First Solar - JMP

Copyright © 2007, SAS Institute Inc. All rights reserved.

Innovative Improvement Methods at First Solar 

2

Contents 

Company Overview 

Technology & Manufacturing 

Products & Performance 

Market Strategy 

Environmental Responsibility 

My first success story using JMP 

Six Sigma – What works for First Solar 

First Solar tools used for reducing variation 

Summary 

MD-5-921 US October 2007 3


4


Strategic Objective 

Reduce the cost of solar electricity to a level competitive with 

conventional energy, enabling solar to become a sustainable 

mainstream source of energy 

Reduce the cost of solar 

modules using thin film 

technology and automated, 

scalable production 

Migrate from subsidized 

markets to non-subsidized 

markets by leveraging 

economies of scale – become 

“subsidy independent” 

Reduce dependence on scarce 

natural resources and curtail 

greenhouse gas emissions to 

improve our environment 



Key Accomplishments 

Formed in 1999 and launched 

production of first commercial 

products in 2002 

Raised $450 million in November 

2006 IPO. Publicly traded on 

NASDAQ (FSLR) 

Largest thin-film module 

manufacturer in the world 

Lowest cost PV manufacturer in 

the world 

First pre-funded module collection 

and recycling program in the 

PV industry 

First Solar Manufacturing Plant, Frankfurt (Oder), Germany 



Module Production Capacity 

Scaled first 25MW module production line in the U.S. to steady state 

volume in 2005, added two 25MW production lines in 2006 

Annual Nameplate Capacity = 90MW* May 2007 

2006 

Four 30MW* production lines (120MW) built in Frankfurt (Oder), 

Germany 

2005 

Annual Nameplate Capacity = 210MW by Q4 2007 

Four 30MW production lines (120 MW) under construction in Kedah, 

Malaysia with full production targeted by second half of 2008 

Annual Nameplate Capacity = 330MW by 2H 2008 

2007 

A second Four 30MW production lines (120 MW) announced for 

Kedah, Malaysia with full production targeted by first half of 2009 


A third Four 30MW production lines (120 MW) announced for Kedah, 

Malaysia with full production targeted by first half of 2009 

2008 - 2009 


* In May 2007 we increased the nameplate capacity from 25MW to 30MW per line 



Worldwide Associates > 1,150 

United States 

Phoenix, Arizona 

Perrysburg, Ohio 

Germany 

Mainz 

Berlin 

Frankfurt (Oder) 

Corporate Headquarters 

Operations, R&D and Manufacturing Headquarters 

Sales, Marketing, Customer Support (EU) 

Government & Public Affairs 

Manufacturing plant 

Europe 

Brussels 

Madrid 

Amsterdam 

Government & Public Affairs (EU) 

Sales 

Business Development 

Malaysia 

Kedah 

Three Manufacturing Plants 

(groundbreaking of first plant April 2007) 



9


Fully Integrated, Automated and Continuous Thin Film Process 

Glass in 

~2.5 hours 

Semiconductor 

Deposition 

Final Assembly 

& Test 

Cell Definition 

Module out 

 

 

99% reduction in high-cost 

semiconductor material 

Fully integrated, continuous 

process vs. batch processing 

Large (2’x4’) substrate vs. 6” 

wafers 

vs. Crystalline Silicon Batch Processing 

Feedstock Ingot Wafer Solar Cell 

Solar Module 



Intellectual Property 

 

Over 90 U.S. & foreign 

patents granted and 

pending 

Substantial trade secrets / 

know-how surrounding 

process, device design and 

product packaging 

 

 

Proprietary equipment 

designs 

Exclusive relationships with 

key vendors 



Disciplined Replication Process 

Proven replication at Base Plant 

Continuous improvement 

methodologies 

570 MW 

120 

“Copy Smart” 

replication 

330 MW 

120 

210 MW 

120 120 

120 120 120 

75 MW 

50 60 60 60 

25 MW 

25 25 30 30 30 

2005 2006 2007 2008 2009 

Ohio Base Plant Ohio Expansion German Facility Malaysia 1 Malaysia 2 Malaysia 3 



13


First Solar Series 2 Module Features 

Frameless glass-glass laminate (60 x 120 cm, 27 lbs) is 

durable and recyclable 

Power increments of 2.5W (5% rating tolerance) with power 

per module of up to 72W 

High energy yield in real operating conditions (PR>80%): 

