In-Situ Metrology: the Path to
Real-Time Advanced Process Control
G.W. Rubloff
University of Maryland, 2145 A. V. Williams Building, College Park, MD 20742-3285, USA
rubloff@isr. umd. edu, www. isr. umd. edu/gwrubloff/
Abstract. While real-time and in-situ process sensors have been effectively applied to fault detection, process
control through course correction has been mainly focused on in-line metrologies to drive run-to-run feedback and
feedforward control We have developed in-situ metrologies based on mass spectrometry, acoustic sensing, and
FTIR techniques which enable real-time thickness metrology and control in CVD processes at a level of about 1%
accuracy. These developments open the door to real-time sensors as the basis for both fault management and
course correction, i.e., for real-time advanced process control We have also employed in-situ metrology to
develop robust control schemes for CVD precursor delivery from solid sources, and we are exploring a new
spatially programmable reactor design paradigm for which real-time, in-situ sensing, metrology, and control of
across-wafer uniformity is fundamental
As indicated in Table 1, the goals of CC and FM
ADVANCED PROCESS CONTROL
are quite different: CC is aimed at keeping product
Metrology is a mainstay of advancement and
quality on track, while FM is intended to monitor and
practice in semiconductor technology development and
maintain equipment so that product is not wasted and
manufacturing. [1,2] Advanced process control (APC)
equipment repair is timely. The other characteristic
[3, 4] is aimed at exploiting metrology to increase
which differentiates APC approaches is a distinction
equipment productivity and lower manufacturing
between run-to-run and real-time control: run-to-run
costs. Early efforts toward APC were in evidence from
control can provide benefits from in-line or off-line
the late 1980's, including TI's MMST program [5-8],
measurements, while real-time control can deliver a
Sematech's J-88 program [9] , and the inclusion of a
different set of benefits if real-time/in-situ metrology is
metrology and process control roadmap as a
available. [19]
supplement to the first NTRS in 1994. [10-12] Today,
Control
Course
Fault
much progress has been made in developing and
Management
strategy
Correction
implementing APC in the industry, and APC has
monitor &
Goal
maintain
become a mainline component of
the ITRS
product quality
maintain
(Metrology section) [13]
and the subject of
equipment
professional meetings [14, 15], consortia [16], and
emerging
Run-to-run
mainline
publications [17, 18].
metrology
in-line, off-line
In both conceptual and operational senses, APC
control
feedback &
accelerated
is perhaps best regarded in terms of two components,
feedforward
maintenance
course correction and fault management.
Course correction (CC) is aimed at adjusting
apply to
unit process &
unit process
parameters in a process or process sequence such that
process sequence
targets metrics are maintained in the presence of
corrects for
long-term drift
process drift and short-term variability. In principle,
Real-time
emerging
mainline
CC may be accomplished as run-to-run (or wafer-tometrology
real-time/in-situ
wafer) feedback and/or feedforward control, or as realcontrol
real-time
equipment stop,
time control within a unit process.
correction or
accelerated
Fault management (FM) is intended to detect
end-point
maintenance
equipment and process problems as soon as possible
apply to
unit process
(hopefully in real time), and then initiating corrective
corrects for
long-term drift & short-term
action, ranging from immediate shut-down and repair
variability
of equipment to a more extensive exercise in fault
classification and prognosis that leads to rescheduling
TABLE 1. Goals and modes for run-to-run and real-time
of tool maintenance.
advanced process control.
CP683, Characterization and Metrology for VLSI Technology: 2003 International Conference,
edited by D. G. Seiler, A. C. Diebold, T. J. Shaffner, R. McDonald, S. Zollner, R. P. Khosla, and E. M. Secula
© 2003 American Institute of Physics 0-7354-0152-7/03/$20.00
583
for short-term random variability within a process step,
as well as for long-term process drift, thus achieving a
higher degree of overall course correction APC. In
addition, sensors suitable to drive real-time metrology
may be capable of serving both real-time course
correction and fault detection goals simultaneously.
An early, major success for APC was IBM's
application [20] of in-situ residual gas analysis (mass
spectroscopy) to detect faults in process equipment,
terminate the process, and alert the operator and/or
engineer, thereby avoiding further yield loss. Since a
number of important equipment failure modes are well
understood and readily distinguishable this way (e.g.,
gas source impurities from inadequate purging upon
gas bottle change), this real-time fault management
mode has provided substantial return and has been
broadly implemented in manufacturing, as indicated by
"mainline" in Table 1. More sophisticated fault
detection algorithms have been reported. [21]
Expanding fault management to more complex
fault mechanisms, equipment health prognosis, and
optimized
maintenance
scheduling
is
more
challenging: in the general case, multiple sensor
signals, system modeling, and probabilistic risk
assessment may be required to classify faults, specify
likely origins, and predict when they will demand
repair. On the other hand, this more general FM goal
can benefit from in-line and off-line as well as realtime/in-situ metrologies, since they deliver trend
information helpful to both classification and
prognosis. A variety of interfacing and software tools
are currently in use for data collection, integration and
algorithm application [22] to develop these emerging
strategies to the point that they can define equipment
repair actions needed and appropriate schedules for
them.
