Real time image processing techniques, combined with multitasking computational capabilities are used to establish thermal imaging as a multimode sensor for systems control during crystal growth. Whereas certain regions of the high temperature scene are presently unusable for quantitative determination of temperature, the anomalous information thus obtained is found to serve as a potentially low noise source of other important systems control output. Using this approach, the light emission/reflection characteristics of the crystal, meniscus and melt system are used to infer the crystal diameter and a linear regression algorithm is employed to determine the local diameter trend. This data is utilized as input for closed loop control of crystal shape. No performance penalty in thermal imaging speed is paid for this added functionality. Approach to secondary (diameter) sensor design and systems control structure is discussed. Preliminary experimental results are presented

Wargo, Michael J.

English

NASA Technical Reports Server

N90- 179 -0
Use of Anomolous Thermal Imaging Effects for Multi-Mode
Systems Control During Crystal Growth
Michael J. Wargo
Massachusetts Institute of Technology
Cambridge, MA 02139
Abstract
Real time image processing techniques, combined with multitasking
computational capabilities are used to establish thermal imaging as a
multi-mode sensor for systems control during crystal growth. Whereas
certain regions of the high temperature scene are presently unusable for
quantitative determination of temperature, the anomolous information thus
obtained is found to serve as a potentially low noise source of other
important systems control output. Using this approach, the light
emission/reflection characteristics of the crystal, meniscus and melt system
are used to infer the crystal diameter and a linear regression algorithm is
employed to determine the local diameter trend. This data is utilized as
input for closed loop control of crystal shape. No performance penalty in
thermal imaging speed is paid for this added functionality. Approach to
secondary (diameter) sensor design and systems control structure is
discussed. Preliminary experimental results are presented.
Introduction
Real time thermal imaging has been found to be a powerful tool for passively
measuring the effects of applied magnetic fields on the temperature distribution of
encapsulated high temperature semiconductor melts during the crystal growth operation.
A complete description of the thermal imaging system's design and capabilities is available
in [1]. Advanced image processing techniques are employed to reduce or eliminate
temporal as well as spatial high frequency noise from the image. In this way spurious
perturbations, such as bubbles entrained within the encapsulating liquid glass layer, do not
effect the quantitative determination of the local temperature distribution. (See figures 1
and 2 for photographic and schematic descriptions of the thermal imaging process and
appartatus.) However, certain attributes of the optical path related to the geometry of
the high temperature scene introduce anomolous light intensity information which is not
directly related to the temperature at that position in the image.
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A typical example of this behavior is illustrated in figure 1 reproduced from [1]. The
horizontal line across the 512 x 512 digitized image (8 bit, 256 gray level) is user input
using a mouse-type pointing device. In this case, the line was selected to cross the
encapsulated melt as well as the meniscus region located at the edge of the growing
crystal. The light intensity data (directly related to the temperature of the melt) is read
from this line and dynamically superimposed on the image. The information has been
subjected to a linear convolution to reduce the effects of high frequency spatial noise.
The peaks in optical intensity associated with the meniscus region (at the edge of the
crystal) are unrelated to the local temperature since the temperature of the melt in
contact with the crystal must be at the melting point of the material being solidified (in this
case, GaAs). This behavior is currently interpreted as a measure of the radiation from
the hot crucible walls which is reflected by the curved meniscus (non-normal to the CCD
imaging camera, see figure 3 for details). However, this anomolous thermal information
provides for an indirect measurement of the evolving crystal shape since the distance (in
pixels, 0.3 mm/pixel) between peaks is proportional to the diameter. This difference data
is used as input to a closed loop proportional controller for control of the crystal diameter
during growth. The system input which is changed by the proportional controller is the
heater temperature ramp rate. The temperature of the heater is monitored by a
thermocouple situated in close proximity to an element of the heater and controlled using
a digital PI (proportional/integral) controller. (See section on Control Algorithm
Development and Experimental Results and figure 8.)
Experimental Approach
A conventional silicon puller (Hamco CG-800) was modified for the low pressure
growth of GaAs by LEC (Liquid Encapsulated Czochralski) pulling. A thermal imaging
