statistical process control and computer integrated manufacturing
TRANSCRIPT
Lecture 16: From SPC to APC
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Statistical Process Controland
Computer Integrated Manufacturing
Run to Run ControlReal-Time SPC
Computer Integrated Manufacturing
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Auto-correlated DataOne important and widespread assumption in SPC is that the samples take random values that are independently andidentically distributed according to a normal distribution
With automated readings and high sampling rates, each reading statistically depends on its previous values. This implies the presence of autocorrelation defined as:
The IIND property must be restored before we apply any traditional SPC procedures.
yt = µ + et t = 1,2,... et ~ N (0, σ2)
ρk = Σ (yi - y)(yi+k - y)Σ (yi - y)2
= 0 k = 1,2,...
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Time Series Modeling
Various models have been used to describe and eliminate the autocorrelation from continuous data.A simple case exists when only one autocorrelation is present:
The IIND property can be restored if we use this model to "forecast" each new value and then use the forecasting error (an IIND random number) in the SPC procedure.
yt = µ + ϕ yt-1 + et et ~ N (0, σ2)µ' = µ / (1- ϕ), σ' = σ / 1-ϕ2
et = yt - yt
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Example: LPCVD Temperature Readings
Temps are not IIND since future readings can be predicted!
100806040200601
602
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604
605
606
607
608
Temp Readings from LPCVD Tube
Time607605603601
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602
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LPCVD Temp Autocorrelation
Temp (t)
Temp(t+1) = 758 - 0.253 Temp(t)
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The Residuals of the Prediction can be Used for SPC..
100806040200601
602
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606
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608Temp. Readings from LPCVD Tube
Time100806040200
-3
-2
-1
0
1
2
3Temperature Residuals
Time
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Estimated Time Series
A "Time Series" is a collection of observations generated sequentially through time.Successive observations are (usually) dependent.
Our objectives are to:Describe - features of a time series processExplain - relate observations to rules of behaviorForecast - see into the futureControl - alter parameters of the model
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Two Basic Flavors of Time Series ModelsStationary data (i.e. time independent mean, variance and autocorrelation structure) can be modelled as:Autoregressive
Moving Average
Mixture (i.e. Autoregressive + Moving Average) models.
zt = ϕ1 zt-1 + et et ~ N (0, σ2)zt = yt - μ
zt = θ1 et-1 + et et ~ N (0, σ2)zt = yt - μ
yt = μ + ϕ1zt-1 + ϕ2zt-2 +...+ ϕpzt-p
+ et - θ1et-1 - θ2et-2 -...- θqet-q
ϕ: autoregressiveθ: moving average
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First Order Autoregressive Model AR(1)This model assumes that the next reading can be predicted from the last reading according to a simple regression equation.
zt = ϕ1 zt-1 + et et ~ N (0, σ2)zt = yt - μ
-0.6-0.5-0.4-0.3-0.2-0.10.00.10.20.30.40.50.6
35 40 45 50 55 60
sampleARForecastError
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Higher order AR(p) and ACF, PACF representationHigher order autoregressive models are common in engineering. Their structure can be inferred from acf and pacf plots.Autocorrelation Function (acf):
ρk = Σ (yi - y)(yi+k - y)Σ (yi - y)2
= 0 k = 1,2,...
Partial Autocorrelation Function (pacf):
zt = ϕ1zt-1zt = ϕ1zt-1 + ϕ2zt-2
zt = ϕ1zt-1 + ϕ2zt-2 + ϕ3zt-3zt = ϕ1zt-1 + ϕ2zt-2 + ϕ3zt-3+ ϕ4xt-4
...
k
k
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First Order Moving Average Model MA(1)This model assumes that the next reading can be predicted from the last residual according to a simple regression equation.
zt = θ1 et-1 + et et ~ N (0, σ2)zt = yt - μ
time
zt
0
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Mixed AR & MA Models: ARMA(p,q)
In general, each new value depends not only on past readings but on past residuals as well. The general form is:
This structure is called ARMA (autoregressive moving average). The particular model is an ARMA(p,q).If the data is differentiated to become stationary, we get an ARIMA (Autoregressive, Integrated Moving Average) model.Structures also exist that describe seasonal variations and multivariate processes.
yt = μ + ϕ1zt-1 + ϕ2zt-2 +...+ ϕpzt-p+ et - θ1et-1 - θ2et-2 -...- θqet-q
ϕ: autoregressiveθ: moving average
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Summary on Time Series Models
Time Series Models are used to describe the "autocorrelation structure" within each real-time signal.After the autocorrelation structure has been described, it can be removed by means of time series filtering.The models we use are known as Box-Jenkins linear models.The generation of these models involves some statistical judgment.
