send-ahead wafers. The ability to forecast optimum offset commands
lessens the need for send-ahead wafers. (Dedicated routing, send-ahead
wafers, and nonproduct wafers are all costly.)
stepper availability. Improved overlay and CD control reduces
rework rates and the need for send-ahead and nonproduct wafers, increasing
design-rule programs. Reduced overlay and CD variation enables
fabs to accelerate the introduction of smaller design rules.
control offers well-documented benefits.13 Nevertheless,
it is not a panacea. In photolithography, it has limitations in the
A hallmark of feedback control is its ability to automatically compensate
for short-term exposure-tool drifts. However, the full population
of exposure tools must still be anchored regularly to a fixed reference
using a traditional matching methodology. Failure to do so results
in the decoupling of subgroups from the main group, leading to an
increase in high-frequency noise entering the R2R controller and subsequently
limiting the effectiveness of the control strategy.
When there is a sudden and transient shift in process or tool
state caused by a source outside the observable realm of the R2R controller,
an overcompensation condition can arise, negatively affecting a small
percentage of lots. However, when configured properly, feedback control
minimizes overcompensation effects, improving average lot-to-lot performance.
The positive performance of the great majority of lots far outweighs
the poor performance of a very small number of lots.
rates. In most cases, R2R control results in reduced rework rates.
However, if the rework rate is already quite low (less than 1%, for
example), feedback control may not have a significant impact. Low
rework rates may indicate that the production line is not fully using
tool capability, using too many send-ahead or nonproduct monitor wafers,
being controlled through the intense manual efforts of knowledgeable
engineers, or experiencing a hidden yield issue where the metric used
to determine the rework decision does not properly correlate to true
overlay limited-yield criteria. Even when rework rates are low, R2R
control is still beneficial because it corrects for future observed
shifts and drifts without manual intervention.
errors. Feedback control calculates optimum job-offset commands
for each exposure event so that systematic errors are driven close
to zero. Most remaining errors can be attributed to noncorrectable
residual sources (pattern placement errors on production wafers).
Even under ideal conditions, processes and exposure tools are likely
to generate noncorrectable residuals which are not correctable by
any available combination of job-offset commands. Noncorrectable residual
errors are caused by lens distortion, photomask pattern placement
errors, metrology errors, random stage errors, boustrophedonic stage
errors, wafer warping, or nonuniform expansion. In addition, the dynamic
nature of the disturbances that cause overlay error results in a time
delay between process, measurement, and control, causing high-frequency
disturbances whose period is equivalent to the time delay being left
the R2R Controller Works
Optimizer. The feedback optimizer (FBO) is an application containing
a set of automated algorithms to simulate the way the R2R controller
handles feedback requests. FBO operates on a static data set that
contains process stream information, exposure parameters, and modeled
error parameters. Once the data set has been imported into the tool,
the user can optimize filters, warning limits, and weighted moving
average through a series of plots and menus, as illustrated in Figure
1. As shown in Figure 2, optimization decisions are simplified by
reviewing FBO-estimated overlay improvement values. These values have
proven to be quite accurate when compared with real-world control
|Figure 1: Screen capture showing
run-to-run controller's feedback optimizer.
with a static data set to optimize the closed-loop R2R controller
has several advantages:
learning cycles. Filter and warning limits can be tried and tested
off-line rather than during production. Improper selection of these
limits can result either in false failures, which translates into
unnecessary equipment downtime, or failure to detect real problems.
2: Screen capture showing estimated improvements in overlay
operation. Experimenting with weighted moving average as a function
of the time delay between the exposure event and the corresponding
metrology measurement allows the R2R controller to be optimized for
performance without suffering from time-delay sensitivities.
configuration setup. Wildcarding and default settings simplify
the configuration setup. By analyzing data off-line, the user can
determine wildcarding rules that allow similar process streams to
be combined and determine default settings that can be globally applied
to many process streams.
to test new functionalities. Advancements are continually being
made in lithography R2R controllers. Simulating new functionalities
in off-line mode allows the user to test the applicability of new
functionalities for their specific process environment.
to determine active control. Improvement estimates are made for
each correctable parameter. When a parameter exhibits a significant
amount of high-frequency noise, the R2R controller may actually degrade
performance. In such situations, it may be appropriate to disable
active control until the root cause of the noise is isolated and corrected.
R2R Control. The first type of data collected by the control system
is the actual vector (A), which represents the actual process conditions
used at the exposure step.
production wafers undergo the exposure step, some or all of them are
measured using one or more metrology tools. The schematic diagram
of an R2R control system in Figure 3 illustrates the point at which
the measurement step takes place. Measurements, in the form of raw
data, must be converted into a form that is useful for process control.
The R2R controller performs that conversion by applying an analysis
function to the raw data, resulting in the computation of an error
vector (E). This vector describes the error in the input settings
that were used to process the wafers that have just been measured.
