MASSIVELY PARALLEL 3D INSPECTION AND METROLOGY of
µFEATURES IN 3D PACKAGING
Arun A. Aiyer, Tianheng Wang
Frontier Semiconductor
San Jose, California USA
aruna@frontiersemi.com
Key Words:
3D Areal Imaging, Phase Image, µBumps, C4 Bumps
Image Processing, VIT, White light Interferometer
INTRODUCTION
All commercially viable chip stacking and die stacking
methods require that conductors such as Cu nail or Cu
pillar or solder bumps be placed on the chip or die I/Os to
act as the interconnection. So in the realm of 3D
packaging involving TSV/Cu nails in 2.5/3D-IC, RDL
µBumps in FO-WLP or solder bumps in Flip Chips, 3D
co-planarity inspection of these interconnect features are
critical for enhancing yield. Miniaturization in 3D
packaging along with demand for higher density I/Os are
challenging inspection and metrology approaches to
higher measurement capability and throughput. Therefore,
a high throughput, production ready metrology tool with a
high performance sensor that can measure C4 bumps to
Cu reveal height and capable of 100% height inspection
through metrology, will be very valuable in terms of yield
improvement, cost of ownership reduction and tool
utilization.
DISCUSSION:
3D-AI is an imaging technique that captures phase shift
introduced by µfeatures such as µBumps, C4 bumps, Cu
pillars, RDL bumps etc. in the measurement beam. Use of
unique source-sensor configuration enables creation of
phase map of the features, from which their height
information can be deduced. Additionally, this technique
allows all features in the field of view to be measured
simultaneously, thus enabling massively parallel
measurement of all individual features imaged by the
sensor. An example of arrayed features that are measured
using 3D-AI is represented schematically in figure 1.
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ABSTRACT:
Frontier Semiconductor (FSM) will introduce and present
data on a new sensor platform that has the ability to
measure in one exposure, individual heights of several
hundred bumps or as many as there are in one field of
view. With this parallel measurement capability, the
sensor can be configured to measure 100% of µBumps &
Cu pillars/posts used in 2.5D, Flip Chip, e-WLB
packaging and Cu-Nails used in 2.5D/3D stacking at
industry acceptable throughput levels. This patent
pending technology is based on imaging which enables
large area measurement. We refer to it as 3D Areal Image
(3D-AI) sensing. Using this technique we have measured
a few microns tall Cu nails from Cu reveal process to tens
of microns tall solder bumps used in 3D chip packaging
such as Flip Chip, e-WLB etc. The theoretical resolution
is <1% of the nominal height. Initial dynamic
repeatability tests show that measurement is reproducible
to ~1% of the nominal height. Correlating data from
White Light Interferometer (WLI) and FSM’s patent
pending Virtual Interface Technology (VIT) is also
presented.
Cu NAIL height
Bump height
Figure 1. µFeatures that can be measured with 3D-AI
Camera
Imaging
Optics
Source
Sample
Figure 2. 3D-AI sensor schematic
The optical set-up shown schematically in figure 2 uses a
white light continuum for illuminating the sample, a unity
magnification imaging system and a 1.3M pixel CMOS
camera. The inspection ready field of view is determined
by the size of the illumination beam and the sensor
format. A series of phase images are acquired which
provides phase information at each and every point in the
field of view. Intensity in the image plane can be
presented as
I ( x)  I1 ( x)  I 2 ( x)  2 I1 ( x) I 2 ( x) cos 12
Where I1 and I2 are the intensity corresponding to two
different locations in the sample plane and 12 carries the
necessary phase information. From this, height of each
µFeature can be determined. A simulated example of this
approach is given in figure 3. In this example, 10µm tall
Figure 4. Best Fit Plane—Top View
Figure 5. Best Fit Plane—Side View
Coplanarity defined as Max-Min in this case is 0.44µm.
See reference 2.
Figure 3. Modeling of 3D-AI sensor
µBumps are used. The regression plane that best fits the
topography generated by the bumps populating the x-y
plane is shown in figures 4 and 5.
Measurement_1: µBumps
Using the 3D-AI sensor with a FoV of 3mm, we
measured heights of micro solder bumps that are
nominally 26 µm tall. A representative raw image of the
bumps that were measured and reported here is shown in
figure 6.
Figure 7. Processed micro bump image with data label
Once the bump heights are determined, coplanarity can be
gauged. In one approach [1], the bumps that are within 2
standard deviations are considered to exhibit good
coplanarity. For the example in figure 7, 120 bumps were
measured simultaneously. The average bump height is
26.96 with 1 SD of 0.44 µm. Ninety nine bumps are
within 2 SD. As seen from figure 8, there are a few
outliers which may cause problem.
