Modeling and optimizing flexible capacity allocation in semiconductor manufacturing

237 Pages·2016·2.53 MB·English

by  Carl Johnzén

Modeling and optimizing flexible capacity allocation in
semiconductor manufacturing
Carl Johnzén
To cite this version:
Carl Johnzén. Modeling and optimizing flexible capacity allocation in semiconductor manufacturing.
EngineeringSciences[physics]. EcoleNationaleSupérieuredesMinesdeSaint-Etienne, 2009. English.
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(cid:176)
N d’ordre : 521 GI
TH¨SE
prØsentØe par
Carl JohnzØn
Pour obtenir le grade de Docteur
de l’Ecole Nationale SupØrieure des Mines de Saint-Etienne
SpØcialitØ : GØnie industriel
Modeling and Optimizing Flexible Capacity Allocation in
Semiconductor Manufacturing
soutenue (cid:224) Gardanne le 6 avril 2009
Membres du jury
President Prof. L. M(cid:246)nch University of Hagen
Rapporteurs Prof. M. Jacomino Institut Polytechnique de Grenoble
Prof. J. Rooda Eindhoven University of Technology
Examinateurs Prof. M.-C. Portmann Ecole des Mines de Nancy (INPL)
Prof. M. Sevaux UniversitØ de Bretagne-Sud
Directeur de thŁse Prof. S. DauzŁre-PØrŁs Ecole des Mines de Saint-Etienne
Tuteur industriel Ir. P. Vialletelle STMicroelectronics
ii
Acknowledgments
The work presented in this PhD thesis has been realized within the framework
of Convention de Formation par la Recherche (CIFRE n 104) in accordance with
the Association Nationale de la Recherche Technique, which supports companies to
involve PhD students in their work. The thesis also has been written as a part of
the European Union project HYMNE.
The (cid:28)rst two years of the work on this thesis has been done within the framework
of Crolles2 Alliance, tripartite joint operation between Freescale Semiconductor,
NXP and STMicroelectronics (ST). Since the (cid:28)rst of January 2008 the work has
been carried out in cooperation with ST as the only partner.
During my thesis writing I was an employee at ST at their site in Crolles, France.
This has not only helped to (cid:28)nance my PhD studies but also given me practical
support. I have been able to use their resources in my work such as an o(cid:30)ce space,
computer and more important data from their production for the tests that I have
performed. The administrative aid from ST has made it possible for me to concen-
trate on my research. I am very grateful for the assistance of STMicroelectronics. I
especially want to express my gratitude toward Philippe Vialletelle my supervisor
from the Industrial Engineering group at STMicroelectonics in Crolles, who shared
his experiences and ideas. Although overloaded with work, his clear-sighted ideas
always helped me to move my research forward.
Without mentioning all my colleagues at ST, I would like to at least mention the
guidance and friendship that has been o(cid:27)ered to me by Fran(cid:231)ois Buttin, Mariangela
Cardille, Manuel Cali, Leon Vermariºn and my fellow PhD students Jean-Etienne
Kiba and Casper Veeger.
The scienti(cid:28)c part of the research has been done within the framework of Centre
MicroØlØctronique de Provence - Georges Charpak (CMP) at Ecole Nationale SupØ-
rieure des Mines de Saint-Etienne (EMSE) in Gardanne, France. When STMicroe-
lectronics got into a di(cid:30)cult (cid:28)nancial situation EMSE continued to (cid:28)nance my visits
at their research center and to conferences for which I am very grateful.
I’m very thankful to my scienti(cid:28)c supervisor, professor StØphane DauzŁre-PØrŁs.
iii
His guidance and expertise within the operational research area has helped me to
raise the quality of my research several levels. I truly appreciate his ability to scru-
tinize data. He could always identify something that did not work optimally, re-
commend which methods should be used for solving my models, (cid:28)nd bugs in my
computer programs, and suggest better ways to write articles or make presentations.