Error vector calculations are based on the settings of the process
and tools that are being controlled. To ensure that only valid metrology
events are considered for feedback, raw-data-quality filters (validation
rules) are applied to the analysis. Examples of such filters may include
the number of successfully measured raw data points > N and process
events marked as ignored for feedback processing.
3: Schematic diagram of an R2R control system illustrating the
points at which the measurement step takes place and the vectors
some cases, values contained in the error vector can exhibit a scaling
or sign difference. This problem is corrected by using a variable-gain
amplifier in the feedback loop. When the gain vector is applied, the
error vector takes on the new form of Eg.
a given exposure, the R2R controller collects the actual vector and
error vector at different times. The actual vector generally arrives
immediately following the completion of the exposure step, while the
error vector is not available until the measurement function has been
completed. Depending on when the metrology measurement occurs, the
metrology time delay can be as short as 1 hour or longer than 24 hours.
A process called store matching is used to link the two vectors together
in the database.
matching of the actual and error vectors ensures that the ideal vector
can be computed. The ideal vector is the corrected vector that takes
errors into account and determines the ideal command, which eliminates
the errors caused by the sum of process, tool, and photomask variations.
The ideal vector is the difference between the actual vector and the
error vector (I = A E). If it were possible to go back in time
and use the error vector value to modify the job-offset command, wafer
measurements would not reveal any systematic errorsthe error vector
would be zero and all errors remaining on the wafer would be noncorrectable
residual ones. But because it is not possible to go back in time,
the R2R controller uses a historical correlation method to apply past
ideal vectors to current forecasting requests.
compare events with similar characteristics, the controller uses a
process-stream correlation method in which each lot at a particular
process event belongs to a stream of lots that have experienced (or
will experience) similar events. When ideal vectors are calculated,
the correlation algorithm calculates a value for the command vector
(CFB) by considering the available history of
the previously calculated ideal vectors. For example, by measuring
wafers that have gone through the same stepper at different times,
the user can determine how processing has changed over time.
fab operations present complex correlation challenges, the values
of the error and command vectors by themselves cannot accurately forecast
the correct value of the ideal vector. Therefore, a correlation algorithm
that uses a history of vectors to calculate the command vector is
needed. Manufacturing characteristics requiring multiple control loops
governed by multiple correlation algorithms (not explicitly shown
in Figure 3) include:
offsets. Photomask-induced variations pose a special challenge
to the feedback control loop because they are large and not as frequently
observed as exposure-tool and process variations. For example, in
high-part-count fabs, some photomasks are used only for one or two
lots per month. Moreover, errors introduced by photomasks may vary
from one exposure tool to another. Fortunately, photomask-induced
errors are static and can be estimated once they are observed. Such
errors are controlled by introducing an outer control loop that removes
the photomask bias from the main control process.
bias. Setpoint bias controls the lithography process so that it
regulates to a nonzero error value. A typical example is a known bias
between a postdevelop measurement and a postetch measurement, where
the postdevelop measurement can be controlled to a nonzero error in
order to compensate for the known etch-process bias.
bias. When the known quality of material entering the exposure
tool produces a known bias in the optimal command settings, the command
vector must be programmatically modified. For example, if the mean
thickness of the nitride layer on a particular lot has been measured,
a feedforward controller can create a bias rule to modify the commanded
exposure dose to compensate for a thickness difference.
the known quality of material entering the exposure tool produces
a known bias as a result of rework, the command vector can be programmatically
modified so that it is similar to the feedforward bias.
actual vector is equal to the command vector if no manual overrides
are issued. However, if a command sent to a process tool by the R2R
controller has been ignored or overridden, the wafers observed by
the measurement step are affected by the actual offsets instead of
the commanded offsets. Therefore, the definition of the ideal vector
takes into account the possibility of ignored or overridden commands.
Performance of the Automated Control System
Collection and Experimental Design. Manufacturing data from a
high-part-count fab were collected over a five-month period from an
exposure tool performing a single process. Data for the first two
months represent material that was processed under a manually intensive
feedback loop, where engineers were assigned to track and respond
to shifts and drifts in overlay performance. The R2R controller's
behavior was simulated using this data set and FBO to determine the
controller's optimal configuration settings. Based on the summary
results from FBO, the controller was activated. Data for the last
three months represent actual overlay performance under active R2R
many of the product IDs listed in Figure 4 consisted of single product
runs tailored to specific customers, it was not surprising that the
quantity of wafers in process per product ID changed dramatically
over the course of a few short months. Data for the first two months
represent the number of manual-control exposure events, while data
for the last three months represent R2R-control exposure events. All
exposure events were performed on the same 5500/700 DUV step and scan
exposure tool from ASML (Veldhoven, The Netherlands), and overlay
registration was measured on a 5200XP system from KLA-Tencor (San
4: Chart showing the product mix at a single process level.
Data for the first two months represent the number of manual-control
exposure events, while data for the last three months represent
R2R-control exposure events.
with and without R2R control was characterized by comparing overlay
results at the parameter level. The total improvement was determined
by means of a model-based summation of each individual parameter.
