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An integrated planning approach for a nanodeposition manufacturing process

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Abstract

This paper deals with the planning problem in a nanodeposition manufacturing process, in which a toolbit that consists of a multilayer grid of micro/nanofluidic channels is used to deposit nanoscale liquid materials to desired positions on workparts to form solid patterns. The objective is to obtain a planning procedure that achieves efficient throughput for the studied nanodeposition manufacturing systems. We break down the studied problem into several sub-problems as design pattern decomposition, nanopore assignment, liquid material routing in the multilayer grid fluidic network, and toolbit path planning. Efficient algorithms are proposed to solve these sub-problems individually, and then finally integrated into a framework that systematically plans the nanodeposition manufacturing process. A software tool that plans, simulates, and controls the nanodeposition manufacturing process by implementing the proposed algorithms is reported in this paper.

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References

  1. Gruenbaum B, Shephard GC (1986) Tilings and patterns. W H Freeman & Co, New York

    Google Scholar 

  2. Jarvis RA (1973) On the identification of the convex hull of a finite set of points in the plane. Inf Process Lett 2:18–21

    Article  MATH  Google Scholar 

  3. Graham RL (1972) An efficient algorithm for determining the convex hull of a finite planar set. Inf Process Lett 1:132–133

    Article  MATH  Google Scholar 

  4. Preparata F, Hong SJ (1977) Convex hulls of finite sets of points in two and three dimensions. Commun ACM 20(2):87–93

    Article  MATH  MathSciNet  Google Scholar 

  5. de Berg M, van Kreveld M, Overmars M, Schwarzkopf O (2000) Computational geometry: algorithms and applications, 2nd edn. Springer-Verlag, London

    MATH  Google Scholar 

  6. Karp RM (1975) On the complexity of combinatorial problems. Netw 5:45–68

    MATH  MathSciNet  Google Scholar 

  7. Kramer MR, van Leeuwen J (1984) The complexity of wire routing and finding the minimum area layouts for arbitrary VLSI circuits. In: Preparata FP (ed) Advance in computing research 2: VLSI theory. JAI Press, London, pp 129–146

    Google Scholar 

  8. Kolliopoulos SG, Stein C (1998) Approximation disjoint-path problems using greedy algorithms and packing integer programs. Sixth International Conference on Integer Programming and Combinatorial Optimization (IPCO) 153–168, 1998, Houston, USA

  9. Lau LC (2004) An approximate max-Steiner-tree-packing min-Steiner-cut theorem. In Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science 61–70

  10. Wagner D (1993) Simple algorithms for Steiner trees and paths packing problems in planar graphs. CWI Q 6(3):219–240

    MATH  Google Scholar 

  11. Suzuki H, Akama T, Nishizeki T (1990) Finding Steriner forests in planar graphs. In Proceedings of the 1st Annual ACM-SIAM Symposium on Discrete Algorithms 444–453

  12. Tang D (2006) Logistics of fluid delivery in micro/nano fluidic networks. Ph.D. Dissertation, University of Illinois at Urbana-Champaign

  13. Garey MR, Johnson DS (1979) Computers and intractability: a guide to the theory of NP-completeness. Freeman, San Francisco

    MATH  Google Scholar 

  14. Takahashi H, Matsuyama A (1980) An approximate solution for the Steiner tree problem in graphs. Math Jpn 24(6):573–577

    MATH  MathSciNet  Google Scholar 

  15. Kou L, Markowsky G, Berman L (1981) A fast algorithm for Steiner trees. Acta Informatica 15:141–145

    Article  MATH  MathSciNet  Google Scholar 

  16. Robins G, Zelikovsky A (2000) Improved Steiner tree approximation in graphs. In proceedings of the 10th Annual ACM-SIAM Symposium on Discrete Algorithms 770–779

  17. Sunazawa M, Hani T (1974) Low-power cross-point switch matrix for space-division digital-switching network. ISSCC Digest of Technical Papers 206–207

  18. Ahuja RK, Magnanti TL, Orlin JB (1993) Network flows: theory, algorithms, and applications. Prentice Hall, New Jersey

    Google Scholar 

  19. Lawler EL, Lenstra JK, Rinnooy Kan AHG, Shmoys DB (1985) The traveling salesman problem: a guided tour of combinatorial optimization. Wiley, New York

    MATH  Google Scholar 

  20. Orloff CS (1974) A fundamental problem in vehicle routing. Netw 4:35–64

    Article  MATH  MathSciNet  Google Scholar 

  21. Christofides N (1976) Worst case analysis of a new heuristic for the traveling salesman problem. Technical Report 388, Graduate School of Industrial Administration, Carnegie-Mellon University, Pittsburgh, PA

  22. Kruskal JB (1956) On the shortest spanning subtree of a graph and the traveling salesman problem. In Proc Am Math Soc 7(1):48–50

    MathSciNet  Google Scholar 

  23. Prim RC (1957) Shortest connection networks and some generalizations. Bell Syst Tech J 36:1389–1401

    Google Scholar 

  24. Lawler EL (1976) Combinatorial optimization: networks and matriods. Holt, Rinehart and Winston, New York

    Google Scholar 

  25. Lin S (1965) Computer solutions of the traveling salesman problem. Bell System Technical Journal 44:2245–2269

    MATH  MathSciNet  Google Scholar 

  26. Lin S, Kernighan BW (1973) An effective heuristic algorithm for the traveling salesman problem. Oper Res 21:498–516

    Article  MATH  MathSciNet  Google Scholar 

  27. Chisman JA (1975) The clustered traveling salesman problem. Comput Oper Res 2:115–119

    Article  Google Scholar 

  28. Jongens K, Volgenat T (1985) The symmetric clustered traveling salesman problem. Eur J Oper Res 19:68–75

    Article  MATH  Google Scholar 

  29. Guttmann-Beck N, Hassin R, Khuller S, Raghavachari B (2000) Approximation algorithms with bounded performance guarantees for the clustered traveling salesman problem. Algorithmica 28:422–437

    Article  MATH  MathSciNet  Google Scholar 

  30. Johnson DS (1974) Worst case behavior of graph coloring algorithms. In Proceedings of 5th South-Eastern Conference on Combinatorics, Graph Theory and Computing, Canada 513–528

  31. Wigderson A (1983) Improving the performance guarantee for approximate graph coloring. Inf Process Lett 30:729–735

    MATH  MathSciNet  Google Scholar 

  32. Halldórsson MM (1993) A still better performance guarantee for approximate graph coloring. Inf Process Lett 45:19–23

    Article  MATH  Google Scholar 

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Authors and Affiliations

  1. Intel Corporation, 5000 West Chandler Boulevard, Chandler, AZ, 85226, USA

    Dong Tang

  2. University of Illinois at Urbana-Champaign, 4016 BIF, 515 East Gregory Drive, Champaign, IL, 61820, USA

    Udatta S. Palekar

  3. 2130 East Saltsage Drive, Phoenix, AZ, 85048, USA

    Dong Tang

Authors
  1. Dong Tang
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  2. Udatta S. Palekar
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Correspondence to Dong Tang.

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Tang, D., Palekar, U.S. An integrated planning approach for a nanodeposition manufacturing process. Int J Adv Manuf Technol 51, 561–573 (2010). https://doi.org/10.1007/s00170-010-2651-1

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  • DOI: https://doi.org/10.1007/s00170-010-2651-1

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