Lecture 16: From SPC to APC
Spanos
EE290H F05
1
Statistical Process Control
and
Computer Integrated Manufacturing
Run to Run Control
Real-Time SPC
Computer Integrated Manufacturing
Lecture 16: From SPC to APC
Spanos
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Auto-correlated Data
One important and widespread assumption in SPC is that the
samples take random values that are independently and
identically 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.
y
t
= µ + e
t
t = 1,2,... e
t
~ N (0,
σ
2
)
ρ
k
=
Σ (y
i
- y)(y
i+k
- y)
Σ(y
i
- y)
2
= 0 k = 1,2,...
Lecture 16: From SPC to APC
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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.
y
t
= µ +
ϕ y
t-1
+ e
t
e
t
~ N (0,
σ
2
)
µ' = µ / (1-
ϕ), σ' = σ / 1-ϕ
2
e
t
= y
t
- y
t
Lecture 16: From SPC to APC
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Example: LPCVD Temperature Readings
Temps are not IIND since future readings can be predicted!
100
80
60
40
20
0
601
602
603
604
605
606
607
608
Temp Readings from LPCVD Tube
Time
607
605
603
601
601
602
603
604
605
606
607
608
LPCVD Temp Autocorrelation
Temp (t)
Temp(t+1) = 758 - 0.253 Temp(t)
Lecture 16: From SPC to APC
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The Residuals of the Prediction can be Used for SPC..
100
80
60
40
20
0
601
602
603
604
605
606
607
608
Temp. Readings from LPCVD Tube
Time
100
80
60
40
20
0
-3
-2
-1
0
1
2
3
Temperature Residuals
Time
Lecture 16: From SPC to APC
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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 process
Explain - relate observations to rules of behavior
Forecast - see into the future
Control - alter parameters of the model
Lecture 16: From SPC to APC
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Two Basic Flavors of Time Series Models
Stationary data (i.e. time independent mean, variance and
autocorrelation structure) can be modelled as:
Autoregressive
Moving Average
Mixture (i.e. Autoregressive + Moving Average) models.
z
t
=
ϕ
1
z
t-1
+ e
t
e
t
~ N (0,
σ
2
)
z
t
= y
t
-
μ
z
t
=
θ
1
e
t-1
+ e
t
e
t
~ N (0,
σ
2
)
z
t
= y
t
-
μ
y
t
=
μ + ϕ
1
z
t-1
+
ϕ
2
z
t-2
+...+
ϕ
p
z
t-p
+ e
t
-
θ
1
e
t-1
-
θ
2
e
t-2
-...-
θ
q
e
t-q
ϕ: autoregressive
θ: moving average
Lecture 16: From SPC to APC
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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.
z
t
=
ϕ
1
z
t-1
+ e
t
e
t
~ N (0,
σ
2
)
z
t
= y
t
-
μ
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
35
40
45
50
55
60
sample
AR
Forecast
Error
Lecture 16: From SPC to APC
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Higher order AR(p) and ACF, PACF representation
Higher order autoregressive models are common in
engineering. Their structure can be inferred from acf and
pacf plots.
Autocorrelation Function (acf):
ρ
k
=
Σ (y
i
- y)(y
i+k
- y)
Σ(y
i
- y)
2
= 0 k = 1,2,...
Partial Autocorrelation Function (pacf):
z
t
=
ϕ
1
z
t-1
z
t
=
ϕ
1
z
t-1
+
ϕ
2
z
t-2
z
t
=
ϕ
1
z
t-1
+
ϕ
2
z
t-2
+
ϕ
3
z
t-3
z
t
=
ϕ
1
z
t-1
+
ϕ
2
z
t-2
+
ϕ
3
z
t-3
+
ϕ
4
x
t-4
...
k
k
Lecture 16: From SPC to APC
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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.
z
t
=
θ
1
e
t-1
+ e
t
e
t
~ N (0,
σ
2
)
z
t
= y
t
-
μ
time
z
t
0
Lecture 16: From SPC to APC
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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.
y
t
=
μ + ϕ
1
z
t-1
+
ϕ
2
z
t-2
+...+
ϕ
p
z
t-p
+ e
t
-
θ
1
e
t-1
-
θ
2
e
t-2
-...-
θ
q
e
t-q
ϕ: autoregressive
θ: moving average
Lecture 16: From SPC to APC
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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.