The primary thrust of course correction APC has
been in run-to-run control. [23-26] In this case in-line
or off-line measurements are used to guide process
recipe changes for succeeding wafer steps - either
subsequent wafers in the same tool (feedback) or
subsequent steps for the same wafer (feedforward).
Such approaches can detect and compensate for longterm drift of process/equipment behavior, where longterm signifies variation evident over multiple wafers,
but not detectable within a single wafer run. Indeed,
the Sematech J-88 program was aimed specifically at
run-to-run control, and since then run-to-run control
become mainline, with substantial success and
adoption in manufacturing for feedback, feedforward,
and multi-step applications. [27-29] This is due in
significant part to the availability of in-line and off-line
metrology technology, which is a prerequisite for
development and manufacturing even without explicit
APC intentions. Platforms for APC implementation emphasizing run-to-run control - have been developed
to meet industry needs. [30]
In contrast, real-time course correction has been
limited, primarily by the fact that real-time/in-situ
metrologies with sufficient quantitative accuracy have
not been available. It is important to note that realtime/in-situ sensors for fault detection do not demand
the quantitative accuracy that is needed for CC
applications. A primary advantage of real-time CC
would be that unit process control could compensate
REAL-TIME APC
Now that important elements of APC have seen
wide acceptance, one can envision a next-generation,
i.e., real-time APC. In this scenario, quantitative realtime metrologies would drive real-time course
correction to achieve enhanced controllability,
effective for both short-term random variability and
long-term drift, and these metrologies would be
compatible with (if not identical to) those employed for
real-time fault detection. With both real-time course
correction and fault detection controlling control
responses at the unit process level (rather like
regulatory controllers), the existing benefits of
feedback and feedforward run-to-run control could be
retained at a more supervisory level of control, and
future advances in fault classification and prognosis
would ultimately add to this picture.
Over the past years our research has pursued the
development of chemical analysis techniques as realtime, in-situ process metrologies, as well as the
demonstration of these in control applications, with
primary emphasis on mass spectrometry, acoustic
sensing, and infrared (FTIR) sensors applied to
chemical vapor deposition (CVD) processes and
rate/thickness control. Currently, these methods - with
three different sensors - have demonstrated better than
1% film thickness metrology and 1.5-2% real-time
thickness control, and they have broad application
(e.g., etching, heterostructures, etc.). Opportunities for
sensing and controlling spatial uniformity - though
longer-term - are also in view.
And similar
approaches to real-time sensor-based control have been
shown, e.g., in the control of MOCVD source delivery
for difficult materials (solids with low vapor pressure).
This paper provides examples of such progress toward
real-time APC in the hope that the next steps of
technology transfer to manufacturing can be taken.
MASS SPEC METROLOGY
Initial investigations using in-situ mass
spectrometry (mass spec) clearly revealed timedependent behavior of reactive gases in CVD [31] and
plasma [32, 33] processes. For example, RTCVD
poly Si and oxide results showed generation of reaction
products and depletion of reactants to an extent
dependent on reaction rate, and they indicated the
possibility of using these for deposition rate metrology
[34] . More recently, we have evaluated the metrology
accuracy obtainable using downstream mass spec for
several W CVD processes, both H2 reduction [35, 36]
and SiH4 reduction [37], as carried out in a Ulvac
ERA-1000 W CVD cluster tool. With reasonable
reactant conversion rates (-20%) in the SiH4 reduction
584
process, thickness metrology of 1-1.5% accuracy was
demonstrated and exploited to achieve real-time end
point control consistent with this metrology. [37]
The experimental setup is indicated in Fig. 1,
showing differentially pumped mass spectrometry
(Inficon Transpector™) arranged to sample reactant
and product species in the CVD reactor. As indicated
by the time-dependent HF product signal in Fig. 2 for
the H2 reduction reaction, the sensor system actively
process gases to determine dynamics through the
process cycle. These in turn are consistent with
dynamic simulations of equipment, process, and sensor
systems. [38]
Ulvac ERA-1000 Selective
W CVD Chamber #2
H2/SIH4
Lamp Heater
The SiH4 reduction process for W CVD,
3 SiH4 + 2 WF6 => 6 H2 (g) + 2 W (s) + 3 SiF4 (g),
produces both H2 and SiF4 as reaction products. For a
low pressure (0,1 torr) process, a thickness metrology
of 1-1.5% accuracy was obtained using the H2 product
generation signal. [37]
By tracking the integrated
product signal and terminating the process when the
target value was reached, run-to-run variability was
reduced from ~4% to -1.5%. Figure 3a shows the
improvement for the mass spec product signal upon
imposing real-time end point control, while Fig, 3b
shows the corresponding improvement in control of the
deposited W film thickness, as determined post-process
Orifices
iiiiiiiiiiiiiiiiiiunnimiiiiiiii
Closed Ion
Source
200AMU QMS
Manifold
Electronics Box
Showerhead
M-
WF6
Wafer
Orifice
High-Conductance
Pressure Control Valve
Differential
Pumping
Diaphragm
Pump
WF6 By-Pass
To Mech
Pumping Stack
by weight measurements. In these experiments, "no
control (experiment)" corresponded to a sequence of
wafers where no end point control was imposed; "no
control (estimate)" reflects an estimate of what the
values would have been if end point control had not
been imposed, judging from the signal levels observed.