At the Sciences de la fabrication et logistique-department (SFL) at CMP I also
want to thank Claude Yugma with whom I have been able to discuss my ideas,
and Alexandre Derreumaux who has helped me a great deal with the programming
performed during my research. Without mentioning all the names, I want to express
my gratitude to the rest of the sta(cid:27) at SFL-department. I have always appreciated
the warm and welcoming atmosphere that I found there. Finally, I want to thank
my (cid:28)ancØe Veronika Gumpinger, who has been the greatest possible moral support.
iv
Table des matiŁres
Acknowledgments iii
RØsumØ Entendu Fran(cid:231)ais 1
0.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
0.2 Gestion de Quali(cid:28)cations . . . . . . . . . . . . . . . . . . . . . . . . . 7
0.3 ModØlisation de FlexibilitØ . . . . . . . . . . . . . . . . . . . . . . . . 8
0.3.1 Mesures de (cid:29)exibilitØ . . . . . . . . . . . . . . . . . . . . . . . 9
0.3.2 Optimisation de l’Øquilibrage du travail . . . . . . . . . . . . . 14
0.3.3 Tests NumØriques . . . . . . . . . . . . . . . . . . . . . . . . . 16
0.4 Optimisation des quali(cid:28)cations . . . . . . . . . . . . . . . . . . . . . . 17
0.5 Extensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
0.5.1 QuantitØs dynamiques de l’en-cours . . . . . . . . . . . . . . . 19
0.5.2 Extensions additionelles . . . . . . . . . . . . . . . . . . . . . 21
0.6 Impact des quali(cid:28)cations sur l’ordonnancement . . . . . . . . . . . . 22
0.6.1 Le simulateur d’ordonnancement pour l’atelier de photolitho-
graphie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
0.6.2 Le simulateur d’ordonnancement pour l’atelier de gravure sŁche 23
0.7 ImplØmentations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
0.7.1 Le logiciel pour la gestion des quali(cid:28)cations . . . . . . . . . . 25
0.7.2 Le simulateur d’ordonnancement pour l’atelier de gravure sŁche 27
0.8 Discussion et Perspectives . . . . . . . . . . . . . . . . . . . . . . . . 28
v
TABLE DES MATI¨RES
General Introduction 31
1 Industrial and Scienti(cid:28)c Context 33
1.1 A Brief Introduction to Industrial Engineering . . . . . . . . . . . . . 34
1.2 Semiconductor Manufacturing . . . . . . . . . . . . . . . . . . . . . . 36
1.2.1 Industrial engineering in wafer fabs . . . . . . . . . . . . . . . 36
1.2.2 Basic concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
1.2.3 Wafer and IC dimensions . . . . . . . . . . . . . . . . . . . . . 39
1.2.4 Fabrication . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
1.2.5 Wafer fab modeling . . . . . . . . . . . . . . . . . . . . . . . . 42
1.3 Quali(cid:28)cations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
1.3.1 Recipe quali(cid:28)cations in semiconductor manufacturing . . . . . 44
1.3.2 Literature on quali(cid:28)cation management . . . . . . . . . . . . . 46
1.3.3 Importance of quali(cid:28)cation management . . . . . . . . . . . . 48
1.3.4 Flexible quali(cid:28)cations . . . . . . . . . . . . . . . . . . . . . . . 51
1.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
2 Modeling Flexibility for Quali(cid:28)cation Management 57
2.1 Motivations for Modeling Flexibility . . . . . . . . . . . . . . . . . . . 58
2.2 A Literature Review on Flexibility . . . . . . . . . . . . . . . . . . . 60
2.3 Flexibility Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
2.3.1 Toolset (cid:29)exibility . . . . . . . . . . . . . . . . . . . . . . . . . 64
2.3.2 WIP (cid:29)exibility . . . . . . . . . . . . . . . . . . . . . . . . . . 66
2.3.3 Time (cid:29)exibility . . . . . . . . . . . . . . . . . . . . . . . . . . 66
2.3.4 System (cid:29)exibility . . . . . . . . . . . . . . . . . . . . . . . . . 69
2.3.5 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
2.4 Optimizing Workload Balancing . . . . . . . . . . . . . . . . . . . . . 72
2.4.1 Workload balancing for WIP (cid:29)exibility FWIP . . . . . . . . . 73
2.4.2 Workload balancing for time (cid:29)exibility Ftime . . . . . . . . . . 78
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TABLE DES MATI¨RES
2.4.3 Performance of the balancing algorithms . . . . . . . . . . . . 89
2.5 Numerical Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . 90
2.5.1 Impact of quali(cid:28)cations . . . . . . . . . . . . . . . . . . . . . . 90
2.5.2 Impact of the (cid:29)exibility balance exponent . . . . . . . . . . . 93
2.5.3 Time (cid:29)exibility versus WIP (cid:29)exibility . . . . . . . . . . . . . . 95
