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Recently JGA, in cooperation with
Brooks Instrument, explored developing a software package
to give end-users access to the bells and whistles that
digital mass flow products provide. This project opened
my eyes to the potential this technology offers for
routine monitoring of many different types of critical
sensing components in addition to MFCs. In this article
I present some sample data extracted from a simulated
Single-Wafer CVD System equipped with a bank of digital
MFC prototypes. I hope it will be illustrative of the
potential of digital technology for tighter process
monitoring.
Digital processing has permeated our daily lives
in many ways. We communicate with each other digitally
via cell phones and e-mail; read digital news on the
Internet; take digital photos and videos of our families;
we will soon be watching digital TV. A decade ago, all
of these media were analog, or didn’t even exist. The
reasons for the transformation are obvious to those
of us who appreciate the power and flexibility of these
toys. But “We,” in semiconductor wafer processing, are
a bastion of Luddites . We love our analog signal processing,
the noisier the better.
Take, for example, mass flow controllers. Analog MFCs
are found everywhere in wafer fabs. They meter precise
quantities of reactive gases in many different types
of equipment. Their performance and reliability help
determine our industry’s ability to produce components
with in-spec characteristics. Future processing – and
safety -- requirements will no doubt demand even more
precise control and reliability in the metering of gaseous
reactive chemicals.
Here are some reasons why I believe replacing analog
components with their digital counterparts in current
generations of fab equipment can help improve processes.
First is “bandwidth and access.” You can generate all
the data you need to understand the subtleties of your
equipment operation, without depending on a plodding
SECS interface for data acquisition. This advantage
becomes even more pronounced when process step times
are measured in seconds. As an example, consider the
many processes that require a positive pressure gradient
between two chambers during critical operations. If
the equipment does not provide adequate monitoring and
remedial action when error conditions occur, superimposing
a digital pressure data monitoring system can.
A second reason is signal integrity (no noise). You
can easily use the digital data to track what a component
thinks its setpoint is, and what it thinks its output
is. Often these values are quite different from what
the equipment itself thinks. For example, MFC calibration
problems frequently reveal themselves via a significant
non-zero flow output signal when there is no gas flow.
Yet, process controllers on most current generation
fab equipment are rarely programmed to look for this
condition. Where the existing equipment infrastructure
is not able to deal with an identifiable problem, digital
data monitoring systems can help add the needed intelligence
to look for these conditions and initiate appropriate
responses.
A third reason is that digital components come equipped
with lots of Gadgets – just like cameras and cell phones
– that improve their flexibility and usefulness over
analog models. For example, you can instantly change
their ranges, change the type of gas they can be used
with, or automatically “tune” them to improve their
performance under local conditions.
In our laboratory, I assembled a test fixture consisting
of a generic process controller, a bank of hybrid MFCs
(equipped with both standard analog and digital interfaces),
a “process chamber,” and a vacuum system that crudely
simulates a piece of process equipment such as a CVD
system.
In this group of experiments, I tried to simulate the
short-duration repetitive on-off cycling of gases that
occurs in many different types of process equipment
in use today in order to see what kinds of MFC problems
high-bandwidth data capture might reveal. Nitrogen gas
was introduced into a vacuum system at two-minute intervals,
followed by an intervening minute with no flow. This
sequence was repeated several times.
Figure 1 shows some mean flow rate trends. There are
three panels in this figure, each containing data from
a different MFC; each data point represents the average
flow from a single on-cycle of an MFC.
There is substantial variation in the average flow
rates of N2 from interval to interval. For the MFC in
the upper panel, the average flow rate is 777 sccm,
1-sigma is about 14 sccm, and 3-sigma is 42 sccm - about
5.4 percent. For the MFC in the center panel, 3-sigma
is about 6 percent, and for the MFC in the lower panel,
about 4 percent. Flow variations of this magnitude in
process gases such as TEOS or SiH4 could give rise to
film non-uniformity of similar magnitude.
Figure 2 shows an overlay of the MFC trace data from
several intervals. There is substantial variation, but
does it account for the observed large standard deviations
seen in Figure 1? Figure 3 shows the same trend results
as in figure 1, but with a 10-second delay in the start
of the calculation. The standard deviations are lower
in all cases; most of the observed flow variation from
interval to interval occurs while the MFC is ramping
up. The table lists the means and standard deviations
for all three flow controllers with and without the
10-second delay.
Using a digital network, I collected high-resolution
flow data that illustrated several common types of MFC
problems in gory detail. This was accomplished with
a simple two-wire twisted pair network and a PC, without
touching any of our simulated tool’s hardware infrastructure.
Whether the observed interval-to-interval flow variation
during ramp-up is an MFC problem or some other equipment
problem is immaterial – most MFCs, whether analog or
digital, will experience problems sometime during their
lifetimes. The point is that to fix problems you first
have to be able to detect them, and to detect them you
need the data.
Consider the litany of potential MFC problems: flow
overshoots during setpoint changes, long settling times,
unstable flow (oscillation), zero drift and calibration
drift. MFCs function in a rugged environment; they are
subjected to reactive and corrosive gases and unstable
pressure environments. It shouldn’t surprise anyone
that they break. The point is it’s easier to spot one
that’s broken if it is a digital.
There are two potential channels for implementing digital
technology in a wafer fab. One is through OEMs who will
incorporate digital components in future new equipment
designs. This may happen on new-generation 300-mm equipment,
but don’t hold your breath waiting for OEMs to offer
digital sensor retrofit kits for legacy machines. The
other is for end-users to employ digital technology
for their own use. Many components with digital interfaces
are hybrids that can replace existing analog components.
The networks are generally easy to install, usually
requiring a simple two-wire twisted pair strung daisy-chain
fashion from component to component, and a PC. There
is no need to dig into the bowels of the tool’s control
system to install the network.
I believe digital technology can provide unprecedented
automatic monitoring and detection of all sorts of subtle
problems for hundreds of MFCs and other types of digitally
equipped sensors throughout a wafer fab. Early Response
systems are already in place in many fabs that automatically
shut down process equipment when problems are encountered.
Fault detection systems that utilize digital sensors
can provide an extra measure of fab-wide early failure
detection and intervention.
Author information
Jon Goldman is president of Jon Goldman Associates.
JGA's data capture and analysis tools are used to reduce
down time and reduce wafer scrap by implementing and
improving SPC control.
Jon Goldman, Jon Goldman Associates, 2237 N. Batavia
St., Orange CA 92865-3105 USA. Tel: 714-283-5889; Fax:
714-283-2884; e-mail:Jon@JGA-Inc.com.
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