MKS Confidential 1
Process Improvement Team Project
Wave Soldering Thermal
Profiling Management Improvement
MKS Confidential 2
Problem Statement
PWI Introduction
Identify Performance Measures (Statistics)
Measurement Techniques for Performance Measuring
Setting Metric for Performance Measures
• Evaluating the current performance
• Data collection plan development
• Calculating sample size
• Collected data sets
• Analyzed data
Define Process Limits
Piloted to measure PWI
Define PWI Control Limits
Control plan
Benefits
MKS Confidential 3
Problem Statement
Questions:
• Making assumption we get the machine profiles as shown, how can we quantificational qualify the performance of the machine? Does the machine work stable and meet specified criteria?
• How can get profile #1 and profile #2 be compared, which one is better?
MKS Confidential 4
Problem Statement
There is no a quantifiable system of ranking equipment performance. Measuring and comparing the thermal profile to its process window is
a subjective judgment with no real uniformity or statistically reproducible results; from one engineer or technician to the next; from one product to the next…
Also, it is a time consuming process. Is there a quantifying approach to calibrate the performance of
machine?
This project will introduce a quantifying technique – PWI to measure the performance of wave soldering machine and reflow oven.
This presentation reported the implementation results of wave soldering only.
MKS Confidential 5
A Method for Quantifying Thermal Profile Performance - PWI
What is PWI
The Process Window IndexThe PWI is a quantifiable, reproducible, statistical measure of how well a profile
performs relative to critical process limits. Every thermal profile is ranked on the
basis of how it “fits” within the process window. The center of the process
window is defined as zero, and the extreme edge of the process window as
99%. A PWI of 100% or more indicates that the profile will not process product
within specification. A PWI of 99% indicates that the profile will process product
within spec, but it is running at the very edge of the process window. A PWI of
70% indicates a profile is using 70% of the process spec.
The PWI tells us exactly how much of our process window a given profile uses,
and thus how robust that profile is. The lower the PWI, the better the profile.
The thermal process can now be reliably measured, analyzed, compared and
tracked with the same level of SPC and Quality Control available to other
manufacturing processes.
MKS Confidential 6
A Method for Quantifying Thermal Profile Performance - PWI Calculating the PWI
The PWI for a complete set of profile statistics is
calculated as the worst case (highest number) in the
set of statistics. For example: if you run a profile with
three thermocouple, and four profile statistics are
logged for each thermocouple, then there will be a
set of twelve statistics for that profile. The PWI will be
the worst case (highest number expressed as
a percentage) in that set of profile statistics.
(Measured_value[i,j] - average_limits[I,j])
(range[I,j]/2PWI = 100 x MAX
I, j=1
N, M
MKS Confidential 7
Identifying Performance Measures Thermal profile specifications are ranges consisting of minimum or
maximum values. These ranges apply to numerous statistics such as soak time, slope, peak temperature and variety of others. Which statistics were determined to be a CTQ measures?
Project team selected below profile statistics as the performance measure that was being used to calibrate the wave machine.
1. Top Side Preheat Temperature Rising Slope
2. Bottom Side Preheat Temperature Rising Slope
3. Top Side Preheat Peak Temperature
4. Bottom Side Preheat Peak Temperature
5. Top Side Wave Peak Temperature
6. Bottom Side Wave Peak Temperature
MKS Confidential 8
Measurement Techniques for Performance Measuring
Run profiles using devices: Profile Checker SlimKIC 2000 Standard Profile Test board Thermocouples: K Type
Bonding Techniques Attaching the tip of the thermocouples to the
desired location with Aluminum Tape or high temperature solder.
Aluminum Tape Size; Kept solder joint as small as possible.
Thermocouples placements and KIC connections as shown.
Top thermo. measures - Preheat Slope; Peak Temp. & Top Wave
Peak Temp.Bot. thermo.
measures – Bot. Wave Peak
Temp.
