MJOct'022002 ASQ Fall Tech ConferencePage 1 Integration of EPC and SPC for effective Process Control...

Oct'02 2002 ASQ Fall Tech Conference MJ

Integration of EPC and SPC for effective Process Control

by

Mani Janakiram, Intel Corporation

Doug Montgomery, Bert Keats, Arizona State University

Objective of this presentation:

• Provide introduction on EPC & SPC as applied to process control• Show how SPC & EPC can be integrated (simulation + case study)• Discuss types of APC techniques used in the semiconductor industry


Introduction

• Variability exists in all the processes. Reduction of output variability is critical to

process improvement

• Process variation may be due to random cause or assignable cause

• The objective of process control is to keep the output as close as possible to the

target all the time

• The output series can be either independent or correlated

• Two types of process control techniques exist

– Statistical process control (SPC)

– Engineering process control (EPC)

• Shewhart, EWMA and CUSUM techniques are the common SPC schemes

• Integral, PI and PID control schemes with feedback adjustment are the common

EPC schemes


Monitor Process(take samples, plot and look for assignable causes)

Stop the Process

Identify assignable cause(s)

Eliminate assignable cause(s)

Process under controlYes

No

Statistical Process Control (SPC)

Statistical Process Control (SPC) aims at achieving process stability and improving process capability by reducing variation. A set of problem solving tools are used in SPC which range from a simple Histogram to sophisticated control charts. SPC is normally applied in the form of open-loop control for process monitoring. Coefficient of variance and/or Process Capability Index (CpK) may be used as SPC indicators. Shewhart X-bar and R charts are commonly used for process monitoring.

SPC suits stationary processes exhibiting no drift/shift in process mean


SPC techniques used for process control

• For Independent output data

– Shewhart control charts (X-bar & R, C, NC, P, etc.)

– CUSUM control charts (for small shifts)

– EWMA control charts (for small shifts & also for correlated data)

• For autocorrelated output data

– Time series models (AR, MA, IMA (EWMA), ARMA & ARIMA)

– Special cause control charts for residuals

– Moving centerline charts

• Multivariate control charts (MISO, MIMO)

– T2 charts, Multivariate CUSUM & EWMA charts

– Principal component analysis (PCA)

– Partial least squares techniques (PLS)


Monitor Process & Compute next output(Compare with Target)

Compute adjustment (manipulated value)

Make adjustment to process input

Output equal TargetYes

No

Engineering Process Control (EPC)

Engineering Process Control (EPC) also aims at achieving process stability and improving process capability by reducing variation. EPC may be applied in the form of either open-loop or close-loop. The control mechanism could either be feedback or feedforward or combination of both. SPC techniques are often used in combination with EPC. Coefficient of variance and/or Process Capability Index (CpK) may be used as APC indicators.


Difference between SPC & EPC

SPC + EPC = APC

SPC EPC

Philosophy Minimize variability by detection

of and removal of process upsets

Minimize variability by

adjustment of process to

counter-act process upsets

Application Expectation of process stationarity Expectation of continuous

process drift

Deployment

1. Level Strategic Tactical

2. Target Quality characteristics Process parameters

3. Function Detecting disturbances Monitoring setpoints

4. Cost Large Negligible

5. Focus People and Methods Equipment

Correlation None Low to High

Results Process improvement Process optimization

Source: Messina, PhD dissertation


Open loop versus Close loop control

Process Controller

Process

Operator / Technician

Computer Interface

Mea

sure

d Pr

oces

sOu

tput

(man

ual)

Manipulated Process Output

Process Controller

ProcessProcess

Operator / TechnicianOperator / Technician

Computer Interface

Mea

sure

d Pr

oces

sOu

tput

(man

ual)


Process Controller

Process

Operator / Technician

Computer Interface

Measured Process Output


Process Controller

ProcessProcess

Operator / TechnicianOperator / Technician

Computer InterfaceComputer Interface

Measured Process Output


Close loop control is becoming a necessity for APC.


Discrete controllers used in EPC

• Proportional control -

– Correction is proportional to the error

• Xt = Kp * e(t) where Kp is the proportionality controller gain

• Integral control -

– Correction is proportional to the time integral of the error

• Xt = KI * e(u)du where KI is the integral controller gain

• Derivative control -

– Correction is a measure of rate of change of error

• Xt = Kp * (de(t)/dt) where Kp is the derivative controller gain

• Combination of the above controls are commonly used:

– PI –

– PD –

– PID - Note: Response is shown to a step input

The objective is to minimize mean squared error (MMSE) of the output deviation from target. Minimum mean squared error (MMSE) controllers are set to cancel out the minimum variance forecast made at time t of the disturbance at time t+1.However, when large adjustment is required, constrained controllers are used.


