm to efficiently predict packaging material development, packaging process optimization, solder joint reliability. The process of applying machine learning in material field includes data collection, data preprocessing and parameter tuning optimization. Deep learning shines in the prediction of packaging problems. Deep learning algorithms such as convolutional neural network model have gradually been applied to prediction and achieved excellent prediction results. Although the research of machine learning-assisted electronic packaging is still in its infancy, machine learning still has great research and practical value.

Key words: machine learning, electronic packaging, material development, reliability, intermetallic compound

CLC Number:

TG156

ZHANG Shuye, DUAN Xiaokang, LUO Keyu, XU Sunwu, ZHANG Zhihao, CHEN Jieshi, HE Peng. Current Status of Electronic Packaging Materials Using Machine Learning[J]. Journal of Mechanical Engineering, 2023, 59(22): 222-233.

References

[1] WANG Q, ZHANG S, LIN T, et al, Highly mechanical and high-temperature properties of Cu-Cu joints using citrate-coated nanosized Ag paste in air[J]. Progress in Natural Science, 2021, 31(1):129.
[2] MITTAL J, LIN K. Carbon nanotube-based interconnections[J]. Journal of Materials Science, 2017, 52(2):643-662.
[3] LEE L, KANG L, KWON Y, et al, FOWLP technology as wafer level system in packaging (SiP) solution[C]//International Conference on Electronics Packaging, 19-22 April 2017, Yamagata, 2017 International Conference on Electronics Packaging, 08 June 2017, Yamagata.
[4] ZHANG S, XU X, LIN T, et al. Recent advances in nano-materials for packaging of electronic devices[J]. Journal of Materials Science:Materials in Electronics, 2019, 30(15):13855-13868.
[5] YANG M, KO Y, BANG J, et al. Growth inhibition of interfacial intermetallic compounds by pre-coating oriented Cu6Sn5grains on Cu substrates[J]. Journal of Alloys and Compounds, 2017, 701:533-541.
[6] JIN T, PAN Y, JEON G J, et al. Ultrathin nanofibrous membranes containing insulating microbeads for highly sensitive flexible pressure sensors[J]. ACS Applied Materials & Interfaces, 2020, 12(11):13348-13359.
[7] TOM M. Machine learning[M]. Maidenhead, UK:Tracepro, 1997.
[8] WANG F, ZHANG J, ZHENG, et al. Where does AlphaGo go:From church-turing thesis to AlphaGo thesis and beyond[J]. Acta Automatica Sinica, 2016, 3(2):113-120.
[9] HUANG C, HUANG H. Process optimization of SnCuNi soldering material using artificial parametric design[J]. Journal of Intelligent Manufacturing, 2014, 25(4):813-823.
[10] GUO H, ZHOU D, PANG L, et al. Microwave dielectric properties of low firing temperature stable scheelite structured (Ca,Bi)(Mo,V)O-4 solid solution ceramics for LTCC applications[J]. Journal of the European Ceramic Society, 2019, 39(7):2365-2373.
[11] LIU Y, LIU T, HUANG T, et al. Exploring dielectric constant and dissipation factor of LTCC using machine learning[J]. Materials, 2021, 14(19):5784.
[12] JIN K, LUO H, WANG Z, et al. Composition optimization of a high-performance epoxy resin based on molecular dynamics and machine learning[J]. Materials & Design, 2020, 194:108932.
[13] DOBLIES A, BOLL B, FIEDLER B, et al. Prediction of thermal exposure and mechanical behavior of epoxy resin using artificial neural networks and Fourier transform infrared spectroscopy[J]. Polymers, 2019, 11(2):363.
[14] ZHANG S, ZHU B, ZHOU X, et al. Wettability and interfacial morphology of Sn-3.0Ag-0.5Cu solder on electroless nickel plated ZnS transparent ceramic[J]. Journal of Materials Science:Materials in Electronics, 2019, 30(19):17972-17985.
[15] WANG J, CHEN J, ZHANG Z, et al. Effects of doping trace Ni element on interfacial behavior of Sn/Ni (polycrystal/single-crystal) joints[J]. Soldering & Surface Mount Technology, 2022, 34(2):124-133.