Figure 6. Grey scale image of micro bumps
Once the required images are acquired, they are processed
pixel by pixel in each frame. For images like the one
shown above, processing needs to be limited to only those
image pixels that carry information on “care” regions; in
this case the “bump areas”. This means pre-processing of
such images, to identify regions of interest, i.e., “care”
regions, and then analyze only those regions for 3D
metrology and inspection. Once the analysis is done, the
data can be displayed in a variety of manner as per the
customer requirement. As mentioned before, this
technique allows all features in the field of view to be
measured simultaneously, thus enabling massively
parallel measurement of all individual features imaged by
the sensor. The processed image can display individual
heights values simultaneously as shown in figure 7.
Figure 8. µBump coplanarity measurement results
In another approach [2], coplanarity is calculated using
‘Best Fit Plane’ (BFP) approach. As described in the
reference, the BFP is determined through the method of
least squares. The regression plane can then be offset in a
parallel manner to the apex of the bump that exhibits the
greatest perpendicular distance from the substrate. The
offset regression plane is the plane parallel to the best-fit
plane passing through the apex having the greatest
positive signed distance from the best-fit plane (Figure 9).
The coplanarity is the distance from the offset regression
plane to the apex farthest away from it. This is simply:
(max-min). For this example it is 2.27 µm.
Figure 9. Best Fit Regression Plane and Offset Plane
Measurement_2: C4 Bumps
Bump Coplanarity
12
10
8
6
4
2
0
3
3.6
4.2
4.8
5.4
6
6.6
7.2
7.8
8.4
9
9.6
Frequency
C4 bumps with average height of 80.38 µm and 1 SD of
0.9 µm was another sample that was measured using 3DAI sensor. Using best fit plane approach the coplanarity in
one FoV is calculated as 3.62 µm. The following figure
10 only shows the best fit regression plane for better
visualization of data distribution about this plane.
Cu Nail Height in µm
Figure 12. Cu Nail coplanarity measurement results
Measurement4: Flat Solder Bumps
Figure10. Best Fit Plane for the measured C4 bumps
Figure 13. Grey scale image of flat bumps
Measurement_3: Cu Reveal Height
Another example of massively parallel 3D-AI inspection
is shown via measurement of Cu reveal heights in TSVs.
Figure 11. Processed Cu Nail image with height label
One hundred and twenty eight nails were measured with
an average nail height of 5.49 µm and 1 SD of 0.95µm.
Looking at coplanarity from ±1 point of view, there are
outliers that are short enough that can lead to open circuit
and those that are long enough to puncture through the
interconnect layer. See figure 12 for distribution of Cu
nail height in the measured sample.
These flat solder bumps are ~11µm tall with lateral
dimensions ~ 88x36 µm2 and 104x26 µm2. To validate
3D-AI measurements, these bumps were also measured
using VIT [3] and White Light Interferometer. The results
of these three measurements are shown via the chart in
figure 14.
Figure 14. Comparing 3D-AI data with VIT and WLI
data
The three data sets show good correlation. It should be
mentioned that the data given here are raw data and no
calibration factors were used in computing the height
values in anyone of these three techniques.
Additional Feature of 3D-AI Sensor
Another important feature of 3D-AI sensor is its ability to
carry 2D defect inspection. This is clearly illustrated in
figure 15. The latter is made possible because the 3D
inspection is also image based. Thus a single sensor head
can achieve both functionalities.
(a)
Missing one bump Defect
(b)
Figure 15. Example of defect detection. (a) Defect is
detected in the rectangular area; (b) zoomed-in image of
the rectangular area shown in (a).
CONCLUSIONS:
The examples shown above illustrates that 3D-AI sensor
is capable of simultaneous height measurement of all
features in its field of view. This in turn facilitates
inspection of an entire wafer at industry acceptable
throughput rates. Furthermore, images acquired by the
same sensor head can also be used for bump size
determination and 2D defect inspection without additional
time over head.
REFERENCES
1.
2.
3.
“Ultra-Thin
Ultrafine-pitch
Chip-Package
Interconnections for Embedded Chip last
Approach” by Gaurav Mehrotra, Georgia Tech,
Master’s Thesis, p. 90, 2008
“Bump Coplanarity” by Keith Doyle, Solid State
Technology,
http://electroiq.com/blog/2007/05/bumpcoplanarity/
“Optical technologies for TSV inspection” Arun
Aiyer etal. Metrology, Inspection, and Process
Control for Microlithography XXVIII. Proc.
SPIE vol. 9050, 2014