• Low temperature coefficient (-0.25%/ o C) 

• Excellent low light response 

Robust against shading in landscape orientation (perpendicular 

to cells) 

Certified for reliability and safety according to IEC 61646 and 

SK II @ 1000V 

Manufacturing certified to ISO9001:2000 quality and 

ISO14001:2004 environmental standards 

25 year Power Output Warranty for 80% of nominal power 

subject to warranty terms & conditions 

Pre-financed collection & recycling program 



Proven Record of Increasing Module Conversion Efficiencies 

500,000 

11.0% 

450,000 

400,000 

10.0% 

350,000 

9.0% 

300,000 

250,000 

8.0% 

200,000 

150,000 

7.0% 

100,000 

50,000 

6.0% 

0 

Q3'01 Q4'01 Q1'02 Q2'02 Q3'02 Q4'02 Q1'03 Q2'03 Q3'03 Q4'03 Q1'04 Q2'04 Q3'04 Q4'04 Q1'05 Q2'05 Q3'05 Q4'05 Q1'06 Q2'06 Q3'06 Q4'06 Q1'07 Q2'07 

5.0% 



Proven Field Performance 

Documented and approved designs define the module mounting, 

electrical design, and the system energy yield expected in order to 

ensure that systems are designed to perform optimally 

First Solar monitors the performance of > 20MW of installed modules 

in a wide range of systems to ensure modules perform as designed 

Predicted Energy Ratio (PER%) 

(Error Bars =Standard Deviation) 

120% 

115% 

110% 

105% 

100% 

95% 

90% 

85% 

80% 

Month 

MW Monitored 

Monitored MW PER% Expectation 



Tom Hansen, VP & Technical Advisor of Tucson Electric Power 

kWh/kWp 

2,500 

2,000 

1,500 

1,000 

Springerville Generation Station 

Specific Energy Yield 

First 

Solar 

X-Si 

A-Si 

“One of the more fascinating things 

we’ve found by evaluating the First 

Solar modules at Springerville, is 

that they turn on earlier in the 

morning and they actually put more 

voltage out earlier than either the 

crystalline or amorphous silicon 

modules.” 

500 

1.2 

Relative Efficiency vs. Irradiance, 

CdS/CdTe Thin-film and Polycrystalline Si Systems 

0 

All data m easured by Tucson Electric Pow er - Springe rville Generation Station 

Measured w ith Revenue Grade Meter Equipm ent from Septem ber 2003 - October 2004 

“The other thing that is fascinating…, is that 

they produce more power from a lower light 

level than either crystalline or amorphous 

silicon modules. On cloudy days, we see 10%, 

sometimes as much as 12% more energy 

production from the thin film First Solar 

modules than from the crystalline modules.” 

Relative Efficiency 

1.1 

1 

0.9 

0.8 

0.7 

0.6 

0.5 

FS thin-film CdS/CdTe System 

Polycrystalline Si System 

6th order fit to FS CdS/CdTe data 

6th order fit to polycrystalline Si data 

0 200 400 600 800 1000 

Irradiance in Plane-of-Array [W/m 2 ] 



18


Target Applications 

Ground Mounted Systems 

• Typically Multi-MW 

Commercial Roof Mounted Systems 

• Typically 30kW to 1MW+ 



Geographic Markets 

Canada 

Greece 

Portugal 

US 

South 

Korea 

Spain 

France 

Italy 

Germany 



Sales Channels 

First Solar sells modules to project developers and alternative energy 

power plant operators under multi-year framework agreements. 

Project Developers design and develop turnkey commercial grid 

connected solar power plants. 