At higher pressures (10 torr) the quality of the
thickness metrology is even better, which is important
because W CVD processes in manufacturing operate at
100-500 torr. [36]
Using the HF product signal from
the H2 reduction reaction, a linear regression fit to the
weight (thickness) vs. integrated HF product signal
yielded a standard deviation of 0.67%
uncertainty, as
indicated in Fig. 4. These results indicate that mass
spectrometry is a viable sensor-based metrology for
real-time APC.
FIGURE 1. Mass spec sampling of W CVD process.
/
r
'
r
Reactor Total Pressure
i
•
i
*
i
WF6&H2
AN2
i
i i
•
i
'
/
i
i
|
r*-
A
1x10*1000
1200
1400
1600
1800
Time (sec)
2000
2200
2400
2600
FIGURE 2. HF product signal through process cycle using
H2 reduction process for W CVD, i.e., 3 H2 + WF6 => 6
HF(g) + W(s).
585
no control (estimate)
with control
MASS SPEC FOR ULTRATHIN
LAYERS
5.0
o
4.0
3.0 -
There is an increasing need for thickness of
ultrathin layers (in the 10 nm range), driven by specific
applications (e.g., barrier and seed layers, gate
dielectrics).
Mass spectrometry based metrology
shows promise for such applications. Figure 5 shows
mass spec signals for the initial stages of H2/WF6 CVD
reaction (H2 reduction) on the Si surface. In this case,
the H2 reduction reaction is considerably slower than
direct reaction of the WF6 reactant with the Si surface
(the Si reduction reaction), which generates a SiF4
volatile product. [3 9] The Si reduction reaction
continues until the initial Si surface is completely
covered by W, at about 30 nm thickness.
Otarget H 2 value
no control '(experiment)
1.0 0.0
1
2
3
4
5
6
7
8
9
Wafer number
FIGURE 3a. Real-time end point control of integrated H2
reaction product signal for SiH4 reduction W CVD process.
FIGURE 3b. Real-time end point control of film weight
no control (estimate)
with>control
6.0X1012
SiF4 product from
Si reduction reaction
0.04 -
"hot" wafer
HF signal
O)
-***®Z&^*K**&*-K
0.03 -
O)
no control (experiment)
0.02 -
0.01 -
0.00
1
2
3
4
5
6
7
8
2.0
9
time (min)
Wafer number
FIGURE 5. Mass spectrometry product signal for Si
reduction reaction in forming 30 nm W on Si surface.
corresponding to Fig. 3 for SiH4 reduction W CVD process.
The sharp SiF4 peak in Fig. 5 demonstrates that
real-time mass spec has sensitivity in the 10 nm range,
suggesting a thickness metrology for such ultrathin
layer applications of order 5% precision or better. This
suggests at least two key areas of application. First, it
may mean that mass spec metrology will be useful for
ultrathin barrier, seed, or gate dielectric applications.
Second, it shows that the approach can certainly reveal
faults, such as inadequately cleaned contacts where the
shape and intensity of the resulting peak changes
considerably if the CVD chemistry is reasonably
selective (e.g., reduced deposition rate on oxide cf. Si).
,=, 260-
E
Linear Regression
Average Uncertainly +/- 0.56%
Standard Deviation 0.67%
il
0
ACOUSTIC METROLOGY
10* 2.1X10"5 2.2X10"8 2.3X10"5 2.4x10* 2.5x10* 2.6x10* 2.7x10* 2.8x10*
Integrated HP Signal (A-s)
Acoustic sensors enable the determination of gas
composition from the measured velocity of sound in
the gas. Given the charactistic product generation and
reactant depletion which occur in processes like CVD,
this implies that acoustic sensors should also be
capable of reaction rate and thickness metrology. In
fact, real-time in-situ metrology for deposition
thickness has been demonstrated at a comparable level
of accuracy using acoustic sensors. [40, 41]
FIGURE 4. Mass spectrometry based thickness metrology
for H2 reduction W CVD process at 10 torr pressure.