The accuracy of the FBO tool was characterized by comparing the estimated
improvement with the actual improvement observed under R2R control.
|Table I: Comparisons between
tool running under manual and R2R control and between actual and
Results. For all the product IDs listed in Figure 4, Table I summarizes
the overlay performance achieved with manual versus R2R control for
each correctable tool parameter. All values in the table have been
normalized to nanometers. The individual column headings are: Parameter
(individual step-and-scan overlay correctable-parameter names), Manual
Control (average parameter performance over the 60-day period of manual
process control), R2R Control (average parameter performance over
the 90-day period of R2R process control), Actual Improvement (delta
between performance with manual process control and performance with
R2R control), and FBO-Predicted Improvement. Grid orthogonality was
not activated under R2R control because a significant amount of high-frequency
noise had a negative impact on the FBO-predicted performance of that
parameter. For the other nine parameters, FBO accurately predicted
overlay improvement to within 3 nm.
improvement can be realized more directly through a model-based summation
of the individual terms. Several publications document the usefulness
and formulation of this method.4 Total overlay improvement
is listed in Table II.
|Table II: Comparison between
total and FBO-predicted improvement (based on model-based summation
of individual parameters in Table I).
Return on Investment. Because it reduces rework, decreases the
need for send-ahead wafers, increases stepper availability, and accelerates
design-rule programs, R2R control can lower fab costs. Improvements
in any one of those areas can quickly cover the cost of implementing
R2R control and increase fab profitability.5
data from this study were used to estimate the cost savings realized
by a medium-sized fab as a result of rework reduction. The rework
rate from the experimental data set is displayed in the cumulative
probability plot shown in Figure 5, where each data point represents
the cumulative probability of overlay performance. With manual control,
the observed rework rate was 8%, while with R2R control, this highly
visible metric fell to 2%.
5: Cumulative probability plot of rework rate characterization,
where each data point represents the cumulative probability
of overlay performance. The rework rate was 8% with manual control
and 2% with R2R control.
production flow conditions and, consequently, the level of rework
across all process levels varies, it is necessary to project R2R-control
cost savings as a function of rework rate using both optimistic and
conservative assumptions. The optimistic estimate assumes that the
observed reduction in rework will be the same for all process levels
and equivalent to the reduction illustrated in Figure 5. The conservative
estimate assumes that the observed rework reduction will vary across
all process levels but that the average reduction of all process levels
will be 3%. Both estimates were based on the same number of wafer
starts per year, the same number of photolithography steps and corresponding
overlay measurements, and the same single-wafer cost of ownership
incurred by performing resist strip, coat, and exposure steps. As
summarized in Table III, the optimistic and conservative estimates
of annual cost savings achieved by implementing R2R control were $2.9
million and $1.5 million, respectively.
of wafer starts per year
of photolithography steps with overlay measurement per wafer
number of photolithography steps per year
savings from rework reduction
III: Optimistic and conservative estimates of rework reduction
resulting from implementation of R2R control.
performance of a single exposure tool at a single process level was
reviewed to quantify the effects of implementing R2R control. Overlay
improvement was determined by comparing a 60-day period of manual
process control with a 90-day period of R2R process control. Improvement
resulting from R2R control was compared with FBO-estimated improvement.
Finally, overlay improvement and a corresponding reduction in rework
were used to project a return on investment for the project. From
this work, several conclusions can be drawn: R2R improvement is significant,
FPO estimates are accurate, rework can be reduced significantly, and
implementing the system has a high return on investment.
Sturtevant et al., "Implementation of a Closed-Loop CD and Overlay
Controller for sub-0.25-µm Patterning," in Proceedings of
SPIE, Metrology, Inspection, and Process Control for Microlithography
XII, vol. 3332 (Bellingham, WA: SPIE, 1998), 461470.
Reuel, "Run to Run Overlay Control Using APC Feedback," in Proceedings
of AEC/APC Symposium XIII (Austin, TX: International Sematech,
Crow and EL Joubert, "Application of Feedforward Reticle: Offset for
Overlay APC in a High-Part-Count Fab," in Proceedings of SPIE,
Metrology, Inspection, and Process Control for Microlithography XVI,
vol. 4689 (Bellingham, WA: SPIE, 2002), 11511161.
Crow et al., "A Comprehensive Analysis of Statistical and Model-Based
Overlay Lot Disposition Methods," in Proceedings of SPIE, Metrology,
Inspection, and Process Control for Microlithography XV, vol.
4344 (Bellingham, WA: SPIE, 2001), 127138.
J Baliga, "Advanced Process Control: Soon to Be a Must," Semiconductor
International (July 1999), 76.
Crow is manager of advanced applications for New Vision Systems
(Cambridge, MA), where he oversees R&D for product enhancements
and provides process expertise to the company's new business development
activities. Previously, he was a photolithography technology development
engineer and an etch technology development engineer at Cypress Semiconductor.
Crow has published many articles, holds one patent, and has multiple
patents pending. He received a BS in chemical engineering from the
University of Minnesota, Minneapolis. (Crow can be reached at 617/551-2200