Abstract
This paper investigates voiding issues in the underfilling process of ball grid array (BGA) chip packages under various parameter settings such as chip conveyor speed, valve pressure, temperature, and dispense pattern complicate. The study identifies valve pressure as the primary cause of voiding in large quantity BGA chips, achieving 88.9% in accuracy, supported with the deformation of the valve nozzle. Additionally, the findings reveal that racing effects occurs due to asymmetry of the solder ball array arrangement with percentage difference between the TSAM BGA chips experiments and its simulation counterparts in the range of 0.089–3.65%.
References
1.
Ng
,
F. C.
, and
Abas
,
M. A.
, 2021
, “
Underfill Flow in Flip-Chip Encapsulation Process: A Review
,” ASME J. Electron. Packag.
,
144
(1
), p. 010803
.10.1115/1.40506972.
Abas
,
A.
,
Haslinda
,
M. S.
,
Ishak
,
M. H. H.
,
Nurfatin
,
A. S.
,
Abdullah
,
M. Z.
, and
Ani
,
F. C.
, 2016
, “
Effect of ILU Dispensing Types for Different Solder Bump Arrangements on CUF Encapsulation Process
,” Microelectron. Eng.
, 163, pp. 83–97.10.1016/j.mee.2016.06.0103.
Abdullah
,
M. K.
,
Abdullah
,
M. Z.
,
Mujeebu
,
M.
,
Kamaruddin
,
S.
, and
Ariff
,
Z. M.
, 2009
, “
A Study on the Effect of Epoxy Molding Compound (EMC) Rheology During Encapsulation of stacked-CHIP Scale Packages (S-CSP)
,” J. Reinf. Plast. Compos.
,
28
(20
), pp. 2527
–2538
.10.1177/07316844080924094.
Chen
,
K. M.
, 2008
, “
Comparing the Impacts of the Capillary and the Molded Underfill Process on the Reliability of the Flip-Chip BGA
,” IEEE Trans. Compon. Package. Technol.
,
31
(3
), pp. 586
–591
.10.1109/TCAPT.2008.20011615.
Chang
,
R. Y.
,
Yang
,
W. H.
,
Hwang
,
S. J.
, and
Su
,
F.
, 2004
, “
Three-Dimensional Modeling of Mold Filling in Microelectronics Encapsulation Process
,” IEEE Trans. Compon. Packag. Technol.
,
27
(1
), pp. 200
–209
.10.1109/TCAPT.2003.8216826.
Chang
,
R.
, and
Yang
,
W.
, 2001
, “
Numerical Simulation of Mold Filling in Injection Molding Using a Three-Dimensional Finite Volume Approach
,” Int. J. Numer. Methods Fluids
,
37
(2
), pp. 125
–148
.10.1002/fld.1667.
Young
,
W.-B.
, 2010
, “
Modeling of a non-Newtonian Flow Between Parallel Plates in a Flip Chip Encapsulation
,” Microelectron. Reliab.
,
50
(7
), pp. 995
–999
.10.1016/j.microrel.2010.03.0088.
Colella
,
M.
, and
Baldwin
,
D.
, 2004
, “
Void Free Processing of Flip Chip on Board Assemblies Using No-Flow Underfills, Proc
,” Int. Symp. Exhib. Adv. Packag. Mater. Process. Prop. Interfaces
,
9
, pp. 272
–281
.10.1109/ISAPM.2004.12880269.
Edwards
,
K. L.
, 1994
, “
Towards More Effective Decision Support in Materials and Design Engineering
,” Mater. Des.
,
15
(5
), pp. 251
–258
.10.1016/0261-3069(94)90071-X10.
Ng
,
F. C.
,
Ali
,
M. Y. T.
,
Abas
,
A.
,
Khor
,
C. Y.
,
Samsudin
,
Z.
, and
Abdullah
,
M. Z.
, 2019
, “
A Novel Analytical Filling Time Chart for Design Optimisation of Flip-Chip Underfill Encapsulation Process
,” Int. J. Adv. Manuf. Technol.
,
105
(7–8
), pp. 3521
–3530
.10.1007/s00170-019-04573-611.
Martinek
,
P.
, and
Krammer
,
O.
, 2019
, “
Analysing Machine Learning Techniques for Predicting the Hole-Filling in Pin-in-Paste Technology
,” Comput. Ind. Eng.
,
136
, pp. 187
–194
.10.1016/j.cie.2019.07.03312.
Yi
,
S.
, and
Jones
,
R.
, 2019
, “
Machine Learning Framework for Predicting Reliability of Solder Joints
,” Soldering Surf. Mount Technol.
,
32
(2
), pp. 82
–92
.10.1108/SSMT-04-2019-001313.
Krammer
,
O.
,
Martinek
,
P.
,
Illes
,
B.
, and
Jakab
,
L.
, 2019
, “
Machine Learning-Based Prediction of Component Self-Alignment in Vapour Phase and Infrared Soldering
,” Soldering Surf. Mount Technol.
,
31
(3
), pp. 163
–168
.10.1108/SSMT-11-2018-004514.
Liu
,
B.
,
Chen
,
G.
,
Lin
,
H. C.
,
Zhang
,
W.
, and
Liu
,
J.
, 2021
, “
Prediction of IGBT Junction Temperature Using Improved Cuckoo Search-Based Extreme Learning Machine
,” Microelectron. Reliab.