system based on real time image processing techniques has been developed and is
operational on the puller. All control inputs (motor rates, temperature control etc.) are
activated via digital to analog conversion (D/A) of operator chosen or predetermined
feedforward trajectory values (figure 4).
The systems architecture of the thermal imaging system is given in figure 2, from
[1]. The melt is viewed in near normal incidence by a charge coupled device (CCD, 512
x 512 pixels) camera. The resulting monochromatic image (due to a 1 nm wide filter
centered on 633 nm) is digitized (8 bit / pixel) and processed using a pipelined pixel
159
processor operating at an effective 40 million operations per second, The images are
held in digital storage units which appear as extended dual ported memory on the bus of
the main CPU, In this way image data is accessed in parallel by both the vision engine
(real time image processing hardware and software) and CPU with its related floating
point and array processors, permitting significant filtering of both spatial and temporal
noise while maintaining real time (30 frames per second) imaging performance. The
diameter inference is obtained as described above. A significant advantage to this
measurement of crystal diameter is that it is insensitive to even a substantial degredation
of the optical path (e.g. 'fogging' of the windows by evaporated arsenic). This is due to
the measurement of the position of the peaks in intensity not the value of the peak
intensity.
Control Algorithm Development and Experimental Results,
Initial experiments were carried out using a pure diameter error signal
(Diameter[desired] - Diameter[measured]), with no image filtering, for feedback to the
heater temperature using a conventional proportional/integral (PI) digital control algorithm.
This approach to active diameter control gave results during growth which indicated two
fundamental limitations. First, without temporal and spatial filtering of the image the
diameter measurement signal contained fluctuations associated with perturbations caused
by the presence of entrained bubbles in the liquid encapsulant which caused the
temperature controller to change the diameter in a monotonic mode, i.e. the diameter
either was reduced to zero (The crystal pulled off,) or it increased to the crucible wall
(The melt surface froze,). Second and related, this approach did not take into account
the dynamic changes in heater temperature required during pulling to respond to the
batch nature of the process: the process is transient,
The effect of temporal filtering in obtaining a reduced noise representation of
crystal diameter is shown in figures 5, 6 and 7. The thermal imaging of a crystal growth
experiment was archived on a professional video tape recorder (Sony BVU-800) and
used post experiment to determine the crystal diameter with and without the use of a
temporal averaging alogrithm. Diameter sensing in figure 5 employed no digital filtering
(temporal or spatial) while the data in figure 6 was obtained with the application of a
temporal filtering algorithm with a 4 second time constant, (120 images
[4 s x 30 images/s] were averaged,) The post growth measurement of the actual
16o
crystal diameter is given in figure 7. Further optimization of this filtering procedure is in
progress.
The current control algorithm is a combination of feedforward and feedback
components (figure 8). Duringgrowth, the diameter is measured (as described above)
from images processed with both temporal and spatial filters. In five minute increments
the latest diameter data (taken at 10 s intervals) is analyzedby linearregression to obtain
the local diameter trend. This is compared with a set point which is no longer the absolute
diameter but rather a pre-selected (and changeable) diameter slope (diameter as a
function of time or distance grown). Any deviation of the measured diameter slope from
the setpoint diameter slope is used as an error signal to modify using a simple
proportional controller, not the absolute heater temperature, but the rate of temperature
reduction of the heater (the heater temperature trajectory). The error and new heater
trajectories are calculated as follows:
error = diameter slope [desired] - diameter slope [measured] [eq. 1]
new heater temperature trajectory = old trajectory - P * error [eq. 2]
Where P = proportional constant for controller.
This approach to automatic diameter control was used during low pressure LEC
pulling of GaAs. The results are shown in figure 9. The ADC algorithm was initiated 5 cm
following seeding. The setpoint (diameter slope) was initialized at 6 mm/hr. With the rate
of pulling set at 1.3 cm /hr, the resulting diameter rate setpoint was 0.46 mm / cm of
growth. The superposition of this slope on the crystal shape is in qualitative agreement
with the trend in diameter as grown. The cyclic nature of the response indicates that the
sensor did register the changes in diameter and the controller did respond in such a way
so as to reverse adverse trends. The amplitude of these changes indicate the lack of
controller tuning and the effects of residual diameter signal noise. It is important to note