Bot. thermo. measures – Bot. Preheat Peak Temp. & Slope
MKS Confidential 9
Setting Target for Performance Measures
Following activities have been done prior to set the limits for the
above performance measures:
Evaluating the current performance of the machine.
Data Collection Plan Development
Calculating Sample Size
Collected data set
Analyzed data
Applied statistical tools to define the metric of the performance measure
MKS Confidential 10
Based on previous data knew the standard deviation of measures. Refer to the appendix for the baseline data set
Variable StDev
• Top Max. Rising 0.0525
• Bot Max Rising 0.0540
• Top Preheat Temp. 0.725
• Bot Preheat Temp 0.793
• Top Wave Temp 2.37
• Bot Wave Temp 1.90
Evaluating the current performance of equipment
MKS Confidential 11
N - Minimum Sample Size Calculation,
The minimum sample size was determined to 26 per below calculation, that means, 26 profiles required in order to meet the required precision.
The desired precision level was determined shown in below table.
2
△96.1
s
n=
KPIVs StdDev. △ n
Top Max. Rising 0.0525 0.05 5
Bot Max Rising 0.054 0.05 5
Top Preheat Temp. 0.725 0.5 8
Bot Preheat Temp 0.793 0.5 10
Top Wave Temp. 2.07 0.8 26
Bot Wave Temp 1.9 0.8 22
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Measurement/Metric
X or Y
Operational Definition
Type of Data
(Discrete/ Continuous
)
Data Source
and Location
Sample Size
Who Will Collect the
Data?
When Will Data
be Collected?
How Will Databe
Collected?
Is the Measureme
nt System
Capable?
Graphical and/or
Statistical Tools to be
Used
Top Max. Rising X
Wave Soldering process Continuous
Wave Soldering Station 26
David Zhang
June, 2011
Randomly selected Yes Excel form
Bot Max Rising X
Wave Soldering process Continuous
Wave Soldering Station 26
David Zhang
June, 2011
Randomly selected Yes Excel form
Top Preheat Temp. X
Wave Soldering process Continuous
Wave Soldering Station 26
David Zhang
June, 2011
Randomly selected Yes Excel form
Bot Preheat Temp X
Wave Soldering process Continuous
Wave Soldering Station 26
David Zhang
June, 2011
Randomly selected Yes Excel form
Top Wave Temp X
Wave Soldering process Continuous
Wave Soldering Station 26
David Zhang
June, 2011
Randomly selected Yes Excel form
Bot Wave Temp X
Wave Soldering process Continuous
Wave Soldering Station 26
David Zhang
June, 2011
Randomly selected Yes Excel form
Data Measurement Plan
MKS Confidential 13
Setting Pre-heat Zone 1 Temp. ( )℃
Pre-heat Zone 2 Temp.( )℃
Pre-heat Zone 3 Temp.( )℃
Wave Temp. ( )℃
Speed ( cm / min)
Top Side / 140 145
242 103Bottom Side 130 140 145
Run profile per below machine setting for data collection process.