Lecture 16: From SPC to APC
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Shewhart and CUSUM time series residuals
Lecture 16: From SPC to APC
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Fitted ARIMA(0,1,1) Example
Data
ARIMA(0,1,1)
Residuals
Lecture 16: From SPC to APC
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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 T
2
statistics.
This analysis is done simultaneously in several levels:
• Real-Time Signals
• Wafer Averages
• Lot Averages
The multivariate T
2
chart provides a robust real-time
summary of machine “goodness”.
Lecture 16: From SPC to APC
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The Equipment Controller
Today, the operation of individual pieces of equipment can
be streamlined with the help of external software
applications. SPC is just one of them.
Fault
Diagnosis
CIM
database
Local
Database(s)
Equipment
Supervisor
Maintenance
Monitoring
Statistical
Process
Control
Modeling
and
Simulation
Recipe
Generation
Workcell
Coordinator
Lecture 16: From SPC to APC
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The Workcell Controller
Most 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.
Equipment
Model
Step
Equipment
Model
Step
Adaptive
Control
Adaptive
Control
test
test
test
Lecture 16: From SPC to APC
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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 CUSUM
chart of the prediction residuals.
Control limits are based on experimental errors as well as
on the model prediction errors due to regression. Hard limits
on equipment inputs are also used.
Lecture 16: From SPC to APC
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The Idea of Statistically Based Feedback Control
x
o
y
o
original RSM
new RSM
y
n
x
f
y
f
1
2
4
5
3
1. Original Operation
2. Process Shifts
3. Shift is detected by MBSPC
4. RSM is adapted
5. New Recipe is generated
controlling input
response
Lecture 16: From SPC to APC
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Feedback Control
Model-based, adaptive Feedback Control has been
employed on several processes.
Initial
Settings
Recipe
Update
Spin Coat
& Bake
Parameter
Estimator
Equipment
Model
Controller
t Test
M
Model
Update?
yes
No
Lecture 16: From SPC to APC
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Feedback Control in Resist Application
Adaptive
Controller
Spin-Coat
& Bake
Thickness
Incoming
Outgoing
Equipment
& Refl ectance
Measurement
Wafer
Wafer
Input
Settings
Lecture 16: From SPC to APC
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Feedback Control in Resist Application (cont.)
11800
12000
12200
12400
12600
0
10
20
30
40
50
60
Target
Model Prediction
Experimental Data
20
24
28
32
36
40
44
0
10
20
30
40
50
Wafer Number
60
Target
Model Prediction
Experimental Data
Lecture 16: From SPC to APC
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Alarms in Resist Application Control
0
4
8
12
16
20
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
Alarm
Alarm
Wafer Number
Alarm
UCL
Lecture 16: From SPC to APC
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Adapting the Regression Model
The regression model has many coefficients that may
need adaptation.
What can be adapted depends on what measurements
are available.
y
n
= a
o
x
2
+b’
n
x +c’’
n
y
o
= a
o
x
2
+b
o
x +c
o
y
n
= a
o
x
2
+b
o
x +c’
n
y
n
= a
n
x
2
+b
n
x +c’
n
1
2
3
4
Lecture 16: From SPC to APC
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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.
x
1
x
2
y
A
B
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.
Lecture 16: From SPC to APC
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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
Lecture 16: From SPC to APC
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Feed-Forward Control
Models can also be used to predict the outcome and
correct ahead of time if necessary.
Exposure
M
Develop
Recipe
Update
Model
Projected
CD Spread
In
Spec?
Standard
Setting
Yes
No
Lecture 16: From SPC to APC
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Modified Charts for Feed Forward Control
LSL
UCL
LCL
Yield Loss
USL
σ (equipment spread)
σ/
√
n (prediction spread)
No FF
False Alarm Probability
Lecture 16: From SPC to APC
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Feed Forward/Feedback Control Results (cont)
78
80
82
84
86
88
90
92
0
5
10
15
20
25
30
Wafer No
Open Loop
FeedForward
Target = 88.75%
Lecture 16: From SPC to APC
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Concurrent Control of Multiple Steps
Spin
Coat
&
Bake
Expose
Develop
T&R
Meas
R
Meas
CD
Meas
Adaptive
Control
Adaptive
Control
Adaptive
Control
Equip
.