2.6 Limitations of the Flexibility Measures . . . . . . . . . . . . . . . . . 97
2.6.1 Limitations of the toolset (cid:29)exibility measure FTS . . . . . . . 97
2.6.2 Limitations of the WIP (cid:29)exibility measure FWIP . . . . . . . 98
2.6.3 Limitations of the time (cid:29)exibility measure Ftime . . . . . . . . 99
2.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
3 Optimizing Quali(cid:28)cations 101
3.1 Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
3.1.1 Complexity of the (cid:29)exibility measures . . . . . . . . . . . . . . 103
3.2 Properties of the Flexibility Measures . . . . . . . . . . . . . . . . . . 110
3.2.1 Properties of the toolset (cid:29)exibility measure FTS . . . . . . . . 110
3.2.2 Properties of the WIP (cid:29)exibility measure FWIP . . . . . . . . 113
3.2.3 Properties of the time (cid:29)exibility measure Ftime . . . . . . . . . 120
3.2.4 Properties of the system (cid:29)exibility measure FSYS . . . . . . . 120
3.3 Heuristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
3.3.1 Greedy heuristic . . . . . . . . . . . . . . . . . . . . . . . . . 121
3.3.2 Local search heuristic 1 . . . . . . . . . . . . . . . . . . . . . . 122
3.3.3 Local search heuristic 2 . . . . . . . . . . . . . . . . . . . . . . 123
3.3.4 Tabu search . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
3.3.5 Numerical experiments . . . . . . . . . . . . . . . . . . . . . . 126
3.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134
4 Extensions 137
4.1 Anticipating Dynamic WIP Quantities . . . . . . . . . . . . . . . . . 137
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TABLE DES MATI¨RES
4.1.1 Periodical weights . . . . . . . . . . . . . . . . . . . . . . . . . 138
4.1.2 Formulating dynamic (cid:29)exibility . . . . . . . . . . . . . . . . . 138
4.1.3 Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
4.1.4 Numerical experiments . . . . . . . . . . . . . . . . . . . . . . 140
4.2 Further Extensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
4.2.1 Recipe hold types . . . . . . . . . . . . . . . . . . . . . . . . . 142
4.2.2 Easiness levels of quali(cid:28)cations . . . . . . . . . . . . . . . . . 143
4.2.3 Recipe groups . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
4.2.4 Cluster tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
4.2.5 Recipe/tool availability . . . . . . . . . . . . . . . . . . . . . . 147
4.3 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148
5 Impact of Quali(cid:28)cation Management on Scheduling 151
5.1 Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
5.1.1 Literature on scheduling . . . . . . . . . . . . . . . . . . . . . 152
5.1.2 A scheduling simulator for a photolithography workshop . . . 156
5.1.3 A scheduling simulator for a etch workshop . . . . . . . . . . . 157
5.1.4 Batch optimization solver for a di(cid:27)usion area . . . . . . . . . . 165
5.2 Impact of Quali(cid:28)cations on Scheduling in a Photolithography area . . 167
5.2.1 Performance measures . . . . . . . . . . . . . . . . . . . . . . 167
5.2.2 Numerical Experiments . . . . . . . . . . . . . . . . . . . . . . 168
5.3 Impact of Quali(cid:28)cations on Scheduling in an Etch Area . . . . . . . . 178
5.3.1 Performance measures . . . . . . . . . . . . . . . . . . . . . . 178
5.3.2 Numerical experiments . . . . . . . . . . . . . . . . . . . . . . 179
5.4 Impact of Quali(cid:28)cations on Scheduling in a Di(cid:27)usion Area . . . . . . 184
5.4.1 Performance measures . . . . . . . . . . . . . . . . . . . . . . 184
5.4.2 Numerical experiments . . . . . . . . . . . . . . . . . . . . . . 185
5.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188
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TABLE DES MATI¨RES
6 Conclusions and Perspectives 191
A Dictionary 199
A.1 Dictionary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199
B Pseudo-Codes 203
B.1 Pseudo-code : Active Set method . . . . . . . . . . . . . . . . . . . . 203
C Implementations 205
C.1 The Quali(cid:28)cation Management Software . . . . . . . . . . . . . . . . 205
C.1.1 Extensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206
C.1.2 Calculating the (cid:29)exibility measures . . . . . . . . . . . . . . . 209
C.2 Scheduling Simulator for the Etch Area . . . . . . . . . . . . . . . . . 210
C.2.1 Input . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211
C.2.2 Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211
C.2.3 Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212
Bibliography 223
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