Techniques used to apply compensation

Disturbance

OutputManipulatedVariable

+-

Process

Feedforward Controller

+-

Feedback Controller

Process


+-

+-

DisturbanceFeedback control:Very commonly used. Output is compared to target, Corrective action is computed and applied on manipulated variable in close loop. Ex: CMP uniformity control using R2R feedback control.

Feedforward control:Used to eliminate measurable disturbances by adjusting manipulated variable. Can be applied in open loop or close loop. Ex: Alignment check in the lithography.

Cascade control:Multiple feedback (and feedforward) loops used to control multistage processes. Works well for processes with intermediate measurable response. Ex: Contact process involving CVD, CMP, Lithography & etch (RIE) sequence.

Disturbance


+-

Process

Feedforward Controller

+-

Feedback Controller

Feedback Controller


R2R and Real-time control definition

Process

Adjustment

Input OutputMeasurement (in-situ)

Process Measurement (ex-situ)

Adjustment

Input Output

Adjustment

R2R control: Set of algorithms to be used for on-line process control with the goal to reduce output variability as measured by the mean squared deviation from target. The R2R controller responds to post-process and summarized in-process measurements by updating process models between runs and providing a new recipe for use in the next run

Real-time control: On-line control and instead of minutes or hours before action is taken, the machine is shut down automatically when a computer algorithm discovers that the process is non-normal or out of control. Machine parameters rather than process parameters are measured and monitored.

R2R, SPC and Real-time process control fall under Fault detection & Classification (FDC) technique that can be used in open-loop or close-loop mode to ensure that variation is identified and necessary action is applied.


Relationship among APC components

Sensors

Regulatory Control

(End point detection)

Monitor Regulatory Controllers

Predictive Process Model

Supervisory Controller

Monitor Control System Key focus areas:1. Sensors2. Actuators3. Control algorithms4. Standards5. Integration6. Automation7. Analysis techniques

Source: Sematech AEC/APC Conference


Types of Disturbances

• Stochastic Disturbances

– Exists due to random variation occurring continuously in the process

– Disturbance can be either stationary (fixed mean) or non-stationary

– They can be modeled using time series models (AR, MA, IMA, ARMA, ARIMA)

• Deterministic Disturbances

– Exists due to sudden step or ramp changes in load variable at any time

– They can be modeled using differential equations & transfer function models like,

Pulse, Step, Ramp and Sinusoidal models

Pulse Step Ramp Sinusoidal

Any combination of the above can also exist


SPC and EPC techniques for drift/shift scenarios

• Normal random variates [iid(0,1)] was generated (min. of 1000 simulation runs and 1000 observations per simulation)

• A shift or drift in the mean was introduced at the 601st observation• The drift/shift magnitudes investigated were 0.5 to 5.0 with increments of 0.5• The performance indicators analyzed are:

– Output and adjustment variance (SPC/EPC schemes)– Average run length (ARL), standard deviation of run length (SRL) and false alarm (FA)

or out of control points (OOC)

-4

-2

0

2

4

6

8

10

1 56 111

166

221

276

331

386

441

496

551

606

661

716

771

observation number

y(t)

Y

YD

YS

Drift calculation:Yt (I, J) = Yn(I, J)+(I-NSTAT)*y*(t/(N–NSTAT)

Shift calculation:Ys (I, J) = Yn(I, J)+y*s

Where:Yt (I, J) = Ith drifting observation for Jth simulation run

Yn (I, J) = Ith normal random observation for Jth simulation run

NSTAT = First observation where the drift or shift is appliedN = Total number of observations in a simulation runy = Standard deviation of the 1st 200 observations

t = Drift change or delta magnitude s = Shift change or delta magnitude


SPC scheme (EWMA) for 1.5s drift/shift scenario

• In-control ARL of ~ 370 was used as the baseline• EWMA and CUSUM schemes were sensitive to small drift/shift scenarios• Small works well for smaller shifts and larger is suitable for larger shifts• Shewhart scheme is suitable for large drift/shift scenarios• Combined Shewhart-EWMA scheme performs better for both small and large

drift/shift scenarios

-1.5-1

-0.50

0.51

1.52

2.53

1 62 123

184

245

306

367

428

489

550

611

672

733

794

observation number

z(t)