22


Our 3-Point Environmental Plan 

1 

Convert 

mining byproducts 

and waste to clean, 

renewable 

energy 

2 

Produce, use and 

renew solar modules 

in a perpetual, 

environmentally 

safe life cycle 

3 

Reduce 

toxic emissions by 

substituting solar 

energy for 

fossil fuels 


Summary 

24

Summary 

 

 

 

 

First Solar has developed a 

breakthrough thin film module 

product and high volume 

production capability 

Continuous improvements and 

rapid scale-up of manufacturing 

capacity have resulted in the 

lowest production cost per watt in 

the PV industry 

An innovative pre-funded module 

collection and recycling program 

ensures products are life-cycle 

managed for optimal 

environmental benefits 

First Solar is enabling solar to 

become a sustainable, mainstream 

source of energy 


Project Profiles 

Relative size of 6MW 

project in Rote Jahne 

40 MW 

Brandis, Germany 

Project Developer: juwi 

Under Construction 

8MW Commissioned/ 10MW Installed 



1.25 MW 

Dimbach, Germany 

Project Developer: Blitzstrom 

6 MW 

Rote Jahne, Germany 




120 kW 

Meppen, Germany 

Project Developer: R+P Sun Energy 

1.4 MW 

Gescher, Germany 

Project Developer: R+P Sun Energy 



1 MW 

Reussenköge, Germany 

Project Developer: Phoenix Solar 

1.4 MW 

Deponie Sinzheim, Germany 



Six Sigma Strategies that work – for First Solar 

Continuous Improvement is one of First Solar’s 5 core values 

30

5 Keys to Success 

Understand the needs of your company 

– For example, the need to drive costs, and the associated levers 

Customer Focus 

– Champions – Project Selection and tollgate reviews 

– Process Owners – enabling through mentorship 

Create a pull 

– Don’t push – create pull 

Deployment strategy 

– That fits the needs of your organization 

Provide the best tools available 

– JMP enables the practitioner with power, speed and 

visualization 


Deployment Strategies 

Project Selection starts 2 months ahead of each cycle 

Training occurs in 17 weeks cycles – 3 times per year 

Weeks 1-5: Rapid Sigma – DMAIC Problem Solving Process 

Weeks 6-8: MSA 

Week 9: Project Selection/Champion training for next wave 

Weeks 9-12: ESDA 

Weeks 13-15: Robust DOE 

Weeks 16-17: SPC 

Other tips: 

Classes are 2 hours in length, 3X’s a week 

Use a “learn by doing” approach 


My First Success Story using JMP 

33

My First Success Story using JMP 

Introduced to JMP in 1993 by Tom Little while at Read-Rite Corp in Milpitas CA 

Used JMP to visualize the interactions between OW, PW50, Amplitude and Resolution 

and their inter-relationships to Throat Height 

Throat Height, for an inductive write-read head, is one of the most critical design 

dimensions 

DEC RZ28L was “starved” for OW – Yields were 27% - breakeven yields 55% 

The common tribal knowledge suggested going short as possible 

After not more than 20 minutes of data analysis using JMP was able to establish that 

the TH was far too short (Multivariate tool with brushing) 

• But Kuhn Steve – we need more OW not less! 

• Get a refund from Cal Poly 

Yields with the longer TH were greater than 73%! 

Standardized the method for a non destructive Throat Height optimization 

Pradip Thyamballi improved the magnetic models to include the interactions with 

media and spacing 

JMP became the undisputed program of choice – extends to many engineers in S.E. 

Asia including Western Digital today 


Reducing Variation 

Why place effort on reducing variation? 

Cost savings 

Improved experimental results 

Traditional approaches 

OFAT experimentation 

Main effects experiments 

Beat on Suppliers 

ANOVA 

 

 

Focused on mean shifts 

Mean shifts represent only 20% of the overall variation 


Variation Reduction Tools used at First Solar 

Partition of Variation (POV) 

Includes the between and within components of variation 

Robust Design 

Early work was based on Taguchi methods 

Ideal function thinking 

Noise Strategy 

Orthogonal Arrays 

Optimizing SNR of the main effects 

Today FS enhances that same philosophy with Response Surface Methodology 

Capture interactions and curvature effects 

Design to fit the problem as opposed to fixed arrays 

The real world is messy! 

