Journal of Biosciences and Medicines, 2013, 1, 1-5 JBM
http://dx.doi.org/10.4236/jbm.2013.12001 Published Online October 2013 (http://www.scirp.org/journal/jbm/)
A spatiotemporal signal processing technique for
wafer-scale IC thermomechanical stress monitoring by an
infrared camera
Michel Saydé, Ahmed Lakhssassi, Emmanuel Kengne, Roman Palenichka
Department of Computer Sciences and engineering, University of Quebec at Outaouais, Gatineau, Canada
Received July 2013
ABSTRACT
In this paper, we describe a new silicon-die thermal
monitoring approach using spatiotemporal signal
processing technique for Wafer-Scale IC thermome-
chanical stress monitoring. It is proposed in the con-
text of a wafer-scale-based (WaferICTM) rapid proto-
typing platform for electronic systems. This technique
will be embedded into the structure of the WaferIC,
and will be used as a preventive measure to protect
the wafer from possible damages that can be caused
by excessive thermomechanical stress. The paper also
presents spatial and spatiotemporal algorithms and
the experimental results from an IR images collection
campaign conducted using an IR camera.
Keywords: Thermal Monitoring; Ring Oscillator (RO);
Spatial; Spatiotemporal; Thermo-Mechanical Stress;
Temperature Sensor; Thermal Analysis; WaferIC;
Wafer-Scale System
1. INTRODUCTION
An innovative reconfigurable Wafer-Scale Integrated
Circuit (WaferIC) for rapid electronic systems prototyp-
ing has been introduced [1-3]. Electronic components
can be placed anywhere at the smart active surface of the
WaferIC. Then those components can be detected, pow-
ered and interconnected through a complex but regular
reconfigurable network laid over the surface of the
WaferIC. As power consumption depends on user chips
location and power pins distribution, it is not even ly dis-
tributed throughout the surface of the silicon wafer. In
many cases, it is unpredictable and variable as power
consumption of programmable or dynamic components
(e.g. microcontroller s) depends on their activities. There-
fore, managing thermomechanical stress is a real chal-
lenge and it may need to be monitored at all time in high
performance applications.
Thermal monitoring is essential in high performance
integrated structures implemented as multilayer struc-
tures composed of different materials. An increase of the
internal temperature of some circuits can lead to signifi-
cant thermal and thermo-mechanical problems. Under-
standing thermal phenomena occurring on a micro-scale
level is essential for SoC and MEMS -based applications.
Thus, measurement techniques are needed to validate
models predicting thermal behavior of integrated struc-
tures. In particular, measurement techniques are needed
to obtain surface temperature distributions of large inte-
grated structures.
Due to technology scaling, power density of high per-
formance integrated structures has increased drastically.
For example, the power density of high performance
microprocessors has already reached 50 W/cm2 at 100
nm technology and was forecasted to reach 100 W/cm2 at
20 nm technology. Meanwhile, to mitigate the overall
power consumption, many low power techniques have
been proposed [4-10]. These techniques, though helpful
to reduce the overall power consumption, may cause sig-
nificant on-chip thermal gradients and local hot spots due
to different clock/power gating activities and varying
voltage scaling. It has been reported in [11] that temper-
ature variations of 30˚C can occur in a high performance
microprocessor design. The magnitude of thermal gra-
dients and associated thermo-mechanical stress is ex-
pected to increase further as VLSI designs move into
nanometer processes and multi-GHz frequencies.
The WaferIC is a LAIC (Large Area Integrated Cir-
cuit). As a LAIC the increase of the power consumption
and uIC disposition on the surface of the WaferIC will
cause decrease in the wafer performance and the latency.
In addition, 50% of the failure in the electronic device is
due to the increase of the internal temperature of the chip
[12]. However, the WaferIC must be able to support a
large temperature differential at its surface [13]. There-
fore, in this paper we propose a spatiotemporal signal
processing technique for Wafer-Scale IC thermomechan-
ical stress monitoring by an infrared camera. The tech-

