Inventory Management in Semiconductor
Manufacturing Supply Chains (and Beyond):
Insights Gained from a Process Control
Perspective
Daniel E. Rivera
Control Systems Engineering Laboratory
Department of Chemical and Materials Engineering
Ira A. Fulton School of Engineering
Arizona State University
daniel.rivera@asu.edu
About the Presenter
•
Born and raised in San Juan, Puerto Rico
•
Education
– B.S. ChE degree from the University
of Rochester (1982)
– M.S. ChE degree from the University
of Wisconsin (1984)
– Ph.D. from Caltech (1987)
•
Positions:
– Associate Research Engineer, Shell
Development Company, Houston, TX
(1987-1990)
– Associate Professor, Arizona State
University, (1990 - present)
Control Systems Engineering Laboratory Projects
•
Chemical Process Control.
• American Chemical Society-Petroleum Research Fund: “Constrained
Multisine Inputs for Plant-Friendly Identification of Chemical Processes”
• Honeywell Intl. Foundation: “Control Systems Engineering Laboratory”
•
Supply Chain Management.
• National Science Foundation: “GOALI: Process Control Approaches to
Supply Chain Management in Semiconductor Manufacturing”
• Intel Research Council:
“Supply Chain Management Research Using Process Control
Approaches”
“Improving Short-term Demand Forecasting in Supply Chain
Management”
•
Behavioral Health.
• NIH-NIDA (subcon via Penn State): “Control Engineering Approaches to
Adaptive, Time-Varying Interventions in Drug Abuse Prevention”
http://www.fulton.asu.edu/~csel
Presentation Outline
• Control engineering basics review
• Supply Chain Management (SCM) as a process control
problem
• Application to SCM in semiconductor manufacturing
• Adaptive interventions in drug abuse prevention
• Summary and conclusions
What to take with you from this talk
• The transfer of variance from a valuable system resource to a less
expensive one is an important outcome of well-designed control
systems, in any application setting.
• Both feedback and feedforward strategies are needed in the design
of effective control systems for delayed, nonlinear, stochastic plants.
• Process control ideas have significant application in diverse problem
settings, for example:
– supply chain management for semiconductor manufacturing, and
– adaptive interventions in behavioral health
• Prepare yourself for life-long learning, since you may very well work
on problems you have never imagined (in a not-too-distant future).
Control Engineering
• Control engineering is a broadly-applicable field that spans all
areas of engineering:
–
–
–
–
–
–
–
Chemical
Electrical
Mechanical and Aerospace
Civil / Construction
Industrial
Biomedical
Computer Science and Engineering
• Control engineering principles play a role in everyday life
activities.
Control Engineering (Continued)
Considers how to manipulate or adjust system variables
so that its behavior over time is transformed from
undesirable to desirable,
• Open-loop: refers to system behavior without a
controller or decision rules (i.e., MANUAL operation).
• Closed-loop: refers to system behavior once a
controller or decision rule is implemented (i.e.,
AUTOmatic operation).
Open-Loop (Manual) vs. Closed-Loop
(Automatic) Control
Open-Loop “Manual”
Closed-Loop “Automatic”
An Improved Closed-Loop System
(Dual Climate Control)
An Industrial Process Control Problem
are needed to see this picture.
QuickTime™ and a
BMP decompressor
Objective: Use fuel gas flow to keep outlet temperature under control, in spite of
occasional yet significant changes in the feed flowrate.
The “Shower” Control Problem
Disturbances:
Inlet Water Flows,
Temperatures
Controlled:
Temperature,
Total Water Flow
The presence of delay or
“transportation lag”
makes this a difficult control
problem
Hot
Cold
Manipulated: Hot and Cold
Water Valve Positions
Feedback and Feedforward
Control Strategies
• In feedback control strategies, a controlled variable
(y) is examined and compared to a reference value or
setpoint (r). The controller issues actions (decisions
on the values of a manipulated variable (u)) on the
basis of the discrepancy between y and r (e = r - y,
the control error).