Optimization using partial derivatives with respect to the noise factors 

Root Cause of BOB’s and WOW’s (Hi/Lo Analysis) 


Partition of Variation 

POV script developed by Dr. Thomas Little 

www.dr-tom.com 

The POV script provides a Pareto of the variance components 

Between 

Within 

Interactions (for crossed) 

Our exploration with this tool has proven useful in providing 

direction of where to focus vital engineering resources 

The individual components are not claimed to be 100% 

accurate, however the Pareto has been consistent with our 

internal findings 


FS Macro Process Map 

Laser ID 

Front end 

operations 

Edges 

finishing 

Cleaning 

Semiconductor 

deposit 

CdCl 2 

Spray 

Activation 

Sunnyside 

etch 

Laser 

Scribe 1 

Submodule 

operations 

Post Metal 

Heat Treat 

Automatic 

edge 

delete 

Laser 

Scribe 3 

Metallization 

Wet Etch 

Laser Scribe 

2 

Photoresist 

application 

Assembly 

operations 

Lamination 

Lay up 

Lamination Cordplate Final IV 

Module 

Detail 

Box and Ship 


POV Example One 

Process Steps 

1-4 

Production Lines 

A 

The Design is Crossed, not balanced, 2^4 

or 16 levels 

Day to day operations are run with this 

constant mixing 

MD-5-921 US October 2007 39 

B 

1 

2 

3 

4 

Frequencies 

Level 

AAAA 

AAAB 

AABA 

AABB 

ABAA 

ABAB 

ABBA 

ABBB 

BAAA 

BAAB 

BABA 

BABB 

BBAA 

BBAB 

BBBA 

BBBB 

Total 

Count 

58 

120 

86 

71 

132 

181 

186 

134 

49 

108 

82 

66 

152 

183 

189 

122 

1919

The Variability Chart 

Y 1 

Std Dev 

11.0 

10.5 

10.0 

9.5 

0.20 

0.15 

0.10 

0.05 

0.00 

The power of 

visualization – 

all the data 

broken down 

on one page 

A B A B A B A B A B A B A B A B Process 4 

A B A B A B A B Process 3 

A B A B Process 2 

A B Process 1 


Residual Maximum Likelihood Model 

REML - Variance Components 

Component 

Total 

Within 

Process 3*Process 4 

Process 1 



Process 3 

Process 1*Process 2*Process 3*Process 4 

Process 1*Process 3*Process 4 



Process 2 


Process 4 




Var 

Component 

0.04098051 

0.03236716 

0.00469185 

0.00115908 

0.00093237 

0.00079189 

0.00047601 

0.00022384 

0.00017669 

0.00009557 

0.00004709 

0.00001897 

0.00000000 

0.00000000 

0.00000000 

0.00000000 

0.00000000 

% of Total 20 40 60 80 

100.0 

79.0 

11.4 

2.8 

2.3 

1.9 

1.2 

0.5462 

0.4312 

0.2332 

0.1149 

0.0463 

0.0 

0.0 

0.0 

0.0 

0.0 

~80% of the variation is “within”, but not characterized further 


Variability Chart – Example One 

Y 1 

11.0 

10.5 

Visual pattern for between 

variation common to 

Process3*Process4 

Std Dev 

10.0 

9.5 

0.20 

0.15 

0.10 

0.05 

0.00 




A B Process 1 

POV contributes 17% 

of Overall Variation 

to Between 