Z

E_UCL

E_LCL

ZD

-1.5-1

-0.50

0.51

1.52

2.53

1 61 121

181

241

301

361

421

481

541

601

661

721

781

observation number

z(t)

Z

E_UCL

E_LCL

ZS

1.5 drift scenario 1.5 shift scenario


Standard integral and PI schemes

• Discrete form of MMSE for integral control scheme:

where xt is the adjustment, g is the gain, ej = Yj–T and is the EWMA parameter, Yt is the

output and T is the target

• Discrete form of MMSE for PI control scheme:

where is the first order dynamic parameter

• These adjustments can be either applied for each observation or can be applied based on SPC limits on the output in the feedback mode

t

1jje

g -

tx

1)-g(1- ttt eex


EPC and SPC/EPC schemes for drift/shift scenario

-1.5

-1

-0.5

0

0.5

1

1.51 63 125

187

249

311

373

435

497

559

621

683

745

observation number

zt &

xt

Zda

+EWMA

-EWMA

Xtd

-1.5

-1

-0.5

0

0.5

1

1.5

1 63 125

187

249

311

373

435

497

559

621

683

745

observation number

zt, &

xt

Zdl

+EWMA

-EWMA

Xtdl

EPC SPC/EPC

1.5 drift scenario

-2

-1.5

-1

-0.5

0

0.5

1

1.5

1 63 125

187

249

311

373

435

497

559

621

683

745

observation number

zt &

xt

Zsa

+EWMA

-EWMA

Xts

-2

-1.5

-1

-0.5

0

0.5

1

1.5

1 63 125

187

249

311

373

435

497

559

621

683

745

observation number

zt, &

xt

Zsl

+EWMA

-EWMA

Xtsl

EPC SPC/EPC

1.5 shift scenario


Comparison of SPC/EPC scheme with SPC Scheme

• Significant improvement in output variance is possible with SPC/EPC schemes• PI control schemes work better for shift scenarios and integral control schemes

work better for drift scenarios

SPC

(EWMA) SPC/EPC (Integral)

% Change SPC/EPC

(PI) % Change

2x - 0.0003 - 0.0003 -

0Shift (0.0557, 1.1117) 2

e 1.1117 0.1284 765.81 0.1284 765.81

2x - 0.0526 - 0.0654 -

1.5Drift (0.3643, 1.3799) 2

e 1.3799 0.1867 639.10 0.2529 445.63

2x - 0.1018 - 0.1204 -

1.5Shift (0.7587, 1.7478) 2

e 1.7478 0.3729 368.70 0.3899 348.27


Comparison of SPC/EPC scheme with EPC Scheme

• SPC/EPC schemes result in significant improvement in adjustment variance at the expense of slight increase in output variance

• SPC/EPC schemes reduce the frequency and magnitude of adjustment when compared to EPC schemes

EPC

(Integral) SPC/EPC (Integral)

% Change EPC (PI)

SPC/EPC (PI)

% Change

2x 0.0253 0.0003 98.81 0.0641 0.0003 99.53

0Shift (0.0557, 1.1117) 2

e 0.0699 0.1284 45.56 0.11 0.1284 45.56

2x 0.0671 0.0526 21.61 0.1058 0.0654 38.19

1.5Drift (0.3643, 1.3799) 2

e 0.1867 0.2415 22.69 0.2221 0.2529 26.18

2x 0.1198 0.1018 15.03 0.1584 0.1204 23.99

1.5Shift (0.7587, 1.7478) 2

e 0.3319 0.3729 10.99 0.3672 0.3899 14.88


Constrained integral and PI schemes

• Discrete form of constrained integral control scheme:

where xt is the adjustment, g is the gain, ej = Yj – T and is the EWMA parameter, Yt is

the output k is a constant and T is the target

• Discrete form of constrained PI control scheme:

where is the first order dynamic parameter, K1 and K2 are constants

• min(2x + 2

e) would result in optimal constrained controller

• These adjustments can be either applied for each observation or can be applied based on SPC limits on the output