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Shewhart and CUSUM time series residuals
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Fitted ARIMA(0,1,1) ExampleData ARIMA(0,1,1) Residuals
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So, what is RTSPC?RTSPC reads real-time signals from processing tools.
It automatically does ACF and PACF analysis to build and save time series models.
During production, RTSPC ”filters” the real-time signals.
The filtered residuals are combined using T2 statistics.
This analysis is done simultaneously in several levels:
• Real-Time Signals
• Wafer Averages
• Lot Averages
The multivariate T2 chart provides a robust real-time summary of machine “goodness”.
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The Equipment ControllerToday, the operation of individual pieces of equipment can be streamlined with the help of external software applications. SPC is just one of them.
FaultDiagnosis
CIM database
Local Database(s)
Equipment Supervisor
Maintenance
Monitoring
StatisticalProcessControl
Modelingand
SimulationRecipe
Generation
WorkcellCoordinator
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The Workcell ControllerMost process steps are so interrelated that must be controlled together using feed forward and feedback loops.Crucial pieces of equipment must by controlled by SPC throughout this operation.
EquipmentModel
Step
EquipmentModel
Step
AdaptiveControl
AdaptiveControl
test test test
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Model Based Control
All actions are based on the comparison of response surface models to actual equipment behavior.
Malfunction alarms are detected using a multivariate extension of the regression chart on the prediction residuals of the model.
Control alarms are detected with a multivariate CUSUMchart of the prediction residuals.
Control limits are based on experimental errors as well as on the model prediction errors due to regression. Hard limitson equipment inputs are also used.
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The Idea of Statistically Based Feedback Control
xo
yo
original RSM
new RSM
yn
xf
yf 1
2 453
1. Original Operation2. Process Shifts3. Shift is detected by MBSPC4. RSM is adapted5. New Recipe is generated
controlling input
response
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Feedback ControlModel-based, adaptive Feedback Control has been employed on several processes.
InitialSettings
RecipeUpdate
Spin Coat& Bake
ParameterEstimator
EquipmentModel
Controller t Test
M
ModelUpdate?
yes
No
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Feedback Control in Resist Application
AdaptiveController
Spin-Coat& Bake
ThicknessIncoming Outgoing
Equipment& Refl ectanceMeasurement
Wafer Wafer
InputSettings
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Feedback Control in Resist Application (cont.)
11800
12000
12200
12400
12600
0 10 20 30 40 50 60
TargetModel Prediction
Experimental Data
20242832364044
0 10 20 30 40 50
Wafer Number
60
Target
Model Prediction
Experimental Data
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Alarms in Resist Application Control
048
121620
0 10 20 30 40 50 60
Alarm (a)
Alarm (b) Alarm (c)
UCL
0
5
10
15
0 10 20 30 40 50 60
AlarmAlarm
Wafer Number
Alarm
UCL
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Adapting the Regression ModelThe regression model has many coefficients that mayneed adaptation.
What can be adapted depends on what measurementsare available.
yn = aox2+b’nx +c’’nyo = aox2+box +co
yn = aox2+box +c’nyn = anx2+bnx +c’n
1
23
4
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Adapting the Regression Model (cont.)In multivariate situations it is often not clear which of the gain or higher order variables can be adapted in the original model.
x1
x2
y
AB
C
In these cases the model is “rotated” so that it has orthogonal coefficients, along the principal components of the available observations. These coefficients are updated one at a time.
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Model Adaptation in Resist Application Control
-15000
-14500
-14000
-13500
1.85
1.90
1.95
2.00
0 10 20 30 40 50 60
131
132
133
134
135
0 10 20 30 40 50 60
Wafer Number
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Feed-Forward ControlModels can also be used to predict the outcome and correct ahead of time if necessary.
Exposure M Develop
RecipeUpdate
Model ProjectedCD Spread
InSpec?
StandardSetting
Yes
No
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Modified Charts for Feed Forward Control
LSLUCLLCL
Yield Loss
USL
σ (equipment spread)
σ/√n (prediction spread)
No FF
False Alarm Probability
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Feed Forward/Feedback Control Results (cont)
78
80
82
84
86
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90
92
0 5 10 15 20 25 30Wafer No
Open LoopFeedForward
Target = 88.75%
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Concurrent Control of Multiple Steps
SpinCoat
&Bake
Expose DevelopT&RMeas
R
Meas CDMeas
AdaptiveControl
AdaptiveControl
AdaptiveControl
Equip.