586
(frequency integrated signal, Hz-s) in Fig. 7 predicts
the deposited W film weight to about 0.5%,
comparable to the mass spec result.
valve
Gas
4^
FTIR METROLOGY
V
11
Finally, optical sensing, carried out using Fourier
transform infrared spectroscopy (FTIR) has been used
to monitor reactant depletion and product generation in
these CVD processes. [42] In this case the depletion of
WF6 is more sensitive than the generation of HF
reaction product. As shown in Fig. 8, the integrated
FTIR absorption peak of WF6 provides an in-situ
thickness metrology accurate to about 0.5%, i.e.,
comparable to the mass spec and acoustic sensor
results.
it
lit
gimp
o
in 680
to
640
0.270.260.25-
R=-0.99672
660
FIGURE 6. Acoustic sampling of W CVD process.
__-*— — «"^*^
__^t— -—• "\_____
620
- 17000 .
Run numbers
B406°C
600
•^~"
^
2Jf 0.24-
40tfC
40CfC
41CfC
- 16000
O)
5
580
0.23-
- 15000 '
0.14 0.15 0.16 0.17 0.18 0.19 0.20 0.21
£ 0.22-
0.2080C)00
Mass of deposited tungsten (g)
- 14000
0.21-
85000
90000
410°C
FIGURE 8. FTIR sensor based thickness metrology for H2
reduction W CVD process at 10 torr pressure.
95000
Frequency integrated signal (Hz.s)
WAFER STATE METROLOGY FOR
REAL-TIME APC
FIGURE 7. Acoustic sensor based thickness metrology for
H2 reduction W CVD process at 10 torr pressure.
The three sensors described above are, strictly
speaking, process state sensors, yet by suitably
monitoring the dynamic state of the process and
recognizing that the process conditions reflect wafer
state conditions, all three generate a wafer state
thickness metrology of order 1% accuracy or better,
seemingly sufficient to deliver value in manufacturing.
In addition, at least one of them - mass spectrometry is already in widespread use for real-time fault
detection, indicating that it could provide dual use to
real-time APC. Indeed, we have exercised mass
spectrometry for fault detection purposes in concert
with its role in thickness metrology for real-time
course correction.
While these techniques have achieved the
metrology accuracy originally sought, they must be
used carefully. For example, they reflect the total
material deposited, though some of this could be on
other internal surfaces of the reactor (e.g., wafer chuck,
chamber walls). In general, they require pressure
Acoustic sensing requires pressures sufficiently
high to sustain acoustic waves in the medium, typically
50-100 torr, from which the measured sound velocity
reflects the composition of the gas mixture. The
experimental arrangement to achieve this is depicted in
Fig. 6, where in this case the 10 torr pressure for the H2
reduction W CVD process required pressure
transduction to higher pressure in the sensor, which in
turn was achieved using gas compression by a
diaphragm pump.
The sensor was an Inficon
Composer.™
The metrology accuracy of the acoustic sensing
technique for the 10 torr H2 reduction W CVD process
(as reflected in Fig. 4) is shown in Fig. 7. [40, 41] In
this case, 10 wafers processed at 490°C showed a
wafer-to-wafer drift, as evident in the Fig. 7. Though
the algorithm for extracting a metrology signal from
the acoustic sensor data is rather more complex than
that for the mass spec data, the metrology metric
587
accurate metrology for real-time APC than we have
achieved in the research cited here.
CVD is a pervasive semiconductor manufacturing
process and thickness control is important, so this
example may serve as a useful entry point for
development and integration of such sensors into a
larger real-time APC system. However, semiconductor
technology demands a considerably broader set of
processes as well as different kinds of metrology.
Therefore, one should consider the prognosis for
expansion of a real-time APC capability.
Other chemical processes are certainly amenable
to the same or similar techniques. Besides other
thermal CVD processes, these methods can be applied
to plasma-enhanced CVD and plasma etching, as
already demonstrated. [32, 43] But the general notion
of sensing chemical reaction signatures for chemical
processes is much broader. For example, we have
begun investigations of the curing process used to
create nanoporous low-K dielectrics by spin-casting,
using time-of-flight secondary ion mass spectrometry
(ToF-SIMS) [44]. The properties of the material
depend sensitively on the details of how the sacrificial
porogen
species are volatilized (they are the
placeholders for the nanopores that will remain in the
low-K matrix). Initial results clearly show the kinetics
of porogen depletion with curing (annealing), and we
have begun experiments to identify the volatile
products directly through thermal desorption mass
spectrometry.
transduction systems to transform the sampled gas
from the process pressure used to the pressure regime
needed by the sensor, e.g., lower pressure for mass
spectrometry and higher pressure for acoustic sensing.