t1 e )1( g

)1( kxkx tt

1-tt0

21101 ee )1(g

)1( )1()1(

k

xkxkkx ttt

Source: Box and Luceno


Constrained SPC/EPC (PI) schemes for 1.5s drift/shift scenario

• Reduction of output variability is clearly demonstrated in the figure

• No excessive adjustment will be made when constrained SPC/EPC scheme is used

• One adjustment is made when the process is under control

1.5 drift scenario 1.5 shift scenario

-4

-3

-2

-1

0

1

2

3

4

5

1 57 113

169

225

281

337

393

449

505

561

617

673

729

785

observation number

yt &

xt Ytd

Ytdla

Xtdl

-4

-3

-2

-1

0

1

2

3

4

5

1 57 113

169

225

281

337

393

449

505

561

617

673

729

785

observation number

yt &

xt Yts

Ytsla

Xtsl


-1.5

-1

-0.5

0

0.5

1

1 57 113

169

225

281

337

393

449

505

561

617

673

729

785

observation number

zt &

xt

Xtd

Xtd

Standard and constrained SPC/EPC (PI) schemes

-1.4-1.2

-1-0.8-0.6-0.4-0.2

00.20.40.6

1

61

12

1

18

1

24

1

30

1

36

1

42

1

48

1

54

1

60

1

66

1

72

1

78

1

observation number

zt &

xt

Xtdl

Xtdl

SPC/EPCEPC

1.5 drift scenario

-2

-1.5

-1

-0.5

0

0.5

1

1 57 113

169

225

281

337

393

449

505

561

617

673

729

785

observation number

zt &

xt

Xts

Xts

-2

-1.5

-1

-0.5

0

0.5

1

1

61

12

1

18

1

24

1

30

1

36

1

42

1

48

1

54

1

60

1

66

1

72

1

78

1

observation number

zt &

xt

Xtsl

Xtsl

SPC/EPCEPC

1.5 shift scenario


Constrained SPC/EPC (PI) scheme for drift scenarios

PIM-SPC/EPC Scheme PICM-SPC/EPC Scheme Drift

(mean, variance)

y 2

e

x 2

x

y 2

e

x 2

x

0.5 (0.1, 1.1146) 0.0877 0.1604 -0.0091 0.007 0.0888 0.1592 -0.0080 0.0028

1 (0.2321, 1.2182) 0.1691 0.2047 -0.0573 0.0384 0.1776 0.1972 -0.0488 0.0163

1.5 (0.3643, 1.3799) 0.2418 0.2529 -0.1141 0.0654 0.2585 0.2577 -0.0974 0.0353

2 (0.4964, 1.5997) 0.3229 0.3337 -0.1625 0.0994 0.3451 0.3515 -0.1403 0.0587

2.5 (0.6285, 1.8777) 0.4021 0.4357 -0.2128 0.1383 0.4326 0.4736 -0.1823 0.0875

3 (0.7606, 2.2138) 0.4812 0.5646 -0.2632 0.1874 0.5188 0.6224 -0.2256 0.1217

3.5 (0.8928, 2.6081) 0.5598 0.7128 -0.3141 0.2415 0.6045 0.7982 -0.2695 0.1614

4 (1.0249, 3.0605) 0.6371 0.8851 -0.3664 0.3039 0.6897 1.0010 -0.3138 0.2064

4.5 (1.2891, 3.5711) 0.7162 1.0770 -0.4168 0.3713 0.7757 1.2300 -0.3572 0.2571

5 (1.2891, 4.1399) 0.7945 1.2950 -0.468 0.449 0.8617 1.4850 -0.4008 0.3134


Constrained SPC/EPC (PI) scheme for drift scenarios

• The performance indicators analyzed are:– Output and adjustment variance

– ARL, SRL, and OOC

PIM-SPC/EPC Scheme PICM-SPC/EPC Scheme Drift (mean,

variance) ARL SRL OOC ARL SRL OOC

0.5 (0.1, 1.1146) 133136 2968088 3 79874 1256598 5

1 (0.2321, 1.2182) 2778 6348 142 1139 1300 338

1.5 (0.3643, 1.3799) 331.51 43.24 978 299.1 44.03 997

2 (0.4964, 1.5997) 254.02 34.31 1000 231.5 34.51 1000

2.5 (0.6285, 1.8777) 209.08 28.63 1000 190.4 28.96 1000

3 (0.7606, 2.2138) 178.2 25.67 1000 164.2 25.61 1000

3.5 (0.8928, 2.6081) 157.55 22.15 1000 144.4 21.42 1000

4 (1.0249, 3.0605) 140.1 19.28 1000 129.0 19.96 1000

4.5 (1.2891, 3.5711) 126.32 17.96 1000 116.6 18.57 1000

5 (1.2891, 4.1399) 115.39 16.89 1000 106.05 16.17 1000


Comparison of constrained scheme with standard SPC/EPC Scheme

Constrained schemes significantly reduce the adjustment variance at the expense of a slight increase in output variance