[16] ZHANG S, YANG M, WU Y, et al. A study on the optimization of anisotropic conductive films for Sn-3Ag-0.5Cu-based flex-on-board application at a 250℃ bonding temperature[J]. IEEE Transactions on Components, Packaging and Manufacturing Technology, 2018, 8(3):383-391.
[17] ZHANG S, LIN T, HE P, et al. A study on the bonding conditions and nonconductive filler contents on cationic epoxy-based Sn-58Bi solder ACFs joints for reliable flex-on-board applications[J]. IEEE Transactions on Components, Packaging and Manufacturing Technology, 2017, 7(12):2087-2094.
[18] JONATHAN S, LIMING C, SILVANA B, et al. Predicting the stability of ternary intermetallics with density functional theory and machine learning[J]. Journal of Chemical Physics, 2018(148):241728.
[19] LIU Y, PU L, YANG Y, et al. A high-entropy alloy as very low melting point solder for advanced electronic packaging[J]. Materials Today Advances, 2020(7):100101.
[20] ZHANG L, SHI J, LI H, et al. Interfacial microstructure and mechanical properties of ZrB2-SiC-C ceramic and GH99 superalloy joints brazed with a Ti-modified FeCoNiCrCu high-entropy alloy[J]. Materials & Design, 2016(97):230-238.
[21] BRIDGES D, ZHANG S, LANG S, et al, Laser brazing of a nickel-based superalloy using a Ni-Mn-Fe-Co-Cu high entropy alloy filler metal[J]. Materials Letters, 2018(215):11-14.
[22] SHEN Y, LIN C, LI J, et al. Effect of FeCoNiCrCu0.5 high-entropy-alloy substrate on Sn grain size in Sn-3.0Ag-0.5Cu solder[J]. Scientific Reports, 2019(9):3658.
[23] SHEN Y, HSIEH, CHEN S, et al. Investigation of FeCoNiCu properties:Thermal stability, corrosion behavior, wettability with Sn-3.0Ag-0.5Cu and interlayer formation of multi-element intermetallic compound[J]. Applied Surface Science, 2021(546):148931.
[24] ZHANG L, CHEN H, TAO X, et al. Machine learning reveals the importance of the formation enthalpy and atom-size difference in forming phases of high entropy alloys[J]. Materials & Design, 2020(193):108835.
[25] HUANG W, MARTIN P, ZHUANG H L. Machine learning phase prediction of high-entropy alloys[J]. Acta Materialia, 2019, 169:225-236.
[26] KRISHNA Y, JAISWAL U, RAHUL M. Machine learning approach to predict new multiphase high entropy alloys[J]. Scripta Materialia, 2021(197):113804.
[27] LIN Y, DENG W, SHIE J, et al. Optimization of reflow soldering process for BGA packages by artificial neural network[J]. Microelectronics International, 2007, 21(2):64-70.
[28] TSAI T, TSAI C. Development of a closed-loop diagnosis system for reflow soldering using neural networks and support vector regression[J]. The International Journal of Industrial Engineering:Theory, Applications and Practice, 2014, 21(1):19-33.
[29] HUANG C. Innovative parametric design for environmentally conscious adhesive dispensing process[J]. Journal of Intelligent Manufacturing, 2013, 26(1):1-12.
[30] YUN T, SIM K, KIM H, Support vector machine-based inspection of solder joints using circular illumination[J]. Electronics Letters, 2000, 36(11):949-951.
[31] MA L, XIE W, ZHANG Y, et al. Extreme learning machine based defect detection for solder joints[J]. Journal of Internet Technology, 2020, 21(5):1535-1543.
[32] GOTO K, KATO K, SAITO T, et al. Adversarial autoencoder for detecting anomalies in soldered joints on printed circuit boards[J]. Journal of Electronic Imaging, 2020, 29(4):041013-041022.
[33] TSAN T, SHIH T, FUH C. TsanKit:Artificial intelligence for solder ball head-in-pillow defect inspection[J]. Machine Vision and Applications, 2021, 32(3):66-83.
[34] SU L, WANG L, LI K, et al. Automated X-ray recognition of solder bump defects based on ensemble-ELM[J]. Science China Technological Sciences, 2019, 62(9):1512-1519.