And of course they do not indicate across-wafer
uniformity, an equally important metric for
manufacturing productivity, but probably less sensitive
to sources of process variability.
Nevertheless, these results are very promising in
providing a base for expansion of APC to real-time
APC. The problems of implementing sophisticated
sensors like mass spectrometry have already been
surmounted for real-time fault detection, and important
issues of sensor conditioning and reliability have been
dealt with.
In some sense, there remain two key issues for
transfer
of these real-time metrologies to
manufacturing. First, in the context of pervasive runto-run course correction applications and existing realtime fault detection, how will real-time course
correction be integrated into the larger APC context?
This may be a significant architecture issue, though not
a fundamental obstacle given that real-time
quantitative metrologies like these would primarily
drive real-time feedback control at the unit process
level, thereby functioning more like regulatory
controllers within a larger supervisory control system
which executes feedforward as well as feedback
control involving multiple process steps and tools.
Second, what will be the accuracy of these
metrologies when implemented in a manufacturing
tool? Of course this can only be judged by experiment,
and it should be emphasized that this must be the next
step. While we have processed and tested 10-20 wafers
per day in our experiments, it is doubtful that our
academic research environment (or any other) can add
much more in evaluating these techniques for
manufacturing. Technology transfer to a development
or manufacturing environment is the essential next
step, where considerably larger wafer lots can be
processed through real manufacturing tools.
Nevertheless, we can make some predictions
based on our experience. A major hurdle in achieving
the metrology accuracy reported here has been the
presence of wall reactions (e.g., WF6 adsorption and
reaction with subsequent H2 to form HF), and the
concomitant need to condition the reactor system
(chamber, gas lines, etc.) in order to stabilize these
factors, and when this is done the results are very
favorable. In manufacturing tools, where wafers are
processed at high rate over long periods of time, this
conditioning is substantially guaranteed. Furthermore,
when variations from continuous operation of
manufacturing tools are made (e.g., preventive
maintenance), the tools undergo conditioning
procedures (e.g., monitor wafers for recalibration,
recognition of first-wafer effects, etc.). Because the
regularity of the equipment environment is much
higher in manufacturing, we can only predict more
UNIFORMITY CONTROL
While most in-situ sensors target the average
behavior across the wafer, or a single point on the
wafer, across-wafer uniformity is a key metric for
manufacturability and an intense focus in process
development. Clearly, it would be highly desireable to
extend real-time process and wafer state metrology to
encompass uniformity measurements for real-time
APC.
Some sensor approaches are promising in this
regard.
Spatially
resolved
optical
emission
spectroscopy and multichannel laser interferometry
have been used to achieve process and wafer state
uniformity measurements in plasma etching processes
[45], and the latter has led to development of real-time
interferometric
imaging
instruments.
[46]
Ellipsometry has been employed using in-situ etch
rates measured at a single point on the wafer together
with offline multi-point measurements and response
surface modeling for etch uniformity control. [47]
Transient behavior at end point sensed by real-time
mass spectroscopy has distinguished signatures of
uniformity. [43]
We have been exploring a new concept in
equipment design aimed at uniformity control, namely
spatially programmable reactor design. The goal is
reactor designs which ultimately enable spatial
uniformity to be modified through arrays of sensors
and actuators, so that the performance characteristics
588
of the reactor are software-programmable. Such a
paradigm would provide for spatial arrays of process
and wafer state sensors, or multiplexing of spatial
sampling to such sensors, along with arrays of
actuators and control systems, to accomplish spatial
control of the process in run-to-run and real-time
control modes.
Programmable reactor designs would enable three
broad applications: (1) programmable uniformity,
assuring that manufacturing requirements for
uniformity can be achieved at any process design point
which is sought for materials performance; (2)
programmable nonuniformity, in which intentional
nonuniformity is introduced to expedite experimental
process optimization, essentially a "one-wafer designof-experiments"; and (3) combinatorial materials
discovery, in which gradients of stoichiometry are
intentionally introduced to accelerate the development
of complex new materials.
The initial testbed for this concept is the
development of a spatially programmable CVD
showerhead, applied initially to W CVD under NSF
ITR sponsorship. [48] The current prototype is a threezone showerhead [49] to test the approach, evaluate
novel design concepts, and validate models [50].
Future embodiments will involve higher levels of
integration and MEMS devices for sensors and
actuators.