ModelEquip.Model
Equip.Model
InputsOriginal
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Resist Thickness Example
0 5 10 15 2011000
11500
12000Target = 11906Å
Wafer NumberClosed LoopOpen Loop
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Resist Thickness Input
0 5 10 15 2040
60
80
100
Wafer Number
Closed LoopOpen Loop
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Latent Image output
0 5 10 15 2070
80
90
100
Wafer Number
Target = 87.75%
Malfunction AlarmControl AlarmClosed Loop
Open Loop
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Latent Image Input
0 5 10 15 200.60
0.70
0.80
0.90
1.00
Wafer Number
Closed LoopOpen Loop
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Developer Output
0 5 10 15 202.50
2.60
2.70
2.80
Wafer Number
Target = 2.66 um
Malfunction AlarmControl AlarmClosed Loop
Open Loop
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Developer Input
Wafer Number0 5 10 15 20
Closed LoopOpen Loop
50
55
60
65
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Process Improvement Due to Run to Run Control
CD in μm CD in μm2.55 2.65
2
4
1
3
5
2.75 2.85 2.55 2.65
2
4
1
3
5
2.75 2.85
Closed Loop Operation Open Loop Operation
Target Target
Dev. Malf.
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Supervisory Control
The complete controller must be able to perform feedback and feed-forward control, along with automated diagnosis.
Feed-forward control must be performed in an optimum fashion over several pieces of the equipment that follow.
max Cpk
equip
control
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The Concept of Dynamic Specifications
ConstraintMapping
Step
Control
Model
Step
Control
Model
ConstraintMapping
Specs are enforced by a cost function which is defined in terms of the parameters passed between equipment.
A change is propagated upstream through the system by redefining specifications for all steps preceding the change.
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Specs to step A must change if step B "ages"
Step Step StepA B C
Fixed speclimits for BNew spec
limits for A
Out1A
Out2A
Out1B
Out2B
Mapping
Modelof B
PC2PC1
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Specs are outlined automatically - Stepper example
(Develop time varies between 55 and 65 seconds)Thickness
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An Example of Supervisory Control
0.80.91.01.11.21.31.41.51.61.71.81.9
CD (μm)0
5
10
15
20
Outliers
Target
0.80.91.01.11.21.31.41.51.61.71.81.9
CD (μm)0
5
10
15
20
Outliers
Target
0.80.91.01.11.21.31.41.51.61.71.81.9
CD(μm)0
5
10
15
20
Outliers
TargetBaseline FF/FB only Supervisory
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Results from Supervisory Control ApplicationBaseline FF/FB Only Supervisory
Th
PAC
LI
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Control Results - CD
Baseline FF/FB Only Supervisory
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Summary, so far• Real-time Statistical Process Control – Time
Series modeling.• Response surface models can be built based on
designed experiments and regression analysis.• Model-based Run-to-Run control is based on
control alarms and on malfunction alarms.• RSM models are being updated automatically as
equipment age.• Optimal, dynamic specifications can be used to
guide a complex process sequence.