Machine Settings
MKS Confidential 14
Date Top Max. Rising Bot Max Rising Top Preheat Temp. Bot Preheat Temp Top Wave Temp. Bot Wave Temp
1.67 1.73 119.1 120.2 157.6 238.71.64 1.7 119.73 120.3 163.1 238.71.61 1.69 119.2 120.2 160.3 238.91.65 1.68 119.1 120.17 159.7 236.91.59 1.56 119.1 119.7 166 2351.59 1.73 119.5 120.3 158.7 238.21.61 1.59 119.2 119.5 160.7 237.51.56 1.6 118.7 158.3 233.731.66 1.58 119.8 119.9 165 234.11.64 1.63 118.8 119.1 164 237.61.6 1.62 118.2 119.2 159.9 235.2
1.64 1.65 119 120 159.5 234.21.61 1.64 118.9 119.4 156.2 238.21.63 1.61 119.5 120.5 161.9 236.21.63 1.62 119.4 119.67 163.9 238.61.61 1.66 118.9 119.7 158.6 238.81.61 1.58 119.9 119.6 161.1 235.71.64 1.76 118.7 119.7 157.6 237.71.61 1.61 118.8 119.6 157.5 237.11.62 1.62 119 119.2 161.5 236.51.68 1.67 118.2 155.8 2351.62 1.61 119.3 119.4 160.6 235.91.64 1.66 119 118.8 156.1 237.71.65 1.67 119 119 158.4 240
2011/6/13 1.64 1.65 119.4 119.3 163.5 234.81.64 1.65 119.1 119.2 161 2351.64 1.67 119.1 119.1 157.6 237.1
2011/5/24
2011/5/26
2011/5/27
2011/5/30
2011/5/31
2011/6/1
2011/6/2
2011/6/3
2011/6/7
2011/6/8
2011/6/9
2011/6/10
2011/6/14
Collected Data Sets
MKS Confidential 15
1. 7001. 6751. 6501. 6251. 6001. 5751. 550
99
95
90
80
70
605040
30
20
10
5
1
Top Max. Ri si ng
Percent
Mean 1.627StDev 0.02628N 27AD 0.550P-Value 0.142
Probabi l i ty Pl ot of Top Max. Ri si ngNormal
121. 0120. 5120. 0119. 5119. 0118. 5118. 0
99
95
90
80
70
605040
30
20
10
5
1
Bot Preheat Temp
Percent
Mean 119.5StDev 0.5533N 27AD 0.234P-Value 0.772
Probabi l i ty Pl ot of Bot Preheat TempNormal
167. 5165. 0162. 5160. 0157. 5155. 0
99
95
90
80
70
605040
30
20
10
5
1
Top Wave Temp.
Percent
Mean 160.2StDev 2.792N 27AD 0.303P-Value 0.549
Probabi l i ty Pl ot of Top Wave Temp.Normal
241240239238237236235234233232
99
95
90
80
70
605040
30
20
10
5
1
Bot Wave Temp
Percent
Mean 236.8StDev 1.734N 27AD 0.444P-Value 0.264
Probabi l i ty Pl ot of Bot Wave TempNormal
Data Analysis – Normality Test
120. 0119. 5119. 0118. 5118. 0
99
95
90
80
70
605040
30
20
10
5
1
Top Preheat Peak Temp.
Percent
Mean 119.1StDev 0.4081
N 26
AD 0.504
P-Value 0.186
Probabi l i ty Pl ot of Top Preheat Peak Temp.Normal
• P-value > 0.05
• All data are normally distributed
1. 751. 701. 651. 601. 551. 50
99
95
90
80
70
605040
30
20
10
5
1
Bot Preheat Max. Ri si ng Sl ope
Percent
Mean 1.646StDev 0.04917
N 27
AD 0.258
P-Value 0.692
Probabi l i ty Pl ot of Bot Preheat Max. Ri si ng Sl opeNormal
MKS Confidential 16
Data Analysis - Hypothesis Test
Verified the two data sets – Top Preheat Max. Rising Slope and Bot. Preheat Max. Rising, if both without a statistical significant difference, than two measures could be combined and defined with an identical process limits.
Paired T-Test and CI: Top Max. Rising, Bot Max Rising
N Mean StDev SE Mean
Top Preheat Max. Rising 27 1.62704 0.02628 0.00506
Bot Preheat Max. Rising 27 1.64593 0.04917 0.00946
Difference 27 -0.01889 0.04627 0.00890
95% CI for mean difference: (-0.03719, -0.00058)
T-Test of mean difference = 0 (vs not = 0): T-Value = -2.12 P-Value = 0.044
Since P-value is < 0.05, rejected the Null Hypothesis. That is the two measures
– Top Max. Rising and the Bottom Max. Rising with statistical significant
difference.
