a condition-based process control model for STI trench depth control
Gaddam and Martin W. Braun, Texas Instruments
isolation (STI), which is used to define active areas and
to separate device elements in ICs, is critical for the proper functioning
of transistors. The STI etch process is used to create shallow trenches
in the silicon substrate, which are subsequently filled with dielectric
material to form isolation barriers between device elements. Tight control
of isolation critical dimension (CD) and trench depth is required for
optimal device performance and for achieving desired yields. While STI
CD variation degrades direct drain quiescent current (IDDQ), transistor
performance, and speed, trench depth variation results in post-CMP step
height variation, the erosion of active areas (moats), gate length reduction,
incomplete isolation trench oxide fill and voids, IDDQ failures, and
reduced wafer yields. To control STI CD and trench depth, effective
and robust process control models and strategies are required, in addition
to advanced processes and reliable metrology tools.
standard STI etch process involves etching the bottom antireflective
coating (BARC), the nitride, the underlying pad oxide, and a shallow
trench into the silicon substrate. At the 130-nm technology node, STI
etch uses a hard-mask process to reduce defectivity and improve chamber
performance. In the first step of this process—nitride hard-mask
open—the BARC, nitride, pad oxide, and some silicon are processed
in the nitride etcher. In the next step, ex situ ash and wet cleans
are performed. Following ashing and cleaning, wafers are processed in
the silicon etcher to etch the remaining silicon trench. Because this
process involves two separate etch steps, metrology can be performed
after both nitride etch and silicon etch to achieve improved CD and
trench depth monitoring and control. All metrology steps, including
the measurement of CD, trench depth, and nitride thickness, are conducted
using scatterometry.1 Figure 1 presents a schematic view
of the process flow.
1: Schematic flow diagram of the STI etch hard-mask process.
etcher wet clean cycles, silicon etch rates tend to fall, resulting
in a gradual downward shift in the total silicon trench depth. (Wet
clean cycles are the periods between two wet clean events in the chamber.
Hence, chamber radio frequency (RF) hours are zero immediately after
a wet clean and reach their maximum value immediately before the next
wet clean.) The downward trend in silicon etch depth causes lot-to-lot
and tool-to-tool variations. Tool-to-tool variations occur when wet
cleans are performed in different chambers at different times. Figure
2 illustrates the effect of a wet clean cycle on the trench depth.
2: Effect of performing an etcher wet clean on trench depth measurements.
engineers at Texas Instruments (TI; Dallas) have manually adjusted etch
time on a regular basis to keep trench depth in control and on target.
However, that tedious, non-value-added procedure requires that fab personnel
constantly monitor trench depth using statistical process control charts.
To eliminate the need for manual recipe management and reduce process
variability, a fully automated feedback/feedforward process control
strategy was designed and deployed in ProcessWorks, a TI-developed process
control software application offered by Adventa Control Technologies
develop a control model for the software, simulation analysis was performed
using Matlab (Natick, MA). The analysis enabled engineers to rapidly
create prototypes of potential designs using historical data. The performance
of several candidate models was evaluated based on overall performance
statistics, ease of maintaining the control model, and visual inspection
of the simulated performance in time-series representation. The model
that performed best was selected for additional tuning and implementation.
resulting control model takes into account variation caused by the nitride
etcher, the silicon etcher, the reticle set, and the process type. Feedforward
information includes postnitride-etch trench depth. The model automatically
takes into account changes in the trench depth target specification.
The final total silicon trench depth is fed back into the control model
and used to update the model parameters. The process model employed
in the control model is summarized in the following equation,
= (Etch Time X M) + B1 + B2 + TSN
Etch Time is the silicon main etch time, TSD
is the total silicon trench depth, TSN
is the silicon trench depth after nitride etch, and M, B1,
and B2 are model parameters as defined in Table I. The process
flow and control model are illustrated schematically in Figure 3.
Model parameter (gain)
I: Variable definitions.