Integral control

Constrained Integral control

% Change

PI Control

Constrained PI Control

% Change

2x 0.0526 0.03 42.97 0.0654 0.0353 46.02 1.5Drift

2e 0.1867 0.2680 30.34 0.2529 0.2577 1.86

2x 0.1018 0.0611 39.98 0.1204 0.0719 40.28 1.5Shift

2e 0.3729 0.4443 16.07 0.3899 0.4204 7.25


Proposed adjustment scheme to use standard controller as a constrained controller

• The manipulated variable is subjected to suitable SPC schemes and

adjustments are made

– when the manipulated variable is within the control limits and

– also the output is outside 2 limits

• Adjustment can be either in open-loop or in closed-loop fashion

– In open-loop technique, the process is stopped for correction when either output

response or manipulated variable goes outside the control limits

– In closed-loop technique, the limit applied to the manipulated variable is integrated

with adjustment calculation

• The performance of the proposed adjustment scheme is comparable and

sometimes better than the mathematically complex constrained controllers

• It is easy to design and tune this controller for a process exhibiting drift or

sustained shift in the process mean


Proposed adjustment scheme to use standard controller as a constrained controller (cont.)

-2.0000

-1.5000

-1.0000

-0.5000

0.0000

0.5000

1.0000

1.5000

2.00001 63 125

187

249

311

373

435

497

559

621

683

745

observation number

xt &

zt

Zdl

LCL_X

Xtdl

UCL_X

-2.0000

-1.5000

-1.0000

-0.5000

0.0000

0.5000

1.0000

1.5000

2.0000

1 64 127

190

253

316

379

442

505

568

631

694

757

observation number

xt &

zt

Zsl

LCL_X

Xtsl

UCL_X

-3

-2

-1

0

1

2

3

4

5

1 61 121

181

241

301

361

421

481

541

601

661

721

781

observation number

zt &

xt

Zdl

+EWMA

-EWMA

Xtdl

-3

-2

-1

0

1

2

3

4

5

1 57 113

169

225

281

337

393

449

505

561

617

673

729

785

observation number

xt &

zt

Zdl

LCL_X

Xtdl

UCL_X

1.5 drift/shift scenario

5 drift scenario


Application of SPC/EPC techniques to a hybrid industry

• Current process is experiencing lot of

output variation (powder weight)

• Process disturbance is humidity and it

corresponds to an EWMA series w/ = 0.4

• Manipulated variable is amplitude of

vibration of the feeder bowl

• Integral control is used in SPC/EPC

xt = -(/3) (Yt – 1)

• Application of SPC/EPC resulted in

significant process improvement

• Constrained schemes were applied to avoid

excessive adjustment

Pass thro’ window

Index wheel

Barricade

Feeder bowlUnloaded part

Spoon dispense mechanismLoaded part

Pass thro’ window

Index wheel

Barricade

Feeder bowlUnloaded part

Spoon dispense mechanismLoaded part

Powder loading process of airbag initiator

t1 N̂0.6 )10.4(Y N̂ tt



Significant process improvement resulted due to the SPC/EPC scheme

0Subgroup 50 100

0.92

0.97

1.02

1.07

Sam

ple

Mea

n

Mean=1

UCL=1.057

LCL=0.9434

0.00

0.05

0.10

Sam

ple

Ran

ge

1 1

R=0.02130

UCL=0.06958

LCL=0

Shewhart control chart before SPC/EPC

5040302010Subgroup 0

1.05

1.00

0.95

Sam

ple

Mea

n

Mean=1

UCL=1.053

LCL=0.9473

0.080.070.060.050.040.030.020.010.00

Sam

ple

Ran

ge

R=0.009882

UCL=0.07337

LCL=0

Shewhart control chart under SPC/EPC



• SPC/EPC schemes outperform either the

SPC or the EPC schemes

• Constrained adjustment scheme

significantly reduces the adjustment

variability at the cost of a moderate

increase in output variance

• Performance of the proposed adjustment

scheme is comparable to the constrained

adjustment scheme

-0.85

-0.80

-0.75

-0.70

1 16 31 46 61 76 91 106

121

136

151

166

181

observations

xt

xt-ICM

xt-IM

xt-IMX

SPC (EWMA)

SPC/EPC (Integral control)

SPC/EPC (Constrained

integral control)

% Change

SPC/EPC (xt adjusted

integral control)

% Change

2y 0.0012 0.0004 0.0008 50 0.0005 20

2x - 0.0035 0.0028 20 0.0032 8.57



Monitoring the powder weight disturbance using control charts (SPC) and adjusting the bowl amplitude based on integral control (EPC) resulted in an effective SPC/EPC integration