Between top 3 Pareto 

Process 3 6.4% 

P3*P4 3.8% 

Process 4 1.8% 


Variability Chart – Example One 

Y 1 

11.0 

10.5 

Visual pattern for 

within component 

common to Process2 

Std Dev 

10.0 

9.5 

0.20 

0.15 

0.10 

0.05 

Within top 4 Pareto 

Common 43% 

Process 2 29% 

Process 1 7% 

P3*P4 2% 

0.00 




A B Process 1 

Entitlement: B- 

line of Process 

Step 2 should 

match A line 


Text Book Example of POV 

11.5 

Y2 is Step 4 related 

11.0 

1.27 

1.26 

1.25 

1.24 

1.23 

1.22 

121 

Voc 

Isc 

10.5 

Y1 

EfficiencyTotal 

10.0 

Y2 

Avg=10.60722 

9.5 

Metal Line 

Step 4 

100 

99 

98 

Y3 97 

96 

95 

94 

93 

92 

Y3 is Step 2 related 

FFO-1-2-A 

FFO-1-2-B 

FFO-1-2-A 

FFO-1-2-B 

FFO-1-2-A 

FFO-1-2-B 

FFO-1-2-A 

FFO-1-2-B 

FFO-1-2-A 

FFO-1-2-B 

FFO-1-2-A 

FFO-1-2-B 

FFO-1-2-A 

FFO-1-2-B 

FFO-1-2-A 

FFO-1-2-B 

FFO-1-2-A 

FFO-1-2-B 

FFO-1-2-A 

FFO-1-2-B 

FFO-1-2-A 

FFO-1-2-B 

FFO-1-2-A 

FFO-1-2-B 

NPR Line 

Step 3 

Step 2 

Step 1 

FFO-1-2-A FFO-1-2-B FFO-1-2-A FFO-1-2-BCdCl2 Line 

FFO-1-2-A FFO-1-2-B Coat Line 


Another Text Book Example 

Process Step 2 

Line B 

High Variation (21%) 

Also evidence of 

step3*step 4 

interaction (7%) 

Step 4 

PBG-1-2-A 

PBG-1-2-B 

PBG-1-2-A 

PBG-1-2-B 

PBG-1-2-A 

PBG-1-2-B 

PBG-1-2-A 

PBG-1-2-B 

PBG-1-2-A 

PBG-1-2-B 

PBG-1-2-A 

PBG-1-2-B 

PBG-1-2-A 

PBG-1-2-B 

PBG-1-2-A 

PBG-1-2-B 

PBG-1-2-A 

PBG-1-2-B 

PBG-1-2-A 

PBG-1-2-B 

PBG-1-2-A 

PBG-1-2-B 

PBG-1-2-A 

PBG-1-2-B 

PBG-1-2-A PBG-1-2-B PBG-1-2-A PBG-1-2-B 

Step 3 

Step 2 

Step 1 

PBG-1-2-A PBG-1-2-B 


POV Advantage 

Within variation typically represents 80% of the 

overall variation 

Mean shifts are often more gratifying to work on 

However, alone they don’t constitute the largest source of 

variation 

In the worse case, mean shifts may even be a distraction 

POV provides a quantification of the within variation 

entitlements 

The quantification is not absolute, however, experience 

suggests the Pareto scales properly 


POV Injection Example 

Prefer n>40 for each sub group 

The following example represents ~24 hours of production 

for one pair of lines 

Process Steps 

1-4 


A 

B 

Frequencies 

Level 

A-A-A-A 

A-A-A-B 

A-A-B-A 

A-A-B-B 

A-B-A-A 

A-B-A-B 

A-B-B-A 

A-B-B-B 

B-A-A-A 

B-A-A-B 

B-A-B-A 

B-A-B-B 

B-B-A-A 

B-B-A-B 

B-B-B-A 

B-B-B-B 

G-A-A-A 

G-A-A-B 

G-A-B-A 

G-A-B-B 

G-B-A-A 

G-B-A-B 

G-B-B-A 

G-B-B-B 

Total 

Count 

311 

301 

370 

375 

128 

128 

141 

149 

100 

100 

78 

100 

50 

34 

97 

99 

40 

37 

62 

42 

80 

68 

173 

174 

3237 

MD-5-921 US October 2007 47 

G 

Dev. 

Tool 

The Design is Crossed, not balanced with 24 

combinations (2^4 + 2^3 levels) 