3: ProcessWorks model for STI etch trench depth control.
a production lot is about to be processed in the silicon etcher, the
controller automatically retrieves the model parameters that match the
lot context, incorporates the target silicon trench depth (TTSD)
and post-nitride-etch trench depth (TSN)
from the manufacturing execution system, and then calculates the input
to the etcher recipe (silicon main etch time). The etch time setting
is subject to asymmetric maximum move constraints (i.e., it can decrease
etch time more than it can increase it from one run to another).
scatterometry has been performed on the post-silicon-etch wafers, the
total silicon depth is uploaded to the controller. This measurement
is checked against the goodness of fit, which defines the residual fit
error of the scatterometry model when applied to the raw data. The final
average trench depth measurement is also subject to a spike filter similar
to those discussed in the literature.2 The spike filter compares
the current data to the most recent previous data to determine if they
are gross outliers or if they represent a true process shift.
metrology data are used to update the process gain M and the components
of the process offsets B1 and B2. The exponentially
weighted moving average (EWMA) observer is used to track the variation
associated with each model parameter for the given lot context. Discussion
of the properties of the EWMA observer is found in the literature.3
The EWMA observer for B1 is described in the following equation
(the EWMA observer for B2 has the same form):
= B1(k)– λB1X
this equation, the variable k denotes the current run, while
k+1 denotes a future run. The variable λB1
is the tuning constant for the EWMA observer for the B1 parameter.
B1 and B2 each have their own unique tuning parameter.
EWMA observer for M is similar to that for B1. However,
to take into account the dependence of etch rate on the wet clean cycle,
a time-based tuning curve found in the literature was employed.4
This tuning curve determines the tuning constant λM
and has the form:
tuning curve is well suited for STI depth control, because it can be
parameterized and fit to enable the control model to rapidly tune immediately
following a wet clean event. Because post–wet clean metrology
data are uncertain and it is difficult to know the RF hours immediately
before lot processing, it is desirable to tune the controller rapidly
when the etch rate in the wet clean cycle changes rapidly, rather than
to make a full correction after the wet clean step. Figure
4 illustrates the properties of the tuning curve that make it suitable
for this purpose.
Figure 4, the variable φ1 controls how fast the curve
descends to the asymptotic value as RF hours (t) grow. The
variable φ2 determines the relative value of the initial
and final values of the tuning curve as t grows. The variable φ3
allows the overall curve to be scaled. The tuning parameters of the
control model—λB1, λB2,
φ1, φ2, and φ3—were
then optimized to provide the desired balance of robustness and performance.
This operation was critical, since the process offset of the model is
decomposed into B1 and B2. The literature demonstrates
that the decomposition of model parameters, combined with EWMA observers,
defies traditional assumptions of robust performance and stability bounds.5
Therefore, simulation on historical data ensured that the tuning parameters
would work in the production environment.
possibilities exist to capture the ramplike nature of the etch rate's
dependence on the wet clean cycle. The predictor-corrector control observer
suggested in the literature is one such approach.6 Additionally,
the Kalman filter or the recursive least-squares approach have been
proposed for STI etch processes.7 While these methods are
effective and applicable to a wide class of problems, the tuning curve
proposed in this article provides a simple, effective method for matching
the controller's response speed with the seasonality of the wet clean
effect on the etch rate.
control model was employed in the ProcessWorks advanced process control
environment. After it was run in monitor mode for a period of time to
validate the implementation and supporting automation code, the controller
was released in control mode. Figure 5 illustrates the performance improvement
that was achieved by using the controller. A comparison between Figures
2 and 5 demonstrates that the controller can control the wet etch process
5: Run time performance of the controller in production.
a wet clean, look-ahead wafers are run for each process type performed
in the tool. The data gained from this are fed directly into the controller
and used by the EWMA filter and tuning curve to update the value of
the process gain M for each tool/process combination. The controller
can then maintain the trench depth target for the entire wet clean cycle.
system offers several features for effective and robust process control.
