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RESEARCH PAPERS

A Systems Approach for Analyzing Uncertainties in Misalignment of a Fiberoptic System

[+] Author and Article Information
Satish Radhakrishnan, Ganesh Subbarayan

 Purdue University, West Lafayette, IN 47907

Luu Nguyen, William P. Mazotti

 National Semiconductor Corporation, Santa Clara, CA 95052

J. Electron. Packag 127(4), 391-396 (Dec 14, 2004) (6 pages) doi:10.1115/1.2056573 History: Received April 19, 2004; Revised December 14, 2004

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.

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Copyright © 2005 by American Society of Mechanical Engineers
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Figures

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Figure 1

The fiberoptic system chosen in the present study to illustrate systems approaches

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Figure 2

Function-variable matrix of a simple spring system and its partitioning into two subsystems is illustrated in the figure. Optimal partitioning leads to subsystems that are least interactive. The linking variables F1, x1 and F2 are system-level variables since they determine the nature of the interaction between the subsystems.

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Figure 3

Schematic illustration of the conversion from function-variable matrix to hypergraph to graph for the application of graph partitioning algorithms (4)

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Figure 4

Schematic illustration of calculation of edge weights based on sensitivity values

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Figure 5

Flow chart describing the simulated annealing procedure

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