Characterize and Quantify the Production Inspection...

Characterize and Quantify the Production Inspection Capability of the AXI of HiP(Head in Pillow) Defects Project

End-of-Project Report

May 9, 2019, 11 AM EDT &May 10, 2019, 9 AM CST

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Team Members

Name CompanyHerb Holmes (Co-Leader) Intel

Robin Hou (Co-Leader) IBM

Ayman Fayed; Bill Hardin Intel

Rick Tao Celestica

Phuong Chau; Jiyan Zhang Flex

PK Pu Lenovo

Wayne Zhang IBM

Maxwell Milroy Microsoft

Bee-ling Wong; Jeremy Pemberton-Pigott Keysight

Yuko Nomura SAKI Corporation

Richard Coyle NokiaSeow Zi Yang; Chong Wei Chin; Hee Wei Ken; Ong Cheng Soon Vitrox

Cindy Han Wistron

Project Scope

To characterize and quantify HiP detection capabilities for AXI equipment used in PCBA manufacturing across the spectrum of x-ray technology set, algorithm methods and vendor offerings.

Objective

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q Perform Industry scan of AXI equipment and Improve capability of HiP/NWO/NCO detection thato Improves detection rates (> 99%)o Minimizes false call rateso 0 false accept rateso Algorithm based for improved Repeatability and Reproducibilityo In-line CT scan capability optimizing inspection time and accuracy of defect identificationo Production tool

Background

• HiP (Head in Pillow) is process abnormality from the SMT reflow process, where BGA solder balls do not coalesce on the solder paste properly. (Figure 2)

• It looks like a head resting on a soft pillow from a cross-section view (see Figure 1).• The HiP solder joint is not reliable as it is an intermittent contact and will easily pass

the subsequent electrical test with limited test coverage, thus potentially escaped and cause field failure.

Figure 1

Figure 2

Background

• HiP solder joints have a greater risk of occurrence in today’s technological landscape due to:– Increased package size, reduced package thickness and reduced ball pitches– Non- complimentary board + package warpage characteristics– Characteristics of thick multi-layered PCBs

X-Ray Systems Evaluated

• Vendor 1:– Used a high-accuracy 3D generated Planar Computed Tomography (PCT) system

• Vendor 2:– Utilized a system which uses Digital Tomosynthesis X-ray Image Reconstruction

Technology

• Vendor 3:– Testing was conducted utilizing a Zeiss Xradia MicroXCT-400 high resolution 3D

X-ray imaging system. – Performed the “Golden Sample” testing. The reason the Xradia tool was used to

verify true HiP locations is that it is the best method available that is non-destructive to the samples

Samples Tested

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Table 1: Sample Descriptions

ID PackageDetails

PinCount

BallSize

BoardDim

PitchSize

BoardThickness

S01 FP BGA 1356 9 mils 6”x6” .4mm

S02 POP 256 Top1064Bottom

10 mils 7”x6” .4mm 0.99mm

S03 Socket 1366 20 mils 12”x13” .5mm 3mm

S04 FP BGA 84 18 mils 12”x11” 0.8mm 3mm

S05 LGA Socket 4036 24 mils 18.8”x9.3” .5mm 3mm

S06 POP 396 Top1178Bottom

12mils Top8mils Bottom

5.8”x1.6” .4mm 0.976mm

Results/Discussion

• HiP Count by vendor as compared to Xradia standard

Parameter Vendor 2 Vendor 1# false calls – Sample 01 2.55 N/A# false calls – Sample 03 .067 N/A# false calls – Sample 05 1.64 N/A# false calls – Sample 06 12 N/ATool Under Reject Rate - % escapes 26.5% 3%Detection Rate = # Defects found / # Defects present 25/34 = 73% 33/34 = 97%# Slices used for detection 3 3

• False Call/Detection Rates

SAMPLE SN DEFECT POINT XRADIA HIPQTY

VENDOR 2 HIP QTY

VENDOR 1 HIP QTY

1 BGA1 A66.AR71.AU69.AY68.AY71.BA66.BA67.BA70.BA71.G71

10 10 10

2 BGA1 N/A 141 121BGA2 N/A 181 69BGA3 N/A 0 0

3 BGA1 AW35 1 1 0

4 BGA1 N/A N/A 4/8BGA2 N/A N/A 8BGA3 N/A 3 N/ABGA4 N/A 5 N/ABGA5 N/A 22 N/A

5 BGA1 0 0 0BGA2 A51, F58 2 2 2BGA3 A51, DF8 2 2 2

BGA4 A43, A47, A51, A53, B54, D56, DB58, E57, F58,G57

10 10 10

6 BGA1 1186,1223,1296,1334,1335 5 0 5BGA2 2,3,39,76 4 0 4

Example HiP Defect Image

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Conclusions

• All three of the X-ray solutions are capable of capturing HiP Defects on some boards

• Primary issue is likely human factors related to machine set up and with test image verification

• Vendor # 1 had the best detection rate, but cannot make a strong conclusion without gathering machine false call data

Challenges

• Lack of false call data to determine the cost for finding defects

• Hard to get data in a timely fashion and not as much data as we would have liked

• Difficult to manage the samples from site to site– Boards lost in customs– Keeping track of where samples were at a given time

• Keeping everyone engaged and excited throughout the duration of the project– Several vendors agreed to participate but then some

dropped out without providing data

Next Steps

• This project did not include the test cycle time in performance indicator. But in physical industry situation, it would be a critical factor for factories to choose an on-line X-ray machine.

• High resolution setting in Machine would require long test cycle time, so the appropriate balance between test resolution, which would directly relate to false call and detectability, and test cycle time would be an ideal next topic for our follow-on works.

• Algorithm optimization/study needs to be conducted

www.inemi.orgMark Schaffermarks@inemi.org

Recommendations for improvement

• Get all of the “golden sample” data collected before sending samples around the world

• Use a smaller sample set with more device types on the board. – For example a sample test board that has all of the

package types assembled on it.• Give each vendor a set amount of time to test• No retesting allowed