[35] WEI W, WEI L, NIE L, et al. Using active thermography and modified SVM for intelligent diagnosis of solder bumps[J]. Infrared Physics & Technology, 2015(72):163-169.
[36] FAN M, WEI L, HE Z, et al. Defect inspection of solder bumps using the scanning acoustic microscopy and fuzzy SVM algorithm[J]. Microelectronics Reliability, 2016(65):192-197.
[37] ZHANG L, UME L, GAMALSKI J, et al. Study of flip chip solder joint cracks under temperature cycling using a laser ultrasound inspection system[J]. IEEE Transactions on Components and Packaging Technologies, 2009, 32(1):120-126.
[38] ERDAHL D, ALLEN M, UME I, et al. Structural modal analysis for detecting open solder bumps on flip chips[J]. IEEE Transactions on Advanced Packaging, 2008, 31(1):118-126.
[39] LIU S, UME I. Vibration analysis based modeling and defect recognition for flip-chip solder-joint inspection[J]. Journal of Electronic Packaging, 2002, 124(3):221-226.
[40] LI K, WANG L, WU J, et al. Using GA-SVM for defect inspection of flip chips based on vibration signals[J]. Microelectronics Reliability, 2018(81):159-166.
[41] WU H, GAO W, XU X. Solder joint recognition using mask R-CNN method[J]. IEEE Transactions on Components, Packaging, and Manufacturing Technology, 2020, 10(3):525-530.
[42] ZHOU J, XIAO X, EN Y, et al, Thermo-mechanical fatigue reliability optimization of PBGA solder joints based on ANN-PSO[J]. Journal of Central South University of Technology, 2008, 15(5):689-693.
[43] YUAN C, FAN X, ZHANG G. Solder joint reliability risk estimation by AI-assisted simulation framework with genetic algorithm to optimize the initial parameters for AI models[J]. Materials, 2021, 14(7):4835-4844.
[44] SAMAVATIAN V, FOTUHI-FIRUZABAD M. Correlation-driven machine learning for accelerated reliability assessment of solder joints in electronics[J]. Scientific Reports, 2020, 10(1):14821-14830.
[45] YUAN C, LEE C. Solder joint reliability modeling by sequential artificial neural network for glass wafer level chip scale package[J]. IEEE Access, 2020(8):143494-143501.
[46] KUNWAR A, MALLA P, SUN J, et al. Convolutional neural network model for synchrotron radiation imaging datasets to automatically detect interfacial microstructure:An in situ process monitoring tool during solar PV ribbon fabrication[J]. Solar Energy, 2021(221):230-244.
[47] ANIL K, LILI A, LIU J, et al. A data-driven framework to predict the morphology of interfacial Cu6Sn5 IMC in SAC/Cu system during laser soldering[J]. Journal of Materials Science & Technology, 2020(50):115-127.
[48] DELE-AFOLABI T, HANIM A, OIOKUPOLUYI J, et al. Interfacial IMC evolution and shear strength of MWCNTs-reinforced Sn-5Sb composite solder joints:Experimental characterization and artificial neural network modelling[J]. Journal of Materials Research and Technology-JMR&T, 2021(13):1020-1031.
[49] ANIL K, JOHAN H, SUKEHARU N, et al. Combining multi-phase field simulation with neural network analysis to unravel thermomigration accelerated growth behavior of Cu6Sn5 IMC at cold side Cu-Sn interface[J]. International Journal of Mechanical Sciences, 2020(184):105843.
[50] ANIL K, YURI C, JOHAN H, et al. Integration of machine learning with phase field method to model the electromigration induced Cu6Sn5 IMC growth at anode side Cu/Sn interface[J]. Journal of Materials Science & Technology, 2020(59):203-219.
[51] MESSINA J, LUO R, XU K, et al. Machine learning to predict aluminum segregation to magnesium grain boundaries[J]. Scripta Materialia, 2021(204):114-150.
[52] KOROLEV V, MITROFANOV A, KUCHERINENKO Y, et al. Accelerated modeling of interfacial phases in the Ni-Bi system with machine learning interatomic potential[J]. Scripta Materialia, 2020(186):14-18.