Model
Equip.
Model
Equip.
Model
Inputs
Original
Lecture 16: From SPC to APC
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Resist Thickness Example
0
5
10
15
20
11000
11500
12000
Target = 11906Å
Wafer Number
Closed Loop
Open Loop
Lecture 16: From SPC to APC
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Resist Thickness Input
0
5
10
15
20
40
60
80
100
Wafer Number
Closed Loop
Open Loop
Lecture 16: From SPC to APC
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Latent Image output
0
5
10
15
20
70
80
90
100
Wafer Number
Target = 87.75%
Malfunction Alarm
Control Alarm
Closed Loop
Open Loop
Lecture 16: From SPC to APC
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Latent Image Input
0
5
10
15
20
0.60
0.70
0.80
0.90
1.00
Wafer Number
Closed Loop
Open Loop
Lecture 16: From SPC to APC
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Developer Output
0
5
10
15
20
2.50
2.60
2.70
2.80
Wafer Number
Target = 2.66 um
Malfunction Alarm
Control Alarm
Closed Loop
Open Loop
Lecture 16: From SPC to APC
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Developer Input
Wafer Number
0
5
10
15
20
Closed Loop
Open Loop
50
55
60
65
Lecture 16: From SPC to APC
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Process Improvement Due to Run to Run Control
CD in
μm
CD in
μm
2.55
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
.
Lecture 16: From SPC to APC
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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
Lecture 16: From SPC to APC
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The Concept of Dynamic Specifications
Constraint
Mapping
Step
Control
Model
Step
Control
Model
Constraint
Mapping
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.
Lecture 16: From SPC to APC
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Specs to step A must change if step B "ages"
Step
Step
Step
A
B
C
Fixed spec
limits for B
New spec
limits for A
Out1
A
Out2
A
Out1
B
Out2
B
Mapping
Model
of B
PC
2
PC
1
Lecture 16: From SPC to APC
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Specs are outlined automatically - Stepper example
(Develop time varies between 55 and 65 seconds)
Thickness
Lecture 16: From SPC to APC
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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
Target
Baseline
FF/FB only
Supervisory
Lecture 16: From SPC to APC
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Results from Supervisory Control Application
Baseline
FF/FB Only
Supervisory
Th
PAC
LI
Lecture 16: From SPC to APC
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Control Results - CD
Baseline
FF/FB Only
Supervisory
Lecture 16: From SPC to APC
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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
Lecture 16: From SPC to APC
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Manufacturing Evolution…
Process/Equipment
Model and Controller
Real-time equipment model
Diagnostic Engine
Lecture 16: From SPC to APC
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Drift
Noise
Equipment
Model
Advanced Process Control
APC Requires Integrated RtRPC and FDC
(from
Previous
tool)
Post-Process
Measurements
For Feed-
forward
Control
Run to Run
Controller
Real Time
Equipment
Controller
Process
Model
Equipment
State
Metrology
Process
State
Wafer
State
Automatic
Fault
Detection
Post-Process Measurements
In-Situ Sensors
(to
next
tool)
Updated
Recipe
Modified
Recipe
Unit Operation
Summarized
In-Situ
Measurements
Courtesy: Tom Sonderman / Advanced Micro Devices
Lecture 16: From SPC to APC
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Supervisory Control
Dynamic Scheduling
Predictive Yield Modeling
Factory Wide Control
Demand Profile
Manufacturing Constraints
Transistor/Isolation Island of
Control
CD
M asking
RtR
FDC
Etch
RtR
FDC
CD
Deposition
RtR
FDC
Thk
Polish
RtR
FDC
Thk
E-Test
CD Target
CD Target
Thickness
Target
Thickness
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
Lecture 16: From SPC to APC
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SORT RO
MPSTATIC
0
1
2
3
4
5
6
7
8
9
10
950
975
1000
1025
1050
1075
1100
1125
1150
1175
1200
1225
1250
1275
1300
1325
1350
1375
1400
1425
1450
1475
1500
1525
1550
1575
1600
1625
1650
1675
1700
1725
1750
100MHz
3
rd
consecutive
year > 30% sort
RO improvement
on bulk
Industry average:
21%
Q1-02
Q2-02
Q3-02
Q4-02
Rapid Technology Change
34% Sort RO improvement in 2002
Courtesy: Tom Sonderman / Advanced Micro Devices
Lecture 16: From SPC to APC
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Transistor Control
Effect 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
Lecture 16: From SPC to APC
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Lithography Overlay Control
Performance improvement over 6+ years
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Why Did AMD Implement APC?