1 

2 

3 

4

Variability Chart for Injection Example 

11.5 

11.0 

10.5 

10.0 

9.5 

9.0 

8.5 

8.0 

7.5 0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

0.0 

-0.1 

A B A B A B A B A B A B A B A B A B A B A B A B Process 4 

A B A B A B A B A B A B Process 3 

A B A B A B Process 2 

A B G Process 1 

Visualization of 

Process Step 4 

interacting with 

process Step 1 

on Tool G 

This group is 

amongst the 

least 

variation 

seen to date 


Calculating the Variance with REML 

Variance Components with REML 

Component 

Total 

Within 




Process 2 

Process 1*Process 2*Process 3*Process 4 




Process 1 

Process 3 


Process 4 




Var Component 

0.14658430 

0.11268850 

0.02173898 

0.00438000 

0.00309395 

0.00150594 

0.00094134 

0.00088741 

0.00081464 

0.00053354 

0.00000000 

0.00000000 

0.00000000 

0.00000000 

0.00000000 

0.00000000 

0.00000000 

% of Total 20 40 60 80 

100.0 

76.9 

14.8 

3.0 

2.1 

1.0 

0.6422 

0.6054 

0.5558 

0.364 

0.0 

0.0 

0.0 

0.0 

0.0 

0.0 

0.0 

REML again reveals nearly 80% of the variation is “Within” 

Process1*Process4 between interaction is 15% of overall 


POV Summary Table 

Component 

Between Total 

Between Process 1 




Between Process 1*Process 2 






Within Total 

Within Process 1 




Within Process 1*Process 2 






Common 

Total 

Pop Variance 

0.0273 

0.0129 

0.0027 

0.0002 

0.0004 

0.0010 

0.0005 

0.0087 

0.0001 

0.0000 

0.0008 

0.1129 

0.0199 

0.0002 

0.0001 

0.0328 

0.0020 

0.0046 

0.0362 

0.0011 

0.0002 

0.0005 

0.0153 

0.1402 

. 

. 

. 

% of Total 

19.48 

9.16 

1.95 

0.16 

0.28 

0.73 

0.38 

6.18 

0.04 

0.01 

0.59 

80.52 

14.18 

0.12 

0.10 

23.41 

1.42 

3.26 

25.81 

0.77 

0.12 

0.39 

10.94 

100.00 

. 

. 

. 

Sig. 

* 

* 

* 

* 

* 

* 

* 

* 

* 

POV Method 

calculates within 

as 80% of Total 

Process 

1*Process 4 

Interaction as 

32% of total 


Pareto of the Variance Components 

100 

75 

50 

N=100 

Combined effect of 

Process 1, 4 and 

P1*P4 = 68% 

Percent 

25 

0 




Common 


















Variance Components 


POV Injection – Process Step 2 

Standard 


A B S 

Development 

Line 

Process Flow 

1 

2 

3 

4 


POV Injection Example 2 – The “HOT” path 

Standard 


A B C 

Development 

Line 

Process Flow 

A B S 

G 

1 

2 

3 

4 


A method for Rapid Identification of Common Failure 

Modes 

54

Exploring Relationships for Failure Mode Patterns 

Y1 

Y2 

These 4 - Y’s are 

highly interactive 

performance 

characteristics 

Y3 

Sweet 

spot 

Failure 

Modes 

Failure Mode = f(Y1, Y2, Y3, Y4) 

Y4 


Establishing the Link between Failure Modes and 

Process Lineage 

Step 1: Establish Failure Modes from the multivariate 

relationships (Y’s) 

Step 2: Contingency Analysis of Failure Mode (Y) vs. Lineage 

(X) 

Step 3: Calculate the % of cell Chi-Square/Sum Chi-Square 

Cell Chi Square is the Chi-square values computed for each cell as 

(O - E)2 / E 

Sum cell Chi Square is the sum of all cell ChiSquare for each given 

failure mode 

Step 4: Explore commonalities where the % Chi-Square is large 

and Deviation is positive 


% Cell Chi-Square Example 

% Cell Chi- 

Square 

Heritage FF n FF 

GAAA 1% 188 -1 

GAAB 1% 144 -1 

GABA 1% 256 -2 

GABB 1% 300 -2 

GACC 0% 82 -1 

GBAA 1% 343 -2 

GBAB 1% 289 -2 

GBBA 1% 327 -2 

GBBB 1% 509 -3 

GBCC 0% 97 -1 

GCAA 0% 200 0 

GCAB 1% 185 -1 

GCBA 0% 294 -1 

GCBB 0% 182 0 

GCCC 3% 8756 17 

SAAA 0% 61 0 

SAAB 0% 51 0 

SABA 0% 62 0 

SABB 0% 63 0 

SBAA 0% 72 0 

SBAB 0% 81 -1 

SBBA 0% 112 -1 

SBBB 0% 130 -1 

SCAA 2% 211 3 

SCAB 0% 277 -1 

SCBA 1% 241 1 

SCBB 1% 205 -1 

SCCC 9% 7497 29 

n 

Deviation 

XCCC 

Sum of % Chi-square = 12% 

Deviation = 46 

% Deviation = 46/15,253 = .3% 


% Chi-Square (Cont). 