M. Saydé et al. / Journal of Biosciences and Medicines 1 (2013) 1-5
2
nique will be integrated into the WaferI C for the purpos e
of thermomechanical stress monitoring and management
using distributed RO thermal-sensor networ k.
2. STRUCTURE OF THE WAFERICTM
AND SENSOR INTEGRATION PLAN
As Figure 1 show, the WaferIC is an 8-inche SI wafer
and uses a cell-based architecture design. The surface is
divided into 76 reticles which use inter-reticle stitching
techniques to ensure connections between them. Each
reticle is composed of 32 × 32 unit-cells. All of the cells
are a clone (photo-repetition) and each of them contain a
part of an internal reconfigurable network for intercon-
nection with neighbouring cells and have a 4 × 4
NanoPads on its surface (top metal layer) [2].
In our previews work [13], we introduce d, tes ted on an
FPGA and calibrated a Ring Oscillator (RO) for the pu r-
pose of temperature measurement with the GDS (Gradi-
ent Direction Sensor) technique. For WaferIC imple-
mentation, we propose the use a same RO measurement
technique at the cell-level. So one RO composed of 17
sensing inverter will be implemented into each cell, for a
total of 77824 RO sensors. For each cell, one sensing
inverter will be installed under each NanoPad. And each
RO sensor will have its own control circuit imbedded
into each cell. So a network of embedded temperature
sensors is distributed evenly all over the surface of the
WaferIC. This configuration will provide a spatiotempo-
ral working space (x,y,t), which allow the reproduction of
high fidelity temperature map of the surface of the
waferIC (like an integrated thermal camera), and allows
the use of image and video processing techniques for the
monitoring and management of the thermomechanical
stress at the surface of the WaferICTM.
Figure 1. The WaferICTM architecture.
3. IMAGE PROCESSING AND
EXPERIMENTAL RESULTS
In this section, image processing and experiment results
on the WaferIC using an Infrared camera (VarioCAM®
high resolution inspects 768 from Jenoptik) will be pre-
sented. Figure 2 show s the layout of the experiment,
where the IR camera is installed v ertically over a dummy
WaferIC. The WaferIC is placed on an insulated surface
and heated from beneath with a two heat source with a
different heat rates. The Camera range is adjusted to
measure in 10-bit gray scale (1024 gray-level) tempera-
ture between 30˚C and 60˚C for a resolution of 0.117˚C
/count. While the WaferIC is heated, a stream of IR pic-
tures has been taken, and 2 of them separated by 60-
second apart have been selected to conduct this experi-
ment.
The IR camera has a resolution of 640 × 480 =
307200-Pixel. In order to mimic a spatial workspace
produced by an array of 77,824 temperature sensors im-
plemented on a wafer scale integrated circuit, those im-
ages has been scaled down. Therefore, a geometric trans-
formation has been used [14].
Figure 2. The experiment layout; the IR camera is placed
vertically over the WaferICTM, which is heated from beneath
by 2 separately controllable heat elements.

M. Saydé et al. / Journal of Biosciences and Medicines 1 (2013) 1-5
3
)},{(),( wvTyx =
(1)
where
),( wv
are the pixel-coordinates in the original
image,
),( yx
are the pixel-coordinates of the trans-
formed image, and T is the affine matrix with the fol-
lowing elements,










=
100
00
00
y
x
c
c
T
and the scaling equation become,
),(),( wcvcyx
yx
=
(2)
With
1
)89834710743.1(
−
=
x
c
and
1
)59875776397.1( − =
y
c
we get an array of 242 by 322 or
77924-Pixel. This is the closest as we could get to the
aimed value of 77824-pixels. Figure 3 shows the 3
scaled-down IR pic tures used for this ex pe riment .
3.1. Critical Thermomechanical Peaks
Identification
For identifying critical thermomechanical peaks stress
that could potentially damage the thin layers over the
surface of the wafer-scale IC we must identify the sur-
face heat sources that exceed a specific predefined tem-
perature threshol d va lue.



