ICPIG, July 12-17, 2009, Mexico
B6) Plasma Diagnostic Methods
Development of real-time non-invasive performance analysis tools for
atmospheric pressure plasma system monitoring
J. Tynan1, V. J. Law2, B. Twomey1, A.M. Hynes1, S. Daniels2 G. Byrne1, D.P. Dowling1
1 School of Electrical, Electronic and Mechanical Engineering, University College Dublin, Belfield, Dublin 4, Ireland
2 National Center of Plasma Science and Technology, Dublin City University, Collins Avenue, Glasnevin, Dublin 9,
Dublin, Ireland
A set of real-time non-invasive multivariate analysis tools were developed using LabVIEW software for the
process control of an atmospheric pressure plasma system. It was found that variations in the voltage, current
and frequency of the applied power to an atmospheric pressure plasma could be used to monitor plasma
processing conditions. These diagnostic tools were used to assess the transition of the plasma from primary
glow mode to the secondary glow mode. The electrical observations were recorded in real-time, plotted on a
principle component analysis loading plot and analysed using non-parametric cluster analysis. It was observed
from the plotted electrical parameters that data clusters were formed which relates to both the geometry of the
atmospheric plasma chambers and the mode of plasma operation. The development of these tools facilitates
real-time analysis of the atmospheric pressure plasma process in a reel-to-reel continuous operating mode.
1. Introduction
Many industrial atmospheric pressure plasmas
exhibit both non-uniform filamentary column microdischarge (CMD) and uniform pseudoglow
discharge modes of operation [1]. These operational
modes are typically classified by the characteristics
of their voltage and current waveforms [1-5]. This
paper examines the effect on current (Irms), voltage
(Vrms) and frequency with changes in applied power
and gas mixture in an atmospheric pressure plasma.
The system investigated is the Dow Corning SE1100 LabLineTM, which is a reel-to-reel atmospheric
pressure plasma system (Figure1) [6]. The analysis
of the diagnostic data should facilitate process
control during the surface modification of polymer
webs.
The chaotic nature of atmospheric pressure plasma
electrical waveforms can be attributed to physical
effects such as: bifurcation, mode transition, varying
gas residence time and electrical circuit feedback
[7]. This multi-source of chaotic influences is
potentially suited to analysis by data-driven
Multivariate Analysis (MVA) tools. This paper uses
two such MVA tools: Principal Component
Analysis (PCA) [8] and Non-Parametric Cluster
Analysis (NPCA) [9, 10]. Real-time PCA is a
method in which processed data is transformed into
a new data-set that is projected into multidimensional space in the form of a loading plot. The
loading plot coordinates encompass the fundamental
elements of the process. NPCA is a method of
analysing complex data sets in multi-dimensional
space. Regions within this NPCA space that are
visited frequently are highly populated with data
points forming clusters. The population and spatial
orientation of these clusters have mathematical
deterministic information related to the waveforms
that are being measured. The objective is to
demonstrate for the first time the MVA approach to
analysing the electrical data from an atmospheric
pressure plasma system.
2. Chamber and Measurement
The LabLine atmospheric pressure plasma system
has been described previously [11, 12]. The system
comprises two parallel-plate plasma chambers
arranged in conjunction with a dedicated reel-to-reel
web handling system (Figure 1).
Figure 1: Schematic of LabLine atmospheric pressure
plasma system
ICPIG, July 12-17, 2009, Mexico
Each chamber has two 300 x 320 mm electrodes
with a 5 mm electrode gap. Helium (He) and oxygen
(O2) were introduced at the top of each chamber
using valve rotameters at flow rates of 5 and 0.125
standard litre per min (slm) respectively.
2000W
3500
He + 2.5% O2
Helium Only
1400W
1800W
3000
2500
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1200W
1500
1600W
1400W
1200W
1000W
1000
1000W
500
800W
0
15000
17000
19000
600W
21000
400W
23000
400W
25000
27000
Frequency [Hz]
Figure 2: Loading plot for He and He with 2.5% O2 as a
function of applied power
2.2. Mode of operation
Three distinct areas are observed in the Loading plot
in Figure 2. The primary glow mode is observed at
low powers. As the applied power increases, there is
a transition point at which the slope of the data
changes. This occurs at approximately 1000 W for a
He discharge and at 1600 W for a He/O2 discharge.