% Cell Chi- 

Square 

Heritage FF n FF 

AAAA 2% 2284 8 

AAAB 0% 2227 -3 

AABA 6% 2926 -15 

AABB 11% 2859 -20 

AACC 0% 96 -1 

ABAA 0% 1897 3 

ABAB 2% 1919 8 

ABBA 5% 2302 -12 

ABBB 4% 2483 -11 

ABCC 0% 110 -1 

ACCC 0% 262 1 

BAAA 0% 2129 2 

BAAB 0% 2166 -3 

BABA 4% 2673 -11 

BABB 1% 2528 -4 

BACC 0% 94 -1 

BBAA 0% 1762 0 

BBAB 6% 1781 12 

BBBA 1% 2282 6 

BBBB 7% 2401 14 

BBCC 0% 98 -1 

BCBB 18% 85 4 

n 

Deviation 

AxAx 

Short Hand: 

Sum of %Chi-Square = 4% 

Deviation = 16 

% Deviation = 16/4203 = .38% 

AxAx (4%, 16, .38%) 

BxBx (26%, 24, .5%) 

BxxB (31%, 30, .7%) 

xCxx (30%, 50, .32%) 


Robust Design 

A method for minimizing the effects of variation 

59

Robust Design applied to a new Gage 

Robust Design is often applied to process improvement 

A new gage presents the ideal opportunity for Robust 

Design 

Rather than selecting critical set up factors based on 

manufacturing preference or “Best Guess” a quick and 

practical Robust DOE can help design in excellent gage 

capability before it hits the shop floor 

This particular design was conceived, executed and 

analyzed in less than 4 days. 


How the gage works 

Glass Plate Motion 

X Location 

1 2 3 4 5 

Laser ID 

1 

2 

3 

. 

. 

. 

10 

Y Location 

5 heads are held in place 

along a fixed bar as the 

plate is passed from the 

top of the plate to the 

bottom of the plate. 

Height is calibrated to a 

fixed position by laser 

positioning. 

Plate temperature is also 

measured with IR sensor 

built in to the test system 


Experimental Objectives 

Can the measurement be performed reliably on incoming 

plates? 

Incoming plates are covered with parting medium which is 

used to facilitate glass on glass separation 

If the glass must be cleaned prior to measurement it is a 

major disadvantage to manufacturing 

Understand the relationship of temperature, sensor height, 

and plate speed vs. the gage output (Y2) nominal and 

variation 

Provide an operational guide for gage set up which helps 

ensure a reliable measurement 


The Design 

JMP Custom Design for RSM 

Entered Factors as follows: 

One Categorical Variable: Condition, easy to change 

One uncontrolled variable: Temperature (Measured in-situ) 

Two continuous variable: Speed (Very Hard to change) and 

Height (hard to change, actual height measured in situ) 

Replicates: 

Replicates were unknown as we had given time window and 

wanted to run as many as possible in the given time 


The Real World is messy! 

After running the experiment we found what was 

actually run was different from what was planned 

Gage owner made some executive decisions based on 

practicality 

What to do? 