that control action was not characterized by a limit cycle response. The controller was
not responding alternately between its upper and lower limits. Work is in progress in both
areas.
161
Discussion
Closed loop diameter control during LP-LEC pulling of GaAs using thermal imaging
based diameter sensing has been accomplished. A simple proportional controller is used
to maintain a diameter slope setpoint by modifying the heater temperature trajectory.
Noise in the diameter signal associated with video transmission noise and temporal
perturbations within the high temperature scene (e.g. bubbles) have been reduced by real
time spatial and temporal averaging algorithms. Residual noise in the diameter signal (for
example, from 'stationary' bubbles) still causes perturbations in the control action. The
solution to this problem will be sought by sensing the diameter from multiple regions of the
thermal image (at various angles around the crystal) and using the 'best' data as
determined from comparison with previously established good data values. An optimized
value of the proportional constant will be determined both from feedforward response of
diameter to heater temperature trajectory as well as from experimental experience.
Acknowledgements
The author is indebted to the Defense Advanced Research Projects Agency and the
National Aeronautics and Space Administration for their support of this work. The
stimulating technical discussions with Prof. August F. Witt, Dr. Douglas J. Carlson, Prof. S.
Motakef and Prof. M. Gevelber contributed significantly to this effort.
Bibliography
1,) M.J. Wargo, "Real Time Thermal Imaging of High Temperature Semiconductor
Melts", NASA Conference Publication 2503, Proceedings of a workshop held at
NASA Headquarters Washington, D.C., April 30 - May 1, 1987, Published 1988,
p. 384.
162
Figure 1. From [1]. Thermal image acquired during LEC growth of GaAs. The anomalous
behavior of the measured thermal image in the meniscus region is used to infer the
evolving shape (diameter) of the growing crystal.
/ \
/ \
/
Dud Ported
OigJtolStm'oge
Units
U
O.
Closed Loop Feedback
and
Feedforword Trajectories
1
$
ZI
Figure 2. From [1]. Thermal imaging architecture and control structure for LEC growth of
GaAs. Details can be found in [1].
163
ee
¢-.
_=
1. Advanced Sensor With
Spatial and Temporal F_ltering
2. Insensitive To Changes
In Optical Path
3. Feedforword and Feedback
Components
4. Linear Regression Based
Error Determination
; _j(/-.- Selected Diameter Trajectory
f Crucible Wall' / -- /t%_--Current Diameter Trend
Figure 3. Inference of the crystal diameter during growth is made from anomalous thermal
imaging information in the region of the meniscus formed between the crystal in
contact with its melt. The large peaks in intensity measured in this area are
currently interpreted as due to the radiation emitted from the high temperature
crucible walls which is subsequently reflected from the curved surface of the
meniscus.
Analog Video
GaAs I
Growth
Hamco " _ Analog Data
r
CG-800
Masscomp MC-5500
Analog Data
IConventional
Control
Console
J
Azonix 1000 Dlgltal Data
Figure 4. Data management for thermal imaging and growth control. Analog sensing and
control information is exchanged with the growth system by A/D and D/A
conversion within the Masscomp MC-5500. The analog video information (for
thermal imaging and diameter inference) is digitized by the analog front end of the
real time vision engine incorporated in the Masscomp MC-5500. (See [1] for
details,) The Azonix 1000 provides 18 bit resolution direct digitization of the
thermocouple sensor used to determine heater temperature,
164
160
L40
No Digital Filtering
6O
Figure 5.
I I I I I400 t 2 3 4 5 6 7 e 9
Dtstance (cm)
Crystal diameter (in pixels) inferred from archived thermal imaging information. No
image processing algorithms (temporal or spatial) were applied to the video
information.
t6o Temporally Fi]tered: 4 sec
6O
Figure 6.
400 t 2 3 4 5 6 7
Distance (cm)
8 9
Crystal diameter (in pixels) inferred from archived thermal imaging information. A
temporal averaging algorithm was used to reduce the effect of perturbations (e.g.
entrained bubbles in the encapsulant). See text.
165
E_r_
"-I
110
t'r
Figure 7.
3O
25
2o
i5
t0
f
f
I I I I I I I I
5O 1 2 3 4 5 6 7 8 9
DisLance (cml
Actual measured diameter of the crystal analyzed in figures 5 and 6.
_f---Diameter Slope Set Point
_-_-Meosured Local Diameter Trend
\ _ (By Linear Regression)
_'_-- Crystal
Error = SP - MLDT
Temperature Ramp = Old -
JDiameter Slope _ ProportionalSet point Contr ller
Diameter L
Slope
Inference r _
Heater Temperature
Ramp Rate
T/C
PI
Controller
loo__ ooo,,'l
P * Error
Figure 8. Control structure and algorithm used for closed loop control of crystal shape using
a diameter signal inferred from anomolous thermal imaging information.
166
4O
iO
Setpoint Diameter Slope
0.46 cm/cm
AOC begins
Measured Oiameter
I I I I I I
O0 1 2 3 4 5 6 7 8 9
Distance Gpown (cm)
Figure 9. Preliminary experimental results of closed loop control action based on the
structure and algorithm given in figure 8. See text for details.
167


Use of anomolous thermal imaging effects for multi-mode systems control during crystal growth

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