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Fusion of Ultrasound and X-ray Data for Automatic Inspection of Flip Chip and BGA Solder Joints. Ryan Yang 27/02/2009. Presentation Outline. Introduction Acoustic Micro Imaging X-ray Imaging Image Registration Image Fusion Conclusion. Introduction.

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fusion of ultrasound and x ray data for automatic inspection of flip chip and bga solder joints

Fusion of Ultrasound and X-ray Data for Automatic Inspection of Flip Chip and BGA Solder Joints

Ryan Yang

27/02/2009

presentation outline
Presentation Outline
  • Introduction
  • Acoustic Micro Imaging
  • X-ray Imaging
  • Image Registration
  • Image Fusion
  • Conclusion
slide3

Introduction

  • Solder joint reliability is a primary concern in the assembly of all Electronic components and products.
  • The importance of solder joint reliability became more emphasized in recent years as a result of three factors:
  • 1) The shift from leaded to lead-free solders in semiconductor industry.
  • 2) Shrinking in die size as well as solder balls dimension.
  • 3) The emergence of fine-pitched area array packages that employ hundreds of solder joints for electrical connection.
slide4

Figure 1: Evolution of Packaging Technology

(adapted from Japan Jisso Technology)

Introduction

slide5

Introduction

Hidden Solder Joints !?

slide6

Figure 2a: X-ray image of solder joints

Figure 2b: Ultrasound C-scan image of solder joints

Introduction

  • Acoustic Micro Imaging and X-ray imaging are principle Non-Destructive Testing techniques.
  • Both techniques can penetrate through the component to image the hidden solder joints.
  • X-ray inspection and AMI are complementary, each technology has distinct discriminating features and is good at inspecting certain defects.
slide7

Introduction

  • AMI is an effective approach for detecting gap-type defects due to strong reflections of ultrasound at a solid-air interface.
  • X-ray inspection is able to identify volumetric defects which are hard to detect by AMI.
slide8

Introduction

  • Penetration of AMI through several layers of dissimilar materials is a big challenge for AMI, whereas X-ray penetration is good but without the discrimination accuracy.
  • Inspection of flip chip and BGA solder joints still remains a significant challenge to current testing techniques
  • FUSION of ultrasound and X-ray data for flip chip and BGA solder joints provides a novel way to interpret and analyse the image of the solder joints and potentially increases the resolution of very small dimensions
slide9

Introduction

  • When two complementary techniques are combined, they could be helpful in reinforcing certain evaluations, improving feature measurement resolution, technique can also applied to other fields.
  • Combining multiple image modalities to provide a single, enhanced picture is offering an added value and more informative data to the processor in order to developing an automated inspection system. (Smith, 2005)
  • In future, other techniques such as MRI. Infrared and AFM could be added.
slide10

Acoustic Micro Imaging

  • Acoustic Micro Imaging (AMI) is makes use of the properties of ultrasonic waves which range from 5MHz to 400MHz.
  • Ultrasonic waves are generated by a piezoelectric transducer and propagate through an object.
  • When the wave travels through the object, it may be scattered, reflected and absorbed with respect to the differences between acoustic properties of materials.

Figure 3: Reaction of ultrasound wave in an object (Image adapted from Sonoscan Inc)

slide11

Figure 4b: Through Transmission Mode

Figure 4a: Pulse Echo Mode

Acoustic Micro Imaging

(Images adapted from Sonoscan Inc)

Different imaging modes are used for locating certain defects.

slide12

Acoustic Micro Imaging

Figure 5: AMI images of Flip chip solder joints

slide13

X-Ray Imaging

  • X-ray microscope imaging uses electromagnetic radiation in the soft X-ray band to produce images of very small objects.
  • When X-rays pass through a materials, it experience a variety of scattering interaction. These interactions lead to energy attenuation and the energy is detected by a Charge Coupled Devices (CCD).
  • X-rays imaging is a contrast imaging technique where high density materials lead to higher attenuation and hence produce darker image than those with less density or thickness.
slide14

X-Ray Imaging

Figure 6: X-ray images of Flip chip and BGA solder joints

slide15

Image Registration

  • The essential step in the fusion process is to bring the X-ray and C-scan images into spatial alignment, known as registration.
  • Image Registration is the process of overlaying two or more images of the same scene taken at different times, different viewpoints or different sensors.
  • The registration geometrically align two images or transform different set of data into one coordinate system.
slide16

Image Registration

  • Registration is have been widely used in:
  • Medicine
  • Combining CT, NMR or MRI data
  • Remote Sensing
  • Multispectral classification
  • Environmental monitoring
  • Change detection
  • Image mosaicing
  • Weather forecasting
  • Computer Vision
  • Target localization,
  • automatic quality control
  • Military
  • Satelite detection
  • Map updating
slide17