We have applied acoustic sensing for real-time
control of such problematic precursors, particularly one
of the most challenging - solid Cp2Mg, with a very low
vapor pressure (0.04 torr at 25°C). This materials is
important in optoelectronics materials growth. The
real-time APC setup is shown schematically in Fig. 9.
The inert H2 carrier gas draws precursor from the solid
source, which in turn is mixed with a H2 diluent
stream, after which the composition of the mixture is
sensed by the acoustic sensor.
Carrier gas flow set point
50 g. Cp2Mg
solid source
FIGURE 9. System for real-time control of solid source
delivery using acoustic sensor.
_ 0.020-,
0.016
o
o
:
EQUIPMENT SUBSYSTEMS CONTROL
:§ 0.012
Major manufacturing tools are complex equipment
systems, comprising not only the reactor environment
in which processes modify the wafer, but also
equipment subsystems for reactant delivery, exhaust
management, wafer transport, and other functionalities.
Delivery and control of reactive gas species has been a
notorious problem, e.g., relating to mass flow
controller reliability.
More recently, the management of delivery and
control of liquid and solid sources has become more
important due to the profusion of new and complex
materials needed to support the ITRS roadmap.
Delivery of liquid metallo-organic CVD (MOCVD
precursors has been accomplished by standard bubbler
techniques, in which an inert carrier gas is bubbled
through the liquid source to take up its saturation vapor
pressure of the source material for delivery to the
reactor. More recently, liquid delivery systems have
become common, in which the liquid is delivered to
the reactor and vaporized as it enters.
While
reproducible liquid delivery remains a challenge,
perhaps the most difficult is the use of solid sources
with low vapor pressure, where the precursor delivery
rate is destabilized by variability e.g., in temperature or
in the active area of the powder source as the precursor
is consumed. Indeed, because of such difficulties
suppliers have adopted the alternative of dissolving
such sources in organic solvents and then using liquid
delivery systems, but this raises new issues of
contamination and chemical complexity.
o
"co
t 0.008
60
0.004
Q_
O
0.000
0
40
80
120
160
200
40
Time (min)
FIGURE 10. System for real-time control of solid source
delivery using acoustic sensor.
Results in Fig. 10 show the performance of this
real-time control system.
With a low Cp2Mg
concentration (0.01 mol-%) as the target, the source
temperature was varied from 40 to 32°C, causing a
50% decrease in vapor pressure, to simulate a major
disruption of the precursor delivery rate. With the
acoustic sensor providing a real-time composition
metrology and driving a controller for both H2 streams,
precursor delivery rates can be controlled to within 1%
of their targets.
This source delivery control strategy should be
widely applicable, not only to the most difficult of
precursors like solid Cp2Mg, but to a broad range of
liquid and solid precursors as well. Such sensors can
accomplish both real-time course correction and fault
detection. And the ability to control delivery of exotic
chemical precursors is particularly timely in this era of
589
new multicomponent materials for semiconductor
technology.
ACKNOWLEDGEMENTS
The author is extremely grateful to his
collaborators and students for their contributions and
cooperation over some years of work on in-situ
metrology and real-time APC, especially to Laurent
Henn-Lecordier, John Kidder, Theodosia Gougousi,
Yiheng Xu, Soon Cho, Charles Tilford, and in the
earlier phases Laura Tedder and Guangquan Lu. He is
also appreciative of many discussions on sensors,
metrology, and APC with B. Ellefson, L. Frees, C.
Gogol, A. Wajid, S. Butler, G. Barna, A. Diebold, B.
van Eck, and J. Hosch. We are grateful for financial
support from NIST, NSF, SRC, Texas Instruments, and
Inficon.
CONCLUSIONS
Advanced process control, only a vision a decade
ago, has now become a mainstream thrust for
semiconductor manufacturing.
Its two areas of
primary acceptance and success are in run-to-run
control and real-time fault detection. Run-to-run
control relies on continuing advances in in-line and
off-line metrology tools, whose technology
advancement is already required to meet the future
nodes on the ITRS roadmap. In contrast, real-time
fault detection demands real-time/in-situ sensors;
however, high quantitative accuracy is not required,
since the sensors are intended only to quickly identify
known faults with serious consequences.
Improvements in quantitative accuracy of realtime, in-situ sensors opens the door to a full
complement of real-time APC. High accuracy sensors
which resolve chemical or spectroscopic information
promise to resolve subtle changes in process and to
enable course correction in real time. Amidst the
various available in-situ sensor technologies, we have
demonstrated that chemical sensors of process state can
reveal wafer state consequences associated with
reactions occurring in the process module, leading to
wafer state metrologies. For mass spectroscopy,
acoustic sensing, and FTIR we have demonstrated
thickness metrology with accuracy of 1% or better,
certainly suitable for manufacturing requirements, and
in the case of mass spectroscopy we have demonstrated
its application in real-time end point control. The
process spectrum available with these sensors is
considerably broader than the thermal CVD example
addressed here, and other in-situ approaches (e.g.,
optical [51, 52]) add to the available sensor portfolio
for real-time APC.