Above these applied powers the secondary glow
mode is observed. These regions correspond to
distinct modes of plasma operation, and have also
been examined using visual and photomultiplier
analysis, (Figure 3).
A
B
C
8
.00
Y-axis → Vrms [V] x Irms [mA]
X-axis → Frequency [Hz]
Over a period of time the loading plot becomes a
visual historical map of the process, where each data
point is a profile of the process at a particular time.
The Loading plot outcome obtained for a plasma
formed with He and He with 2.5% O2 as a function
of applied power in steps of 200 W is shown in
Figure 2. (Trend lines are for guidance only.)
It can be seen that the data at varied applied plasma
power produces a trajectory from low to high power
with a clear separation in the data points. A
transition region is observed from 1000 to 1200 W
for He only and 1600 to 1800 W for the He/O2
mixture. The transition region is illustrated by a
non-linear response in the loading plot data points.
Current [A]
6
4
2
Current
PMT
-.10
0
-2
PMT Emission [AU]
2.1. Principle component analysis
Current and voltage waveforms were measured
using a Person (1A:1V) 6585 current probe and a
North Star PVM-5 (1kV:1V) high voltage probe
respectively. Both current and voltage waveforms
are captured and digitally processed using a
National Instrument 5133 100 MHz digitiser and
associated LabVIEW software. The Vrms, Irms and
frequency are extracted from the waveforms
averaged over 0.5 sec. The data is collected in realtime for 100 points and this is mapped in high
dimensional space within a loading plot. The axes
on the loading plots are devised such that clear
separation of the data is observed for varied
operating conditions. The axes score used for the
PCA loading plot are given by:
1600W
4000
V rms [V] x I rms [mA]
Applied powers of up to 2000 W were delivered to
the electrodes using a Vetaphone power supply
(frequency 16 to 23 kHz). The power supply has a
frequency agile impedance matching circuit.
Circuit–resonance is maintained using a pick-up coil
on the transformer primary winding which sends the
load voltage and current, magnitude and phase to the
matching circuit. The supply frequency is adjusted
to ensure a maximum power transfer from the power
supply unit to the plasma chamber.
4500
-.20
Figure 3: Current and PMT waveforms with
corresponding observed He discharge images (15 x 15 cm
area): A) primary uniform glow mode 400 W; B)
Transition region 1000 W and C) secondary uniform glow
mode 1400 W.
As discussed by Okazaki et al. [13] and Roth et al.
[14], the discharge initially forms a uniform glow
after Townsend breakdown. This mode is
characterised by a uniform although spatially nonhomogeneous plasma. With increasing applied
power, the discharge was observed to expand to fill
the chamber. In this transition region an increased
level of CMDs are observed. With further increases
in applied power, the CMDs increase in number
coalescing to form a spatially homogeneous
pesudoglow discharge [5]. These images correlate
well with the transition region observed in the
ICPIG, July 12-17, 2009, Mexico
loading plot. This is illustrated in Figure 3 for a He
plasma at applied powers of 400, 1000 and 1400 W,
with the corresponding current and photomultiplier
tube (PMT) electrical and optical signals. Despite
the varying modes of operation, it has been
established that the system operates with glow
discharge characteristics (emission strongest at the
cathode), at each of these power levels [15].
3. Non-parametric cluster analysis
It was found that for each power examined, the PCA
loading plots exhibited a deterministic clustering
effect whereby the Vrms, Irms and frequency created
up to 4 separate regions or clusters. The data
previously used to generate the loading plot in
Figure 2 is examined more closely in Figures 4 and
5. A visual comparison of theses two figures reveal
that there is a systematic change in the number of
data clusters formed as a function of applied power.
At low power (400 to 800 W) there are generally
two data clusters; at the transition region cluster
formation increases up to 4; and at high power data
cluster formation appears to revert to 1. The change
in the number of data clusters is representative of
the electrical changes associated with the transition
from primary glow mode to secondary glow mode.