MKS Confidential 17
Verified the two data sets – Top Preheat Max. Peak Temp. and Bot. Preheat Peak Temp. if both measures without a statistical significant difference, then these two measures could be combined and defined with an identical process limit.
Paired T-Test and CI: Top Preheat Temp., Bot Preheat Temp
N Mean StDev SE Mean
Top Preheat Temp. 27 119.052 0.421 0.092
Bot Preheat Temp 27 119.511 0.535 0.117
Difference 27 -0.459 0.493 0.108
95% CI for mean difference: (-0.683, -0.235)
T-Test of mean difference = 0 (vs not = 0): T-Value = -4.27 P-Value = 0.000
Since P-value is < 0.05, Reject the Null Hypothesis. That mean the two measures –
Top Max. Preheat Temp. and the Bottom Max. Preheat Temp. with statistical
significant difference.
Data Analysis - Hypothesis Test
MKS Confidential 18
28252219161310741
1. 8
1. 7
1. 6
1. 5
Obser vat i on
Individual Value
_X=1.6464
UCL=1.7815
LCL=1.5114
28252219161310741
0. 20
0. 15
0. 10
0. 05
0. 00
Obser vat i on
Moving Range
__MR=0.0508
UCL=0.1659
LCL=0
6
1
2
1
I-MR Chart of Bot Preheat Max. Rising Slope
Define Process Limits - In-control/Out of Control and Stability Verification• Test the six statistical using rules as shown.
• With exception of the statistical listed below others are in control and stable.
− Bot. Preheat Max. Rising Slope
− Bot. Preheat Peak Temp.252219161310741
1. 70
1. 65
1. 60
1. 55
Observation
Indiv
idual V
alu
e
_X=1.6259
UCL=1.6965
LCL=1.5553
252219161310741
0. 08
0. 06
0. 04
0. 02
0. 00
Observation
Movi
ng R
ange
__MR=0.02654
UCL=0.08671
LCL=0
I-MR Chart of Top Preheat Max. Rising Slope
MKS Confidential 19
252219161310741
120. 5
120. 0
119. 5
119. 0
118. 5
Observation
Ind
ivid
ua
l Va
lue
UCL=120.827
LCL=118.257
_X=119.542
252219161310741
1. 6
1. 2
0. 8
0. 4
0. 0
Observation
Mo
vin
g R
an
ge
__MR=0.483
UCL=1.578
LCL=0
1
66
I-MR Chart of Bot Preheat Peak Temp
252219161310741
170
165
160
155
150
Observation
Ind
ivid
ua
l Va
lue
UCL=169.75
LCL=150.56
_X=160.15
252219161310741
12
9
6
3
0
Observation
Mo
vin
g R
an
ge
__MR=3.61
UCL=11.79
LCL=0
I-MR Chart of Top Wave Temp.
252219161310741
243
240
237
234
231
Observation
Ind
ivid
ua
l Va
lue
_X=236.78
UCL=241.68
LCL=231.88
252219161310741
6. 0
4. 5
3. 0
1. 5
0. 0
Observation
Mo
vin
g R
an
ge
__MR=1.844
UCL=6.024
LCL=0
I-MR Chart of Bot Wave Temp
Define Process Limits -
252219161310741
120
119
118
Observation
Ind
ivid
ua
l Va
lue
_X=119.153
UCL=120.428
LCL=117.878
252219161310741
1. 6
1. 2
0. 8
0. 4
0. 0
Observation
Mo
vin
g R
an
ge
__MR=0.479
UCL=1.566
LCL=0
I-MR Chart of Top Preheat Peak Temp.
MKS Confidential 20
Define Process Limits -
The team performed a cause & effect analysis and determined the most possible root cause as following.
Actions have been taken to eliminate the special cause variations so that the process could be drawn back under control and stable.
• The frequency of changing the aluminum tape has been specified at 1 test / 1 times
• Tape dimension required: 6mm x 6mm
• Run test until setting temp. being reached
Defined the Process Limits for the six statistical per the table as shown below.