• Next stop: a historical / industrial perspective
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Manufacturing Evolution…
Process/Equipment Model and Controller Real-time equipment model Diagnostic Engine
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DriftNoise
EquipmentModel
Advanced Process ControlAPC Requires Integrated RtRPC and FDC
(fromPrevioustool)
Post-ProcessMeasurementsFor Feed-forwardControl
Run to RunController
Real TimeEquipmentController
ProcessModel
EquipmentState
MetrologyProcessState
WaferState
AutomaticFault
Detection
Post-Process Measurements
In-Situ Sensors
(tonexttool)
UpdatedRecipe
ModifiedRecipe Unit Operation
SummarizedIn-Situ Measurements
Courtesy: Tom Sonderman / Advanced Micro Devices
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Supervisory Control Dynamic SchedulingPredictive Yield Modeling
Factory Wide ControlDemand Profile Manufacturing Constraints
Transistor/Isolation Island ofControl
CD
Masking
RtR
FDC
Etch
RtR
FDC CD
Deposition
RtR
FDC Thk
Polish
RtR
FDC Thk
E-Test
CD Target CD TargetThickness
TargetThickness
Target
Transistor Island of Control
Supervisory Control Dynamic Scheduling
BEOL Island of Control
Factory Wide Control (FWC)w/Real-Time Optimization (RTO)
Courtesy: Tom Sonderman / Advanced Micro Devices
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SORT RO
MP
STA
TIC
0
1
2
3
4
5
6
7
8
9
10
950975
10001025
10501075
11001125
11501175
12001225
12501275
13001325
13501375
14001425
14501475
15001525
15501575
16001625
16501675
17001725
1750
100MHz
3rd consecutive year > 30% sort RO improvement on bulk
Industry average: 21%
Q1-02 Q2-02 Q3-02 Q4-02
Rapid Technology Change34% Sort RO improvement in 2002
Courtesy: Tom Sonderman / Advanced Micro Devices
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Transistor ControlEffect on Microprocessor Speed Distributions
Initial Impact of APC on MPU Performance – 1998 – Narrower transistor drive current distribution allows AMD to push our process to the edge of the spec without fallout for high power or slow parts
AMD-K6® drive current distribution before Id,sat control
AMD-K6® drive current distribution after Id,sat control
Initial side-by-side comparison: Leff APC used ever since in F25 and from start-up in F30
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Lithography Overlay ControlPerformance improvement over 6+ years
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Why Did AMD Implement APC?
We saw the need to achieve precision processing beyond the tool capability so as to achieve competitive product performance at the leading edge.
We saw the potential to extend tool life.
We believed that it would become a major enabler in AMD’s dynamic manufacturing environment.
We believed that it was a natural extension of our SPC capability.
We saw it as a mechanism to reduce product costs while increasing the revenue potential from each wafer
We believed that the 130 nm and beyond technology nodes would demand automated control to achieve competitive yields!
We understood the competitive leverage of Precision Manufacturing!
We were right!
Courtesy: Tom Sonderman / Advanced Micro Devices
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Intel R+D and Transfer Strategy
• R+D for manufacturing
• Deliver PCS systems and infrastructure with the technology to manufacturing
• Copy Exactly! transfer to manufacturing
• Ramp and continuous improvement
Courtesy: Kumud Srinivasan / Intel
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Major Functional Groups
• R2R (Run to Run) • FD (Fault Detection)• FC (Fault Classification)• FP (Fault Prediction) • SPC (Statistical Process Control)
= EP= EP
= APC= APC
Courtesy: Kumud Srinivasan / Intel
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Intel PCS Strategy
• PCS capability and applications have been growing with technology advancements
• Capable of: – Rapid detection, classification &
prediction of problems to control process & equipment and keep variability at minimum
• PCS dev. strategy: – Internal Development of the PCS FW– External Engagement to develop the
PCS Interfaces
Courtesy: Kumud Srinivasan / Intel
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APC Applications
Completed APC Apps by Area (18)Implant
1Polish 2
Etch 2
TF/Diff4
Litho9
Completed APC Apps by Technology (Cum)
05
101520
P854 P856 P858 PX60 P1262
A B
APC Proposed Apps (28)
Litho10
TF/Diff7
Polish6
C43
Etch2
Control Points
02468
101214
CD Reg ThicknessControl Points
# of
App
s
Completed Proposed
D C
Courtesy: Kumud Srinivasan / Intel
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Emerging Factory Control Structure
Workstream Recipe Management
Alarm Handler
Planningand SchedulingCentral SPC
Maintenance
"CIM-Bus"
SECSIIServer
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Automated Precision ManufacturingControl Evolution
Phase 3: Predictability
Tool-Level Control
Supervisory Control
IMWET/SORT
MetProcessMet
Dynamic Adaptive Sampling
Electrical Parameter ControlDynamic Targeting
IntegratedFDC/RtRPC
Courtesy: Tom Sonderman / Advanced Micro Devices
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Fab-Wide Control TechnologyFactors Influencing Sampling
Incorporation of control related inputs allows for “smarter”sampling based on factory performance.