Measure powder weight and feed data into control algorithm

Chart data (X-bar &R or EWMA)and continue process monitoring

No drifts and datawithin pre-control limits

Yes

No Data is outsidecontrol limits

Stop process andperform OCAP

Signal feeder bowl to adjust amplitude of vibration

Monitor process and chartamplitude of vibration(X-bar & R or EWMA)

Yes

No

SPC EPC

Integrated SPC/EPC

Measure powder weight and feed data into control algorithm

Chart data (X-bar &R or EWMA)and continue process monitoring

No drifts and datawithin pre-control limits

Yes

No Data is outsidecontrol limits

Stop process andperform OCAP

Signal feeder bowl to adjust amplitude of vibration

Monitor process and chartamplitude of vibration(X-bar & R or EWMA)

Yes

No

SPC EPC

Integrated SPC/EPC


Integrated process control in the semiconductor industry

• APC has been identified in the ITRS roadmap as one of key challenges

• Integration of the control elements (sensors, actuators, controllers) is critical

• Industry consortium is working on integration standards

• Benefits of an integrated SPC/EPC to semiconductor industry:

– Improved cycle time– Cost savings due to reduced non-

product wafers– Reduced operator induced errors– Improved process/product variability

Data analysis anddiagnostic systems

Correctiveaction log

OCAPDecision tree

Gage / Metrology

Gage / Metrology

SPC chart

Data

In-situ diagnosticsystem (EPC)

Real-time data

Diagnose andadjust or fix

Statisticalprocesscontrol (SPC)

Actuators

Controller

Sensors

Wafer In

Wafer Out

Adaptive control(Feedforward fromprior steps)

Data fromprior step

Process equipment

Data analysis anddiagnostic systems

Correctiveaction log

OCAPDecision tree

Gage / Metrology

Gage / Metrology

SPC chartSPC chart

Data

In-situ diagnosticsystem (EPC)

Real-time data

Diagnose andadjust or fix

Statisticalprocesscontrol (SPC)

Actuators

Controller

Sensors

Wafer In

Wafer Out

Adaptive control(Feedforward fromprior steps)

Data fromprior step

Process equipment


Summary

• No system left alone would be in a state of perfect statistical control and hence

both the drift and shift in the process mean are a reality

• Integrated SPC/EPC system is superior to either the SPC or the EPC schemes

• Constrained schemes significantly reduce the adjustment variance at the

expense of a slight increase in output deviation variance

• The proposed simple constrained adjustment scheme is comparable in

performance to the complicated constrained adjustment schemes in use today

• An integrated SPC/EPC methodology is very relevant to the semiconductor

industry

• An integrated SPC/EPC process results in improvement in cycle time and

throughput, reduction in non-product wafer use, improvement in operator

productivity and an overall reduction in process variability

• Much more work/research is required in the SPC/EPC area


Relevant research work

• Following is the list of relevant research work– Box, G. E. P. and Luceno, A (1997). Statistical Control by Monitoring and

Feedback Adjustment. John Wiley & Sons, New York, NY– Box, G. E. P. and Luceno, A (1997). “Discrete Proportional-Integral Adjustment

and Statistical Process Control”. Journal of Quality Technology 29 (3), pp 248-260– Janakiram, M. and Keats, J. B. (1998). “Combining SPC and EPC in a Hybrid

Industry”. Journal of Quality Technology 30 (3), pp 189-200– Lu, C. W., and Reynolds, M. R., Jr. (1999a). “EWMA Control Charts for

Monitoring the Mean of Autocorrelated Processes”. Journal of Quality Technology 31 (2), pp 166-188

– Lucas, J. M. and Saccucci, M. S. (1990). “Exponentially Weighted Moving Average Control Schemes: Properties and Enhancements”. Technometrics 32, pp 1-12

– MacGregor, J. F. (1990). “A different view of Funnel Experiment”. Journal of Quality Technology 22, pp 255-259

– Montgomery, D. C.; Keats, J. B.; Runger, G. C.; and Messina, W. S. (1994). “Integrating Statistical Process Control and Engineering Process Control”. Journal of Quality Technology 26, pp 79-87

– Montgomery, D. C. (1996). Introduction to Statistical Quality Control, 3rd ed. John Wiley & Sons, New York, NY

MJOct'022002 ASQ Fall Tech ConferencePage 1 Integration of EPC and SPC for effective Process Control...

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