• In feedforward control, changes in a disturbance
variable (d) are monitored and the manipulated
variable (u) is chosen to counteract anticipated
changes in y as a result of d.
Shower Problem: Automatic Feedback Control
Flow setpoint
Controlled:
Temperature,
Total Water Flow
Controller
Temp. setpoint
Sensors
F T
Disturbances:
Inlet Water Flows,
Temperatures
Manipulated:
Hot and Cold
Water Valve
Positions
Ho
t
Cold
Actuators
Closed-Loop Feedback Control
“Block Diagram” d Disturbances:
Manipulated:
Hot and Cold
Water Valve
Positions
Reference:
Desired Temperature,
Total Water Flow
r
ec = r - ym
-
C
Pd
u
+
P
+
Inlet Water Flows,
Temperatures
Controlled:
y Temperature,
Total Water Flow
+
ym
C = Controller
P = Plant Model/“Transfer Function”
Pd = Disturbance Model/“Transfer Function”
n
sensor
noise
From Open-Loop Operation to
Closed-Loop Control
Measured Output
20
Temperature
Deviation
(Measured
Controlled
Variable)
10
0
Open-Loop
(Before Control)
-10
-20
0
500
1000
1500
2000
2500
3000
3500
4000
Time[Min]
Input
Hot Water
Valve
Adjustment
10
(Manipulated
Variable)
-10
Closed-Loop
Control
0
0
500
1000
1500
2000
2500
3000
3500
4000
Time[Min]
The transfer of variance from an expensive resource to a cheaper one is
one of the major benefits of engineering process control
Supply Chain Management
F
Factory
Retailer
W
Warehouse
• A supply chains consist of interconnected entities
(e.g., factories, warehouses, and retailers) which
transform ideas and raw materials into delivered
products and services
Motivation
• In the modern economy, products do not simply compete against other
products; supply chains compete against other supply chains.
• Billions of dollars in potential savings by eliminating supply chain
inefficiencies (PriceWaterHouseCoopers, 2000; Kempf, 2004)
• An effective SCM system can
– Improve an enterprise’s agility to respond to market upturns
(and downturns)
– Increase revenue while reducing manufacturing and transportation costs.
– Eliminate excess inventories and reduce safety stocks
– Lower lead times and improve customer satisfaction
The Business Literature Can Inspire a
Control Engineering Approach
• The “bullwhip” effect (Lee et al., "Information Distortion in a
Supply Chain: The Bullwhip Effect", Management Science 43(4)
546, 1997); demand distortion caused by variance amplification
of orders upstream in the supply chain
• This and similar terminology highlight issues relating to stability
and performance of a dynamical system, which merit a controloriented approach.
• Not strictly an engineering/scientific problem: financial,
organizational, and social issues come into play in this problem.
“Bullwhip” Effect
Supply Chain Inventory Management as a
“Level” Control Problem
ORDER DECISIONS/STARTS
CTL
Starts (Manipulated)
production time; also
known as throughput time)
LT
Net Stock
(Controlled)
Demand
d
(Disturbance)
delivery time)
Meet demand (with forecast possibly given f days beforehand) for a node
with day production (or order fulfillment) time and d delivery time.
Feedback-Only Inventory Control Problem
Starts (Manipulated)
production time)
CTL
LT
Net Stock
(Controlled)
Demand
(Disturbance)
d
delivery time)
In the feedback-only control problem, ordering decisions are
calculated based only on perceived changes to “level”
(e.g., net stock or equivalent variable).
Single Node Inventory Problem
Combined Feedback/Feedforward Control
Starts (Manipulated)
production time)
LIC
Demand Forecast
(known f days
beforehand)
LT
Net Stock
(Controlled)
Demand
d
(Disturbance)
delivery time)
In the combined feedback/feedforward problem, a demand forecast is
used for feedforward compensation.
