Performance of a fiberoptic system depends on the coupling efficiency and the alignment retention capability. A fiberoptic system experiences performance degradation due to uncertainties in the alignment of the optical fibers with the laser beam. The laser devices are temperature sensitive, generate large heat fluxes, are prone to mechanical stresses induced and require stringent alignment tolerance due to their spot sizes. The performance of an optoelectronic system is also affected by many other factors such as geometric tolerances, uncertainties in the properties of the materials, optical parameters such as numerical aperture, etc. To analyze such a complex system, we need to understand the inter-relationships between various elements that together make the complex system. In this paper, we apply systematic, formal procedures for identifying the relationships between the critical system level parameters through system decomposition strategies. We have included the sensitivity of the variables with respect to the functions to assist in the system decomposition. We apply graph partitioning strategies to decompose the system into different subsystems. We also demonstrate system decomposition technique using a simple to implement simulated annealing algorithm. The results of system decomposition using graph partitioning technique and simulated annealing are also compared.

1.
Iezekiel
,
S.
, et al.
, 1997, “
Application of Silicon-Glass Technology to Microwave Photonic Multichip Modules
,” in
Proceedings of Interpack
, pp.
759
764
.
2.
Haftka
,
R. T.
, and
Gurdal
,
A.
, 1992,
Elements of Structural Optimization
,
Kluwer
, Dordrecht, The Netherlands.
3.
Kusiak
,
A.
, and
Larson
,
N.
, 1995, “
Decomposition and Representation Methods in Mechanical Design
,”
Trans. ASME
0097-6822,
117
, pp.
17
24
.
4.
Michelena
,
N. F.
, and
Papalambros
,
P. Y.
, 1995, “
Optimal Model-Based Partitioning for Power Train System Design
,”
1995 ASME Design Automation Conference
,
Boston
, MA.
5.
Bi
,
T.
,
Ni
,
Y.
,
Shen
,
C. M.
, and
Wu
,
F. F.
, 2002, “
An On-Line Distributed Intelligent Fault Section Estimation System for Large-Scale Power Networks
,”
Electr. Power Syst. Res.
0378-7796,
62
, pp.
173
182
.
6.
Saraydar
,
C. U.
, and
Yener
,
A.
, 2001, “
Adaptive Cell Sectorization for CDMA Systems
,”
IEEE J. Sel. Areas Commun.
0733-8716,
19
, No.
6
, pp.
1041
1051
.
7.
Walshaw
,
C.
, and
Cross
,
M.
, 2000, “
Mesh Partitioning: A Multilevel Balancing and Refinement Algorithm
,”
SIAM J. Sci. Comput. (USA)
1064-8275,
22
, No.
1
, pp.
63
80
.
8.
Ouyang
,
M.
,
Toulouse
,
M.
,
Thulasiraman
,
K.
,
Glover
,
F.
, and
Deogun
,
J. S.
, 2002, “
Multilevel Cooperative Search for the Circuit/Hypergraph Partitioning Problem
,”
IEEE Trans. Comput.-Aided Des.
0278-0070,
21
, No.
6
, pp.
685
693
.
9.
Subbarayan
,
G.
, and
Raj
,
R.
, 1999, “
A Methodology for Integrating Materials Science With System Engineering
,”
Mater. Des.
0264-1275,
20
, pp.
1
12
.
10.
Kernighan
,
B. W.
, and
Lin
,
S.
, 1970, “
An Efficient Heuristic Procedure for Partitioning Graphs
,”
Bell Syst. Tech. J.
0005-8580,
49
, pp.
291
307
.
11.
Fiduccia
,
C. M.
, and
Mattheyses
,
R. M.
, 1982, “
A Linear Time Heuristic for Improving Network Paritions
,” in
Proceedings of 19th IEEE Design Automation Conference
,
IEEE
, pp.
175
181
.
12.
Hendrickson
,
B.
, and
Leland
,
R.
, 1995, “
The Chaco User’s Guide
,” Version 2.0, Technical Report No. SAND95-2344,
Sandia National Laboratories
, Albuquerque, NM.
13.
Goldberg
,
M. K.
, and
Burstein
,
M.
, 1995, “
Heuristic Improvement Technique for Bisection of VLSI Networks
,” in
Proceedings IEEE International Conference on Computer Design: VLSI in Computers
, pp.
122
125
.
14.
Johnson
,
D. S.
,
Aragon
,
C. R.
,
McGeouch
,
L. A.
, and
Schevon
,
C.
, 1989, “
Optimization by Simulated Annealing: An Experimental Evaluation; Part I, Graph Partitioning
,”
Oper. Res.
0030-364X,
37
, No.
6
, pp.
865
892
.
15.
Sheild
,
J.
, 1987, “
Partitioning Concurrent VLSI Simulation Programs onto a Multiprocessor by Simulated Annealing
,”
IEEE Proceedings— Part E: Computers and Digital Techniques
,
134
(
1
), pp.
24
30
.
16.
Kirkpatrick
,
S.
, 1984, “
Optimization by Simulated Annealing: Quantitative Studies
,”
J. Stat. Phys.
0022-4715,
34
, No.
5–6
, pp.
975
86
.
17.
Suhir
,
E.
, 1999, “
Modeling of the Mechanical Behavior of Materials in ‘High-Tech’ Systems: Attributes and Review
,”
ASME J. Electron. Packag.
1043-7398,
121
, No.
4
, pp.
213
221
.
18.
Metropolis
,
N.
,
Rosenbluth
,
A.
,
Rosenbluth
,
M.
,
Teller
,
A.
, and
Teller
,
E.
, 1953, “
Equation of State Calculations by Fast Computing Machines
,”
J. Chem. Phys.
0021-9606,
21
, No.
6
, pp.
1087
1092
.
19.
Kirkpatrick
,
S.
,
Gelatt
,
C. D.
, Jr.
, and
Vecchi
,
M. P.
, 1983, “
Optimization by Simulated Annealing
,”
Science
0036-8075,
220
, No.
4598
, pp.
671
380
.
20.
Cerny
,
V.
, 1985, “
Thermodynamical Approach to the Traveling Salesman Problem: An Efficient Simulation Algorithm
,”
J. Optim. Theory Appl.
0022-3239,
45
, No.
1
, pp.
41
51
.
21.
Aarts
,
E.
, and
Krost
,
L.
, 1989,
Simulated Annealing and Boltzmann Machines, A Stochastic Approach to Combinatorial Optimization and Neural Computing
,
Wiley
, New York.
22.
Haftka
,
R. T.
, and
Gurdal
,
A.
, 1992,
Elements of Structural Optimization
,
Kluwer
, Dordrecht, The Netherlands.
23.
MATHEMATICA, Version 4.2, Copyright 1988–2002 Wolfram Research, Inc.
24.
Radhakrishnan
,
S.
,
Subbarayan
,
G.
,
Nguyen
,
L.
, and
Mazotti
,
W.
, 2003, “
Optimization and Stochastic Procedures for Robust Design of Photonic Packages With Applications to a Generic Package
,” in
Proceedings of the 53rd Electronics Components and Technology Conference, IEEE
, pp.
720
726
.
You do not currently have access to this content.