>
≤
=
s
s
Tyxfif
Tyxfif
yxD ),(1
),(0
),(
(3)
where
),( yxD
identify each peak with 1, and
s
T
is the
temperature threshold value. Figure 4 shows the results
of
),( yxD
with
119=
s
T
(maximum value (122) is in
picture c). So white dot in Figure 4(c), represent critical
temperature values exceeding Ts.
3.2. Temperature Change Velo city
Another important parameter that we tend to measure is
the temperature change velocity at each heat source
o
[ C/s]
.
),,(),,(
),,(
),,(
1nn
tyxftyxf
t
tyxf
tyxg −=
∂
∂
=
+
(4)
where
),,( tyxg
is the temperature change velocity at
(a) (b) (c)
Figure 3. IR picture of the WaferICTM heated from beneath by
two heat sources; we can see the evolution of temperature in
time over the surface from (a) to (c); pictures are separated by
60-second between.
(a) (b) (c)
Figure 4. Thresholding IR pictures of the WaferICTM to detect
critical hot spots, as we see one spot is detected in (c).
each sensor.
Again
),,( tyxg
can be compared to a threshold
1s
T



>
≤
=
1
1
),,(1
),,(0
),(
s
s
Ttyxgif
Ttyxgif
yxG
where
),( yxG
represent only velocities that exceed
1s
T
Figure 5 shows the resulting of this spatiotemporal
operation on pictures from Figure 3. As we see, the tem-
perature velocity is stronger at the beginning of the tran-
sient mode (picture a).
3.3. Thermal Shock Stress
A thermal shock to the thin layers at the surface of the
wafer also needs to be measured and quantified as it can
cause a serious damage to the structure if it exceeds a
certain limit
o
[ C/mm]L
. Thermal shock is closely re-
lated to temperature change velocity, but also to maxi-
mum temperature gradient value over the surface of the
WaferIC. Calculating a spatial gradient can help in eva-
luating the distribution of t emper ature over the surface










∂
∂
∂
∂
=






==∇
y
T
x
T
g
g
TgradT
y
x
)(
, (5)
The direction of the gradient is:
'),(
1






=
−
y
x
g
g
tgyx
α
(6)
This gradient is perpendicular to the isothermal lines
and has a magnitude of
22
)(),( yx ggTmagyxM+=∇=
(7 )
To simplify the calculation of the gradient we may
need to apply an amplitude thresholding function to the
thermal image
nmnn
TyxfTif
TyxfTif
Tyxfif
n
n
n
yxg
≤<
≤<
≤









=
−
),(
.
.
.
1),(
),(
.
.
.
),(
0
0
1
0
, (8)

M. Saydé et al. / Journal of Biosciences and Medicines 1 (2013) 1-5
4
(a) (b)
Figure 5. Spatiotemporal derivative of Figure s 3(a) and (b)
give (a), then (b); and (c) give (b).
(a) (b) (c)
Figure 6. Isotherm map produced from images of Figure 3.
Then applying a median filter to produce homogenous
nn regions
)},({),( yxfmedianyxm =
(9)
Figure 6, show same images, thresholded with 16
gray-level, 7 × 7 bo x median filter is applied to generate
homogenous regions, then a Laplacien is used (which is
an isotropic second order derivative operator) to generate
a temperature isotherm map for each picture (from Fig-
ure 3). The variation of temperature from 30˚C to 60˚C
has been quantified into 16 levels (or isotherms). So each
level up represents a 1.875˚C temperature increase.
4. CONCLUSIONS
This paper has introduced a methodology to evaluate and
predict possible thermal stress in large VLSI circuits us-
ing an infrared camera. Important factors contributing to
LAIC circuit thermal heating were presented. The pro-
posed monitoring approach can be applied to produce a
thermal map of the surface of the WaferIC. Then a spatial
and spatiotemporal approach has been presented to ex-
tract from successive thermal images a relevant data
which will help to prevent thermomechanical stress from
damagi ng the WaferIC .
Results reported in this paper are encouraging and
provide a good insight into the issues that will be useful
to the process of defining an automated and embedded
method for detection and management of thermome-
chanical stress in LAICs circuit.
5. ACKNOWL EDG EMENTS
The authors thank the Natural Sciences and Engineering Research
Council of Canad a (NSERC), Le Regroupement Stratégiq ue en Micro-
systèmes du Québec (ReSMIQ) and CMC Microsystems for prov iding
design tools, support and associated technologies. The authors thank
the MITACS and Gestion TechnoCap Inc. for their financial support.
Finally, the authors thank prof. Mohand Said Allili for help with pro-
viding thermal camera.
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