3DoF Internal Model Control Results
(random unforecasted demand at t = 90)
Feedback-only
f = 20, 
Combined FB/FF
d = 2, f = 1, r = 1, d = 1, nr=1, nd=3, nff=2
The ASU-Intel SCM Project Team
Involves multiple faculty and graduate students from various departments
in Engineering and Mathematics
•
Dept. of Mathematics, CLAS:
– Professors Dieter Armbruster, Matthias Kawski, Christian Ringhofer and
Hans Mittelmann; Eric Gehrig (Ph.D. student), Dominique Perdaen, Ton
Geubbels (Visiting Researchers from TU-Eindhoven, The Netherlands).
•
Chemical Engineering, Fulton School:
– Prof. Daniel E. Rivera; Wenlin Wang and Jay D. Schwartz (Ph.D. students),
Michael D. Pew (UG student), and Asun Zafra Cabeza (Visiting Researcher
from the University of Seville, Spain)
•
Computer Science and Engineering, Fulton School
– Prof. Hessam Sarjoughian; Donna Huang and Weilong.Hu (Ph.D. students)
•
Intel collaborators:
– Karl G. Kempf, Kirk D. Smith, Gary Godding, John Bean, Mike O’Brien
Proposed Architecture
The Outer Loop
Problem
strategic
planning
Validation
goals
inventory
planning
goals
simulation
limits
tactical
execution
Prediction
The Inner
Loop Problem
Semiconductor Manufacturing Process
Fluid Analogy for Single Fab/Test1,
Assembly/Test2 and Finish Nodes
Modeling Issues and Challenges
•
The manufacturing process displays long throughput times (TPT) which
are stochastic and nonlinearly dependent on load
•
Yields are also stochastic
•
There is an error between the forecasted and actual demand, which is
also stochastic
•
Additional problem features include package dynamics, stochastic splits
in die properties, and multi-factory issues involving cross-shipments,
shared capacity, and correlated demands.
Fab/Test Manufacturing Node Dynamics
Load
Time
Model Predictive Control
(Inventory Levels,
WIP)
(Actual Demand)
(Forecasted Demand)
(Previous Starts)
(Future Starts)
Model Predictive Control Advantages
• Ability to handle large multivariable systems
• Ability to enforce constraints on manipulated and
controlled variables
• Effective integration of feedback, feedforward
controller modes; ability to incorporate anticipation
• Novel formulations (such as hybrid MPC) enable the
application to systems involving both discrete-event
and continuous variables.
Case Study: Assembly/Test2 Stochastic
Split Problem
Fab/Test1
M10
I10
Number of Die
C35
Assmbly/Test2
C36
X
Slow
Fast
devices devices
Speed
M20
I20
I21
C90
C38 C39
C37
M30 M30 Fin/PackM30
I30
C40
I31
C41
M40
M40
D1
D3 D2
E1
E2
E3
The outcome of the
Assembly/Test2
process is stochastic in
terms of the number of
fast and slow devices
that result.
Fast devices can be
used to make high
speed products (C37).
Slow devices can be
used to make low
speed products (C39).
Case 2: No Move Suppression
A/T2 Load
F/T1 Starts
CW (Fast)
Finishing Load
Reconfiguration Starts
CW (Slow)
Case 2: With Move Suppression
[10 10 10 0 10]
A/T2 Load
F/T1 Starts
98.9% variance
reduction
CW (Fast)
43.2% variance
reduction
69.3% variance
reduction
Finishing Load
Reconfiguration Starts
CW (Slow)
4.7% variance
reduction
51.5% variance
reduction
Unfilled Orders:
4.62%
Unfilled Orders:
7.41%
With Move Suppression
Fast Device Backlog
No Move Suppression
Slow Device Backlog
Slow Device Backlog
Fast Device Backlog
Customer Service Comparison
Unfilled Orders:
0.34%
Unfilled Orders:
2.38%
“Combination” Problem
C35
M51
M10
C37
C36
M11
C38
M50
I50
I10
I11
C40
C39
M20
C90
I20
C45
M30
I30
C49
C41
I51
C42
M21
I23
I21
I22 C91
C46
C47 C48
M31 M31
M31
C43
C44
M30 M30
I31
I33
C52
C51
C50
I31
M40
M40
M41
M41
D1
D2
D3
E1
E2
E3
E1
E2
E3
D1
D2
D3
A “Small” Semiconductor Mfg Problem
Asm1
= Mats Mfg
= Inv Hold
= Prod Mfg
= Transport
5.1
Box1E
7.1
37
3.2
18
39
B
20
7
P1
T1-1
3.3 pp
40
3.4 pp
41
6.2
P1
vendor3
vendor1
6.1
24
C
vendor4
25
21
12
43
vend7
Fab1
Fin1
33
4.1
34
11
2.1
T2-1
28
3.1
7.4
pp
7.3 44
vend8
pp 46
45
42
7.2
7.5
1
1.1 si
2
A
3
Fab2
2.2
8
P1,P2
T1-2
7.6
Asm2
14
4.2
Box2
Fin2
5.2
36
F
4
30
15
5
T2-2
29
3.6
P1,P2
38
35
3.5
19
13
1.2 si
3.5
3.7
22
26
6
vendor2
16
Fab3
2.3
9
P2
T1-3
vendor5
P2
10
3.8 pp
vendor6
Blue = Intel
Red = Mat. Sub.