Two different spike filters (trench depth value and goodness of fit,
both of which are derived from scatterometry data) are used to prevent
bogus data from being supplied to the controller, and two different
guard band settings (minimum/maximum bounds and run-to-run delta for
etch time) are used to prevent out-of-bound conditions.
article discusses the development and implementation of an etch chamber
condition–based run-to-run process controller for use in STI trench
depth control. The controller offers preventive maintenance cycle–based
tuning and excursion protection. Its implementation has eliminated the
need for manual process recipe adjustment and has improved process capability
by approximately 30%. Actual process data before and after the controller
was employed demonstrate its efficacy. The controller can effectively
compensate for variations arising from wet clean cycles, tool/chamber
matching, and process and reticle-set differences.
article is an edited version of a paper presented at the IEEE/SEMI Advanced
Semiconductor Manufacturing Conference, held April 11–12, 2005,
in Munich. The authors would like to thank Stacy Bozarth, Padu Krishnagiri,
Ulka Kumar, Don Sonom, James Yarborough, and John Bernard for helping
to implement the STI etch trench depth controller at TI's Kilby Center
S Gaddam and C Baum, "An Improved Process, Metrology, and
Methodology for Shallow Trench Isolation Etch," in Proceedings
of the IEEE/SEMI Advanced Semiconductor Manufacturing Conference
(Piscataway, NJ: IEEE, 2004), 93–97.
NS Patel and R Soper, "Control of Photolithography Alignment,"
in Run to Run Control in Semiconductor Manufacturing, chap.
16, ed. James Moyne, Enrique del Castillo, and Arnon M. Hurwitz (Boca
Raton, FL: CRC Press, 2000), 249–260.
E del Castillo, Statistical Process Adjustment for Quality
Control (New York: Wiley, 2002).
L Ljung and T Söderström, Theory and Practice of
Recursive Identification (Cambridge, MA: MIT Press, 1983).
S Harrison et al., "An Evaluation of the Effects of Product
Mix and Metrology Delay on the Performance of Segregated versus Threaded
EWMA Control" (paper presented at the AEC/APC Symposium XV, Colorado
Springs, CO, September 13–18, 2003).
S Butler et al., "Supervisory Run-to-Run Control of Polysilicon
Gate Etch Using in Situ Ellipsometry," IEEE Transactions on
Semiconductor Manufacturing 7, no. 2 (1994): 193–201.
R Chong et al., "Analysis of a Run-to-Run Controller on
a Drifting STI Etch Process by Augmentation of the Integrated Interferometric
Endpoint Detection System" (paper presented at the AEC/APC Symposium
XIV, Snowbird, UT, September 7–12, 2002).
Gaddam is a senior process engineer and a group technical staff
member at Texas Instruments' Kilby Center in Dallas. His responsibilities
include process development; release to production and ramp to volume
of STI etch processes; rapid thermal processing; and room-temperature
vulcanizing of STI etch processes for the 130-, 90-, and 65-nm nodes.
Previously, Gaddam was a senior process engineer at Unitrode/Texas Instruments
and a process engineer at IDT. He received a BS in chemical engineering
from Osmania University in Hyderabad, India, and an MS in chemical engineering
from Arizona State University in Tempe. (Gaddam can be reached at 972/927-3096
W. Braun, PhD, works at Component Automation Systems, Intel,
in Chandler, AZ. His responsibilities include research, development,
and implementation of run-to-run control. He also focuses on real-time
system identification and control and control-oriented approaches for
inventory control and supply-chain management. In 2001, he joined Texas
Instruments, where his responsibilities included run-to-run modeling
and control of etch and photolithography processes. He received a BS
from SUNY-Buffalo in Buffalo, NY, and MS and PhD degrees from Arizona
State University in Tempe, all in chemical engineering. (Braun can be
reached at 480/554-6339 or firstname.lastname@example.org.)