Plugged data back into Custom DOE 

Entered all the factors as covariates 

Created a new model 


The Analysis 

Multivariate Analysis (exclude gross outliers, double check 

the design range and values) 

Ran step wise regression with main effects, interactions and 

2nd level quadratics 

Selected model terms with p

Unrealistic Values (Gross Outliers) 

1 2 3 4 5 6 7 8 9 10 

1 2 3 4 5 

Y Location 

X Location 

Key Observation: Virtually 

all of the unrealistic values 

are located on the leading 

edge of the plate (Y

Multivariate of Design Factors 

Scatterplot Matrix - Condition = Clean 

Scatterplot Matrix - Condition = Dirty 

60 

50 

50 

40 

30 

Speed 

40 

30 

Speed 

20 

20 

10 

10 

85 

83 

81 

79 

Temp 

75 

74 

Temp 

77 

75 

73 

10 

9 

8 

Height 

73 

72 

11 

10 

9 

8 

Height 

7 

7 

6 

6 

10 20 30 40 50 

73 75 77 79 81 83 85 

6 7 8 9 10 

10 20 30 40 50 60 

72 73 74 75 

6 7 8 9 10 11 


Interactions 

Interaction Profiles 

10 

Y1 

Y1 

Y1 

9.7 

9.5 

9.3 

9.7 

9.5 

9.3 

9.7 

9.5 

9.3 

Speed 

72 

85 

5.7 

11.3 

Temp 

60 

11.3 

5.7 

Height 

10 

60 

85 

72 

Speed Temp Height 

10 

30 

50 

70 

72 

75 

78 

81 

84 

87 

6 

8 

10 

12 


Fit Model 

Actual by Predicted Plot 

Parameter Estimates 

Y1 Actual 

9.6 

9.5 

9.4 

9.40 9.50 9.60 

Condition 

Height Speed 

CleanHi10 

CleanHi60 

CleanLo10 

CleanLo35 

DirtyHi10 

DirtyHi60 

DirtyLo10 

DirtyLo60 

Term 

Intercept 

Condition[Clean] 

Speed(10,60) 

Temp(72,85) 

Height(5.7,11.3) 

Speed*Temp 

Speed*Height 

Temp*Height 

Speed*Speed 

Estimate 

9.4144147 

0.0659857 

-0.1329 

0.0086641 

-0.042314 

-0.175065 

0.0135683 

0.100659 

0.0779556 

Std Error 

0.007973 

0.00317 

0.002974 

0.007149 

0.005219 

0.005426 

0.003391 

0.008214 

0.00681 

t Ratio 

1180.8 

20.82 

-44.69 

1.21 

-8.11 

-32.26 

4.00 

12.25 

11.45 

Prob>|t| 

0.0000* 

The Prediction Profile 

9.5 

Y1 

9.434658 

±0.013339 

9.4 

0.04 

0.02 

0 

-0.02 

dY1/dtemp 

1.89e-10 

±0 

-0.04 

Clean 

Dirty 

10 

20 

30 

40 

50 

60 

72 

74 

76 

78 

80 

6 

7 

8 

0 

0.25 

0.5 

0.75 

1 

Desirability 

1 

0 0.25 0.75 1 

Dirty 

Condition 

26.47804 

Speed 

74.99654 

Temp 

6.599008 

Height 

Desirability 


Capability Comparison 

Nominal Setting 

(Height 9mm, Speed 40cm/sec) 

Optimized Settings 

(Height 5.5mm, Speed 20cm/sec) 

LSL 

Target 

USL 

750 

500 

Count 

LSL 

Target 

USL 

4000 

3000 

2000 

Count 

250 

1000 

9.45 9.47 9.49 9.51 9.53 9.55 

9.45 9.47 9.49 9.51 9.53 9.55 

CpK = .412 

Sigma = .013 

CpK=2.865 

Sigma = .0023 

Before After Optimization 

Improvement 

Ratio 

Sigma 0.013 0.0023 5.7 

Sigma^2 0.000169 0.00000529 31.9 

Measurement Capability Spec 9.5+/-.02 


Solar Fun Fact 

Using current solar technology, a surface area covering 

just 1/10 th of the state of Nevada would be enough to 

supply all the electricity needs for the entire US 


In Summary 

First Solar 

JMP 

 

 

 

 

 

leading enabler of cost effective solar energy solutions 

committed to improving the global environment 

utilizes world class continuous improvement methodologies 

Enables the practitioner and provides a data analysis 

platform that greatly enhances First Solar’s continuous 

improvement efforts 

POV enables a quick daily determination of Six Sigma 

entitlements

First Solar - JMP

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