Green = Cap. Sub.
3.9 ram
17
23
27
3.10
3.11
3.12
Asm3
31
32
D
4.3
T2-3
6.3
Adaptive Interventions
• Adaptive interventions individualize therapy by the use of decision
rules for how the therapy level and type should vary according to
measures of adherence, treatment burden and response collected
during past treatment.
• Adaptive interventions represent an important emerging paradigm
for prevention and treatment of chronic, relapsing disorders, such
as drug and alcohol abuse, depression, hypertension, obesity, and
many other maladies.
• Also known as stepped care models, dynamic treatment regimes,
structured treatment interruptions, and treatment algorithms.
Home Counseling-Parental Function Intervention
• Based on the Fast Track Program (a multi-year intervention
designed to prevent conduct disorders in at-risk children).
• Parental function (the tailoring variable) is used to determine the
frequency of home visits (intervention dosage) according to the
following decision rules:
- If parental function is “low” the intervention dosage
should correspond to weekly home visits,
- If parental function is “average” then intervention dosage
should correspond to bi-weekly home visits,
- If parental function is “high” then intervention dosage should
correspond to monthly home visits.
Parental Function Feedback Loop Block Diagram*
(to decide on home visits for families with at risk children)
Clinical Judgment
Goal
+
Decision
Rules
+
Disturbances
Intervention
Outcomes
Process
I(t)
Review
Interval
If PF(t) is “Low” then Weekly Home Visits
If PF(t) is “Medium” then Bi-Weekly Visits
If PF(t) is “High” then Monthly Home Visits
If PF(t) is “Acceptable” then No Visits
Tailoring Variable
Estimation
+
+
Reliability/
Measurement
Error
Estimated Parental Function PF(t)
*Based on material from Collins, Murphy, and Bierman, “A Conceptual Framework
for Adaptive Preventive Interventions,” Prevention Science, 2004.
Parental Function Feedback-Only
Control Problem
PF Level
I(t) (Manipulated)
CTL
PF Factor
100
90
80
70
60
50
40
30
20
10
0
PF
Factor
Goal
1
11
21
Months 31
LT
Recommended Intervention Dosage
Depletion
D(t) (Disturbance)
Intervention Dosage
PF(t)
(Controlled)
3
2
1
0
1
11
21
Months
31
In the feedback-only control problem, intervention dosages are calculated
based only on perceived changes to “inventory” (parental function PF(t)).
Summary and Conclusions
•
The transfer of variance from a valuable system resource to a less
expensive one is an important outcome of a well-designed control system,
in any application setting.
•
Both feedback and feedforward strategies are needed in the design of
effective control systems for delayed, nonlinear, stochastic plants.
•
Process control ideas have significant application in diverse problem
settings, for example:
– supply chain management for semiconductor manufacturing, and
– adaptive interventions in behavioral health
•
Prepare yourself for life-long learning, since you may very well work on
problems you never imagined (in a not-too-distant future).