With research to date indicating that in-situ
metrology can support real-time course correction, it is
time to move these efforts into a manufacturing
environment. Our experience to date suggests that
their performance will be even better because of the
high volume conditioning which manufacturing
equipment experiences.
New directions for research emphasis are
necessary and evident for in-situ metrology and realtime APC.
One is certainly in development of
spatially resolved sensors which can determine acrosswafer uniformity, or possibly in new concepts for
spatially programmable reactor design. Another is in
employing in-situ metrologies for real-time APC in
equipment
subsystems,
where
manufacturing
variability and fault generation is often found. To this
end, the application of acoustic sensing to a broad
range of precursor delivery systems seems an excellent
example of focusing attention on the larger equipment
system, rather than solely around the wafer.
REFERENCES
[I] Melliar-Smith, M. and Diebold, A. C., "Metrology
Needs for the Semiconductor Industry Over the Next
Decade," in Characterization and Metrology for VLSI
Technology: 1998 International Conference, vol.
CP449, Walters, E. J., Ed.: The American Institute of
Physics, 1998, pp. 3-20.
[2] Diebold, A. C., Solid State Technology, 45, 48, Jan
2002
[3] Butler, S. W.,J. Vac. Sci. Technol. B, vol. 13, pp. 19171923, 1995.
[4] Butler, S. W., Hosch, J., Diebold, A. C., and Van Eck,
B.,2,pp. 315-321, 1997.
[5] Barna, G. G., Loewenstein, L. M., Robbins, R., Obrien,
S., Lane, A., White, D. D., Hanratty, M., Hosch, J.,
Shinn, G. B., Taylor, K., and Brankner, K.Jeee T
Semiconduct M, vol. 7, pp. 149-158, 1994.
[6] Barna, G. G., Loewenstein, L. M., Brankner, K. J.,
Butler, S. W., Mozumder, P. K., Stefani, J. A., Henck,
S. A., Chapados, P., Buck, D.? Maung, S., Saxena, S.,
and Unruh, A.,J. Vac. Sci. Technol. B, vol. 12, pp.
2860-2867, 1994.
[7] Maung, S., Banerjee, S., Draheim, D., Henck, S., and
Butler, S. W.Jeee T Semiconduct M, vol. 7, pp. 184192, 1994.
[8] Moslehi, M. M., Davis, C., and Bowling, A., pp. 44-63,
1992.
[9] Sofge, D. A., "Virtual Sensor Based Fault Detection and
Classification," 1995.
[10] Diebold, A. C., "Metrology Roadmap: A Supplement to
the National Technology Roadmap for
Semiconductors," Sematech, Austin, TX Technology
Transfer #94102578A-TR, 1995.
[II] Diebold, A. C., presented at 1995 IEEE/SEMI
Advanced Semiconductor Manufacturing Conference,
Gaithersburg, MD, 1995.
[12] Diebold, A. C., presented at Semiconductor
Characterization: Present Status and Future Needs,
Gaithersburg, MD, 1995.
[13] Chism, W., Diebold, A., Canterbury, J., and Richter,
C.,Sol St Phen, vol. 76-77, pp. 177-180, 2001.
[14] Advanced Equipment Control / Advanced Process
Control (AEC/APC) conference,
http: //www. sematech. or g/publ i c/ne ws/conferences/aeca
pc/index.shtml
[15] AVS Manufacturing Science and Technology Group,
www.avs.org
[16] Integrated Measurement Association,
www. integratedmeasurement. com
590
[17] Butler, S. W., Solid State Technology, 42, 33-+, JAN
[18] Butler, S. W.,J. Vac. Sci. Technol. B, vol. B13, pp.
1917-1923, 2000.
[19] Helms, B., presented at AEC/APC Symposium XII,
2000.
[20] Reath, M., presented at AEC/APC Symposium XII,
2000.
[21] Lee, S. F., Boskin, E. D., Liu, H. C., Wen, E. H., and
Spanos, C. J.JEEE Trans. Semi. Mfg, vol. 8, pp. 17-25,
1995.
[22] Sanders, M., presented at AEC/APC Symposium XII,
2000.
[23] Stefani, J., Rao, S. P., Doke, N., Larsen, J., Comstock,
S., and Paquette, B., presented at AEC/APC Symposium
XII, 2000.
[24] Sruber, J. D., Pagette, F., and Tang, S., presented at
AEC/APC Symposium XII, 2000.
[25] Toprac, A. J., Campbell, W. J., and Purdy, M., presented
at AEC/APC Symposium XII, 2000.