130
125
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115
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400 W
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4150
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4050
16800
16300
1000 W
20000
1200 W
16700
16750
20500
21000
1600 W
5
Helium Only
He + 2.5% O2
4
16350
16400
16450
Figure 4: Four loading plot examples of He discharge
viewed at high resolution. X-axis: Frequency [Hz], Yaxis: Vrms x Irms [V x mA]
260
255
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1600 W
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3
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1800 W
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No. of Clusters
140
Following the analysis of the loading plot data,
NPCA was used to classify the partitioning of the
data sets into subsets (clusters). NPCA allows the
number of clusters appearing at each power setting
to counted systematically so that this value may be
used for plasma mode classification. To perform the
NPCA, the loading plot images from Figure 4 and 5
are exported as a bitmap file into National
Instrument Vision builder AI 3.5 software. This
software contains a suite of hieratical particle
measurement tools and is set to look for bright
objects within a local threshold. A background
correction method within the ‘detect object step’ is
used. Matching the visual cluster observations of
Figures 4 and 5, the X and Y pixel window values
were adjusted for best fit in order to obtain two
clusters at low power (400 W), a maximum number
of clusters in the transition region and 1 cluster at
high power. The experimentally determined fit was
found to correlate when an asymmetric value of X =
50, and Y = 75 pixels was used to count the clusters.
The results of this fitting procedure are shown in
Figure 6. The NPCA described shown in this figure
yields cluster information that can be used as a
performance indicator of the discharge process. This
cluster number can also be used for comparator
limits in automatic decision control. The largest
number of clusters was observed in the transition
region, 1000-1200 W for He only plasma and 12001400 W for He + 2.5% O2 plasma. No data is given
for He only plasma above 1600 W as arcing is
observed on the electrodes at these higher powers.
20650
20700
2000 W
17400
17450
17500
Figure 5: Four loading plot examples of He with 2.5%
O2. X-axis: Frequency [Hz], Y-axis: Vrms x Irms [V x mA]
discharge viewed at high resolution.
600
800
1000
1200
1400
Applied Plasma Power [W]
1600
1800
2000
Figure 6: Number of clusters observed at varied applied
plasma power for He and He with 2.5% O2.
3.1. Origin of data Clusters
The generation of the data clusters is an affect of
both the electrode geometry and plasma operation
mode [16]. As the LabLine system comprises two
sets of paired electrodes there may be variations
within the gap voltage due to slight variations in
physical electrode gap. This could lead to the
ICPIG, July 12-17, 2009, Mexico
plasma operating at two distinct, although discreetly
different, frequencies. To assess this, single
electrode pair trials were carried out. It was found
that the number of clusters halved when only a
single electrode pair was powered. This
demonstrates that cluster formation within the data
is affected by the number of electrode pairs
connected to the power supply unit. The plasma
operating mode is also a source of data clustering.
As the applied power is increased the plasma
approaches the transition region. In this region the
plasma switches between the primary glow mode
and the secondary glow mode creating a further two
clusters. Hence if both of the electrodes are
operating in this region (e.g. He at 1200 W), then
four different clusters are observed with two from
the changing of the plasma mode and two from the
variation in electrode geometry.
4. Conclusion
This paper has described the application of datadriven multivariate analysis (real-time PCA and
NPCA) to a discharge formed in a pilot plant scale
reel-to-reel atmospheric pressure plasma system.
These tools were applied to the discharge current
and voltage waveforms using National instruments
LabVIEW software.
• Inspection of the collected data showed data
clustering which is thought to be a result of both
the plasma operating mode and electrode
geometry.
• The number of clusters was systematically counted
using LabVIEW software. A maximum number of
4 clusters were observed when both of the
electrode pairs were operating in the plasma
transition region.
• The transition from the primary glow region
through the transition region to the secondary glow
mode was monitored using the developed PCA
techniques, optical imaging and a photomultiplier
tube.
• The PCA loading plots of the electrical signal
exhibits data separation for both changes in
applied plasma power and addition of oxygen to
the helium plasma. This which could potentially
allow this technique to be used for process
monitoring in an industrial environment.
5. Acknowledgement
This work is supported by Enterprise Ireland (Grant
No. CFTD/7/IT/304) and SFI PRECISION
Cluster. The authors would also like to thank W. G.
Graham (QUB) for his support of this work.
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