,
124
(May
), p. 114267
.10.1016/j.microrel.2021.11426715.
Naqib
,
M. N.
,
Ng
,
F. C.
,
Abas
,
A.
,
Abdullah
,
M. Z.
,
Ali
,
M. Y. T.
, and
Samsudin
,
Z.
, 2022
, “
Prediction of the Void Formation in No-Flow Underfill Process Using Machine Learning-Based Algorithm
,” Microelectron. Reliab.
,
135
, p. 114586
.10.1016/j.microrel.2022.11458616.
Microsoft Learn,
2021
, “
Evaluation Metrics
,” accessed May 27, 2022, https://docs.microsoft.com/en-us/azure/cognitive-services/language-service/custom-text-classification/concepts/evaluation-metrics17.
Analytic Steps,
2021
, “
Binary and Multiclass Classification in Machine Learning
,” accessed May 27, 2022, https://www.analyticssteps.com/blogs/binary-and-multiclass-classification-machine-learning18.
Shung
,
K. P.
, 2021
, “
Accuracy, Precision, Recall or F1?
,” accessed May 28, 2022, https://towardsdatascience.com/accuracy-precision-recall-or-f1-331fb37c5cb919.
Dang
,
T.
, 2021
, “
Guide to Accuracy, Precision, and Recall
,” accessed May 28, 2022, https://www.mage.ai/blog/definitive-guide-to-accuracy-precision-recall-for-product-developers20.
Microsoft Learn,
2021
, “
Two-Class Averaged Perceptron Component
,” accessed May 27, 2022, https://docs.microsoft.com/en-us/azure/machine-learning/component-reference/two-class-averaged-perceptron21.
Microsoft Learn,
2021
, “
Two-Class Bayes Point Machine
,” accessed May 27, 2022, https://docs.microsoft.com/en-us/previous-versions/azure/machine-learning/studio-module-reference/two-class-bayes-point-machine22.
Microsoft Learn,
2021
, “
Two-Class Support Vector Machine component
,” accessed May 27, 2022, https://docs.microsoft.com/en-us/azure/machine-learning/component-reference/two-class-support-vector-machine23.
Ardebili
,
H.
, and
Pecht
,
M. G.
, 2009
, “
Chapter 3 - Encapsulation Process Technology
,” Encapsulation Technologies for Electronic Applications
,
Elsevier
, Amsterdam, The Netherlands, pp. 129
–179
.24.
Sorono
,
D. V.
,
Lin
,
J.
,
Chong
,
C. T.
,
Chong
,
S. C.
, and
Vempati
,
S. R.
, 2012
, “
Study on Mold Flow During Compression Molding for Embedded Wafer Level Package (EMWLP) With Multiple Chips
,” IEEE 14th Electronics Packaging Technology Conference (EPTC
), Singapore, Dec. 5–7, pp. 336
–341
.10.1109/EPTC.2012.650710325.
Khor
,
C. Y.
,
Abdullah
,
M. Z.
,
Abdullah
,
M. K.
,
Mujeebu
,
M. A.
,
Ramdan
,
D.
,
Majid
,
M. F. M. A.
,
Ariff
,
Z. M.
, and
Abdul Rahman
,
M. R.
, 2011
, “
Numerical Analysis on the Effects of Different Inlet Gates and Gap Heights in TQFP Encapsulation Process
,” Int. J. Heat Mass Transfer
54
(9–10
), pp. 1861
–1870
.10.1016/j.ijheatmasstransfer.2010.10.03826.
IPC-A-610
, 2010
, “
Acceptability of Electronic Assemblies
,” Revision F - July 2014 Supersedes Revision E, 8.3.12.4, pp. 8
–93
.27.
Ng
,
F. C.
,
Zawawi
,
M. H.
, and
Abas
,
M. A.
, 2021
, “
Spatial Analysis of Underfill Flow in Flip-Chip Encapsulation
,” Soldering Surf. Mount Technol.
,
33
(2
), pp. 112
–127
.10.1108/SSMT-05-2020-001728.
Ng
,
F. C.
,
Abas
,
A.
,
Abdullah
,
M. Z.
,
Ishak
,
M. H. H.
, and
Chong
,
G. Y.
, 2017
, “
Scaling Effect on Velocity Profiles in Capillary Underfill Flow
,” IOP Conf. Ser. Mater. Sci. Eng.
,
203
(1
), p. 012013
.10.1088/1757-899X/203/1/01201329.
Abas
,
A.
,
Ishak
,
M. H. H.
,
Abdullah
,
M. Z.
,
Che Ani
,
F.
, and
Khor
,
S. F.
, 2016
, “
Lattice Boltzmann Method Study of BGA Bump Arrangements on Void Formation
,” Microelectron. Reliab.
,
56
, pp. 170
–181
.10.1016/j.microrel.2015.10.01430.
ThinkML Team
, 2022
, “
CPU vs GPU in Machine Learning Algorithms: Which is Better?
,” accessed May 28, 2022, https://thinkml.ai/cpu-vs-gpu-in-machine-learning-algorithms-which-is-better/31.
Yu
,
H.
, 2020
, “
Scanning Acoustic Microscopy for Material Evaluation
,” Appl. Microsc.
,
50
(1
), p. 25
.10.1186/s42649-020-00045-4