[26] Stefani, J., Poarch, S., Saxena, S., and Mozumder, P.
K.JEEE Trans. Semi. Mfg, vol. 9, pp. 366-383, 1996.
[27] Toprac, A. J., Sonderman, T., Peterson, A. O., and
Miller, M., presented at AEC/APC Symposium XII,
2000.
[28] Passow, M., presented at AEC/APC Symposium XII,
2000.
[29] Atkins, N., presented at AEC/APC Symposium XII,
2000.
[30] Sonderman, T. J., presented at AEC/APC Symposium
XII, 2000.
[31] Tedder, L. L., Rubloff, G. W., Shareef, I., Anderle, M.,
Kim, D. H., and Parsons, G. N.,J. Vac. Sci. Technol. B,
vol. 13, pp. 1924-7, 1995.
[32] Chowdhury, A. I., Read, W. W., Rubloff, G. W.,
Tedder, L. L., and Parsons, G. N.,J. Vac. Sci. Technol.
B,vol. 15, pp. 127-32, 1997.
[33] Srinivasan, E. and Parsons, G. N.,J Appl Phys, vol. 81,
pp. 2847-2855, 1997.
[34] Tedder, L. L., Rubloff, G. W., Cohaghan, B. F., and
Parsons, G. N.,J Vac Sci Technol A, vol. 14, pp. 267270, 1996.
[35] Sreenivasan, R., Gougousi, T., Xu, Y. H., Kidder, J.,
Zafiriou, E., and Rubloff, G. W.,J. Vac. Sci. Technol. B,
vol. 19, pp. 1931-1941,2001.
[36] Cho, S., Henn-Lecordier, L., Liu, Y., and Rubloff, G.
W.,J. Vac. Sci. Technol. B, vol., (submitted).
[37] Xu, Y., Gougousi, T., Henn-Lecordier, L., Liu, Y., Cho,
S., and Rubloff, G. W.,J. Vac. Sci. Technol. B, vol. 20,
pp. 2351-2360, 2002.
[38] Lu, G. Q., Bora, M., Tedder, L. L., and Rubloff, G.
W.,IEEE Trans. Semi. Mfg, vol. 11, pp. 63-74, 1998.
[39] Gougousi, T., Xu, Y., Kidder, J. N., Rubloff, G. W., and
Tilford, C. R.,18, pp. 1352-63, 2000.
[40] Henn-Lecordier, L., Kidder, J. N., Rubloff, G. W.,
Gogol, C. A., and Wajid, A.,J. Vac. Sci. Technol. B,
vol., (in press).
[41] Henn-Lecordier, L., Kidder, J. N., Rubloff, G. W.,
Gogol, C. A., and Wajid, A.,J. Vac. Sci. Technol. B,
vol. 19, pp. 621-6,2001.
[42] Singhal, A., Henn-Lecordier, L., Kidder, J. N., Gogol,
C., and Kushneir, J. F., presented at AVS National
Symposium, San Francisco, 2001.
[43] Chambers, J. J., Min, K., and Parsons, G. N.,J. Vac. Sci.
Technol. B, vol. 16, pp. 2996-3002, 1998.
[44] Lazzeri, P., Vanzetti, L., lacob, E., Bersani, M.,
Anderle, M., Park, J. J., Lin, Z , Briber, R. M., Rubloff,
G. W., and Miller, R. D., presented at 2003 Intl. Conf
on Characterization and Metrology for ULSI
Technology, Austin, TX, 2003.
[45] Economou, D., Aydil, E. S., and Barna, G., Solid State
Technology, 34, 107-111, APR
[46] Zhang, H. and Nikolaou, M., presented at AEC/APC
Symposium XII, 2000.
[47] Stefani, J. and Butler, S. W.,J Electrochem Soc, vol.
141, pp. 1387-1391, 1994.
[48] http://www.isr. umd. edu/Labs/C AC S E/researcn/progrxr/
[49] Liu, Y., Choo, J., Henn-Lecordier, L., Rubloff, G. W.,
and Adomaitis, R. A., presented at AVS 49th National
Symposium, Denver, CO, 2002.
[50] Choo, J.-O., Adomaitis, R. A., Rubloff, G. W., HennLecordier, L., and Liu, Y., presented at AIChE Annual
Meeting, Indianapolis, IN, 2002.
[51] Huang, H.-T., Lee, J.-W., Stutzman, B. S., Klimecky,
P., Garvin, C., Khargonekar, P. P., and Terry, F. L.,
presented at AEC/APC Symposium XII, 2000.
[52] Hosch, J. W., Benson, K., Celii, F., and Harvey, K. C.,
presented at AEC/APC Symposium XII, 2000.
591