Sampling Decisions
Control Requirements
Control Uncertainty
Process ToolEvents
ProcessMonitoring
MetrologyResults
ControlPerformance
ProductionPriority
MetrologyCapacity
Courtesy: Tom Sonderman / Advanced Micro Devices
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Fab-Wide Control TechnologyDynamic Production Control
Determine speed distribution for each lot based on in-line measurements and transistor model
Change priority of lot based on estimate of # of parts in speed bins and production outs requirements (based on user input of outs@speed / week)
Change equipment set for each lot based on equipment performance (defectivity, speed impact) and requirements for outs
Change starts based on current estimations of in-line yield@speed and requirements for outs
E-Test
FEOL MOL BEOL
Factory Control Model
Lot Priorities
Lot Priorities
Inline Measurements
Inline Measurements
Inline Measurements
Model Updates
Courtesy: Tom Sonderman / Advanced Micro Devices
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The SPC Server
Accesses data base and draws simple X-R charts.Disables machine upon alarmBenefits from automated data collectionPerforms arbitrary correlations across the processCan to build causal models across the processMonitors process capabilities of essential stepsMaintains 2000+ charts across a typical fabKeeps track of alarm explanations given by operators and engineers.
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Training for SPCOperators: understand and "own" basic charts.
Process Engineers: be able to decide what to monitor and what chart to use (grouping, etc.)
Equipment Engineers: be able to collect tool data. Understand how to control real-time tool data.
Manufacturing Manager: understand process capabilities. Monitor several charts collectively.
Fab Statistician: understand the technology and its limitations. Appreciate cost of measurements.
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Summary of SPC topicsRandom variables and distributions.Sampling and hypothesis testing.The assignable cause.Control chart and operating characteristic.p, c and u charts.XR, XS charts and pattern analysis.Process capability.Acceptance charts.Maximum likelihood estimation, CUSUM.Multivariate control.Evolutionary operation.Regression chart.Time series modeling.
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The 2002 Roadmap
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The 2004 Roadmap Update
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199719992001 2003 2006 2009 2012
Function/milicent0
5
10
15
20
Function/milice
Overall ProductionEfficiency up by ~20X (!) from 1997 to 2012.
The main metric is “Efficiency”
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Where will the Extra Productivity Come from?
(Jim Owens, Sematech)Time
Other Productivity - Equipment, etc.
Yield Improvement
Wafer Size
3%4%9%
12% Feature Size
12-14%
4%
7-10%2%
12-14%
<2%
<1%
9-15%Ln$
/ Fun
ctio
n
25% - 30% / Yr.Improvement
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The OpportunitiesYear 1997 1999 2001 2003 2006Feature nm 250 180 150 100 70Yield % 85 ~90 ~92 ~93 ?Equipment utilization % 35 ~50 ~60 ~70Test wafers % 5-15 5-15 5-15 5-15
OEE30%
Down Un15%Down Pl
3%No Prod7%
No Oper10%
Quality2%
Test Wafers8%
Setup10%
Speed15%
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Basic CD Economics
(D. Gerold et al, Sematech AEC/APC, Sept 97, Lake Tahoe, NV)
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Application of Run-to-Run Control at Motorola
Dosen = Dosen-1 - β (CDn-1 - CDtarget)(D. Gerold et al, Sematech AEC/APC, Sept 97, Lake Tahoe, NV)
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CD Improvement at Motorola
σLeff reduced by 60%(D. Gerold et al, Sematech AEC/APC, Sept 97, Lake Tahoe, NV)
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Why was this Improvement Important?
600k APC investment, recovered in two days...
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Less Tangible Opportunities
Reduce cost of second sourcing (facilitate technology transfer)
Dramatically increase flexibility (beat competition with more customized options)
Extend life span of older technologies
Cut time to market (by linking manufacturing to design)
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What is the current extend of “control” in our industry?
Widespread inspection and SPC
Systematic setup and calibration (DOE)
Widespread use of RSM / Taguchi techniques
Factory statistics is an established discipline
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Advances for the Semiconductor Industry
An old idea - ensure equipment integrity - automatically
A new idea - perform feedback control on the workpiece
Isolate performance from technologyIsolate technology from equipmentCreate a process with truly interchangeable parts
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Changing the “do not touch my process” attitudeA stable process is one that is locally characterized and locked. SPC is used to make sure it stays there.
An “agile” process is one that is characterized over a region of operation. Process data and control algorithms are used to obtain goals.
Can we reach the 2010 process goals with a “stable”process?
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In ConclusionSPC provides “open loop” control.
In-situ data can be used for tighter run-to-run and supervisory control.
Several technical (and some cultural) problems must be addressed before that happens:
• Need sensors that are simple, non-intrusive and robust.
• User interfaces suitable for the production floor.
Next step in the evolution of manufacturing: From hand crafted products, to hand crafted lines to lines with interchangeable parts.