Spec. Top Max. Rising Bot Max Rising Top Preheat Temp. Bot Preheat Temp Top Wave Temp. Bot Wave Temp
UCL= x' + 3s 1.71 1.79 120.4 120.8 169.9 241.8
x' 1.63 1.65 119.1 119.6 160.2 236.8
LCL= x' - 3s 1.55 1.51 117.8 118.4 150.5 231.8
MKS Confidential 21
Piloted to Measure PWI The profile checker, machine
setting and sample size (26) used to collect the PWI was identical to the baseline data collection. The pilot was run for 13 days starting on Jun 13, run two profiles per each day.
The collected 26 PWIs were less than 100%, which reveals that the pre-defined Process Limits are robust being used to measure the performance of the wave soldering machine.
2321191715131197531
100. 00%
75. 00%
50. 00%
25. 00%
0. 00%
Observati on
Individual Value
_X=51.70%
UCL=97.75%
LCL=5.64%
2321191715131197531
60. 00%
45. 00%
30. 00%
15. 00%
0. 00%
Observati on
Moving Range
__MR=17.32%
UCL=56.58%
LCL=0.00%
I - MR Chart of PWI - Pol i ted
MKS Confidential 22
Define Control Limits for PWI The control limits of PWI is specified as following based on the
piloted measurements. Targeted PWI: ≤75% No need to take action Process Indicator: >75% Keep close monitor the next data Out of Spec: >97% Root cause analysis & take corrective action
Wave Soldering PWI Daily Performance
PWI (%)
0
15
30
45
60
75
90
105
120
26May
26May
27May
27May
30May
30May
31May
31May
01Jun
01Jun
02Jun
02Jun
03Jun
03Jun
07Jun
07Jun
08Jun
08Jun
09Jun
09Jun
10Jun
10Jun
13Jun
13Jun
14Jun
14Jun
GoodPWI
WarningPWI
OutOfSpec
MKS Confidential 23
Process Control Plan
PWI is indentified as the indicator to measure the performance of the wave soldering machine.
PIE technician must to measure the PWI by the mean of running thermal profile every day.
Profile daily record should be reviewed by engineer. Any out of control limits data should be analyzed by
PCBA PIE engineer.
MKS Confidential 24
Process Control Plan
Project Name:
Introduction of PWI methodology for monitoring the performance of Wave Soldering Machine
Process Step Key Indicator
X (control)
or
Y (monitor
)
Product/Process
Specifications/Target
Evaluation/
Measurement Technique
%P/Total (R&R)
%P/Tolerance
Sample
Size
Sample Frequenc
yRespons
ibilityControl Method
Contingency Action Plan
Set wave soldering machine
Conveyor Speed x 1.03m/min
Speedometer 1 Day David
Profile Program
Calibrate machine
Bot. Pre-heat Zone #1 Temp. x 130℃
Th
erm
om
ete
r
1 Day DavidProfile Program
Top Pre-heat Zone #2 Temp. x 130℃ 1 Day
David
Profile Program
Bot. Pre-heat Zone #2 Temp. x 140℃ 1 Day David
Profile Program
Top Pre-heat Zone #3 Temp. x 140℃ 1 Day
David
Profile Program
Bot Pre-heat Zone #3 Temp. x 145℃ 1 Day
David
Profile Program
Wave Temp. x 145℃ 1 Day David
Profile Program
Profiling
PWI Y 0% - 99% 1 Day David
Profile Record & I-MR Control Chart
Calibrate machine
& Check Profile
Test Board
MKS Confidential 25
Control Plan – Machine Profile Daily Records Print the Machine Profile out and send it to the engineer for approval.
PWI Result
Accept Criteria
Signoff
MKS Confidential 26
Benefits of Ranking with PWI
Greatly simplifies the profiling process. Confidence in thermal process capability Profiles can be easily compared Improved Quality Control Significant production savings