¾ We saw the need to achieve precision processing beyond the tool capability
so as to achieve competitive product performance at the leading edge.
¾ We saw the potential to extend tool life.
¾ We believed that it would become a major enabler in AMD’s dynamic
manufacturing environment.
¾ We believed that it was a natural extension of our SPC capability.
¾ We saw it as a mechanism to reduce product costs while increasing the
revenue potential from each wafer
¾ We believed that the 130 nm and beyond technology nodes would demand
automated
control to achieve competitive yields!
¾ We understood the competitive leverage of
Precision Manufacturing
!
We were right!
Courtesy: Tom Sonderman / Advanced Micro Devices
Lecture 16: From SPC to APC
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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
Lecture 16: From SPC to APC
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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
Lecture 16: From SPC to APC
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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
Lecture 16: From SPC to APC
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APC Applications
Completed APC Apps by Area (18)
Implant
1
Polish
2
Etch
2
TF/Diff
4
Litho
9
Completed APC Apps by
Technology (Cum)
0
5
10
15
20
P854
P856
P858
PX60
P1262
A
B
APC Proposed Apps (28)
Litho
10
TF/Diff
7
Polish
6
C4
3
Etch
2
Control Points
0
2
4
6
8
10
12
14
CD
Reg
Thickness
Control Points
# of
A
pps
Completed
Proposed
D
C
Courtesy: Kumud Srinivasan / Intel
Lecture 16: From SPC to APC
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Emerging Factory Control Structure
Workstream
Recipe
Management
Alarm
Handler
Planning
and
Scheduling
Central SPC
Maintenance
"CIM-Bus"
SECSII
Server
Lecture 16: From SPC to APC
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Automated Precision Manufacturing
Control Evolution
Phase 3: Predictability
Tool-Level Control
Supervisory Control
IM
WET/SORT
Met
Process
Met
Dynamic Adaptive Sampling
Electrical Parameter Control
Dynamic Targeting
Integrated
FDC/RtRPC
Courtesy: Tom Sonderman / Advanced Micro Devices
Lecture 16: From SPC to APC
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Fab-Wide Control Technology
Factors Influencing Sampling
Incorporation of control related inputs allows for “smarter”
sampling based on factory performance.
Sampling Decisions
Control
Requirements
Control
Uncertainty
Process Tool
Events
Process
Monitoring
Metrology
Results
Control
Performance
Production
Priority
Metrology
Capacity
Courtesy: Tom Sonderman / Advanced Micro Devices
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Fab-Wide Control Technology
Dynamic 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 alarm
Benefits from automated data collection
Performs arbitrary correlations across the process
Can to build causal models across the process
Monitors process capabilities of essential steps
Maintains 2000+ charts across a typical fab
Keeps track of alarm explanations given by operators
and engineers.
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Training for SPC
Operators: 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 topics
Random 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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19971999
2001 2003
2006
2009 2012
Function/milicent
0
5
10
15
20
Function/milice
Overall Production
Efficiency 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%
L
n
$
/
F
u
n
c
ti
o
n
25% - 30% / Yr.
Improvement
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The Opportunities
Year
1997 1999 2001 2003 2006
Feature nm
250
180
150
100
70
Yield %
85
~90
~92
~93
?
Equipment utilization %
35
~50
~60
~70
Test wafers %
5-15
5-15
5-15
5-15
OEE
30%
Down Un
15%
Down Pl
3%
No Prod
7%
No Oper
10%
Quality
2%
Test Wafers
8%
Setup
10%
Speed
15%
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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
Dose
n
= Dose
n-1
-
β (CD
n-1
- CD
target
)
(D. Gerold et al, Sematech AEC/APC, Sept 97, Lake Tahoe, NV)
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CD Improvement at Motorola
σ
Leff
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 technology
Isolate technology from equipment
Create a process with truly interchangeable parts
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Changing the “do not touch my process” attitude
A 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 Conclusion
SPC 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.
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