Image Registration

  • Open Source Registration tools
    • ITK-Insight software consortium
    • AIR- Roger P. Woods, M.D., UCLA School of Medicine
    • FLIRT – FMRIB centre, University of Oxford
    • DROP - Technische Universität München (TUM) , Germany
    • BunwarpJ - Arganda-Carreras , Universidad Autonoma de Madrid
  • No pre-processing and mainly developed for biomedical images
slide18

Image Registration

Figure 7: Feature Detection

Figure 8: Feature Matching

(Images adapted from Zitova,2003 )

slide19

Image Registration

Figure 9: Transformation Model Estimation

Figure 10: Image Resampling and Transformation

(Images adapted from Zitova,2003 )

slide20

Image Registration

Figure 11: Original Images

Figure 12: Processed Images

slide21

Image Registration

  • Point based methods and Least Square Approximation
  • The transformation that aligns the corresponding fiducial points will interpolate the mapping from these points to other points in the view.

w= weighting factor

X= points of reference image

Y= points of sensed image

R= rotation

t= Translation

Figure 13: Measurement for Registration Errors

slide22

Image Registration

  • Compute the weighted centroid of the fiducial configuration in each space:
  • Compute the weighted fiducial covariance matrix:
slide23

Image Registration

  • Perform singular value decomposition (SVD) of H
  • Finally,
slide24

Image Registration

Figure 14: LabVIEW Program for Computing Point Based Method

slide25

Image Fusion

  • The term Image Fusion generally implies the intelligent combination of multi-modality sensor imagery for the purpose of providing an enhanced single view of a scene with extended information content. (Smith, 2005)
  • Fundamental Standard of fusion
    • The fused image should preserve all salient information of source images.
    • The fusion process should not introduce any artefacts or inconsistencies into the fused image.
    • Undesirable features (noise) should be suppressed in the fused image.
slide26

Image Fusion

Table 1: Table of Fusion Method

slide27

Image Fusion

Maximum Amplitude and Weighted Pixel Averaging

  • Common Fusion Algorithm approaches:
  • Disadvantages
  • Also suppresses salient features
  • Low contrast
  • ‘washed-out’ appearance
  • Advantages:
  • Easy implemented
  • Fast to execute
  • Suppressing noise
slide28

Image Fusion

  • Multi-Resolution Methods
  • Extract the salient features at several levels of image decomposition from coarse to fine
    • Pyramidal Schemes
      • Gaussian Pyramid
      • Laplacian Pyramid
    • Wavelet Schemes
    • Colour Fusion
  • Advantages
  • Produce sharp, high-contrast images
  • Disadvantages
  • Reserve unwanted features
  • Further Assessment is required
slide29

Image Fusion

Pyramidal Schemes

R=Reduce

E=Expand

D=Difference

F=Fused

C=Combined

Figure 15: Generic Pyramidal image fusion scheme

(Image adopted from Smith,2005)

slide30

Image Fusion

  • Reduce operation:
  • Expand operation:
slide31

Image Fusion

  • Wavelet Schemes
  • Discrete Wavelet Transform

Figure 16b: Wavelet representation of Sinusoidal Wave

Figure 16a: Sinusoidal Wave

slide32

Image Fusion

  • Rescaling is usually done in power of two

Figure 17: Generic Wavelet Fusion Scheme

(Image adopted from Smith,2005)

slide33

Conclusion

  • Increasing the Solder Joints reliability can increase the product life time and increases customer quality.
  • Reduces potential warranty costs.
  • Image fusion provide a new method to keep inspection of hidden solder joints in line with the rapid reduction in component size
  • Improves the ability to inspect smaller dimensions seen in newer packaging. May also improve the inspection of area array parts such as BGA which contain interposer
  • Image fusion remains a challenging technology and its application in electronic inspection is less mature and required additional research and assessment
slide34

References

ZHANG, G.M., HARVEY, D.M. and BRADEN, D.R. (2006) “”X-ray Inspection and Acoustic Micro Imaging Applied to Quality Testing of BGA Solder Joints – A Comparative Study”, 2nd GERI Annual Research Symposium GARS 2006, Liverpool, UK, 15th June 2006

KAPUR, A. and et al (2002) “Fusion of Digital Mammography with Ultrasound – A phantom Study”, Proc of SPIE – The international Society of Optical Engineering, 4682, p.526-537

SEMMENS, J.E. (2000) “Flip Chis and Acoustic Micro Imaging: An overview of Past Application, Present Status, and Roadmap for the Future”. Proceedings of ESREF conference, Dresden, Germany, October 2000

ZITOVA, B. and FLUSSER, J. (2003) “Image Registration Methods: A Survey”, Image and Vision Computing vol. 21, p977-1000

SMITH, M.I. andHEATHER, J.P. (2005) “A review of image fusion technology in 2005” Thermosense XXVII. Proceedings of the SPIE, Vol. 5782, pp. 29-45