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    Rights statement: This is the author’s version of a work that was accepted for publication in Discrete Optimization. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Discrete Optimization, ???, ?, 2016 DOI: 10.1016/j.disopt.2016.07.001

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On the complexity of Wafer-to-Wafer Integration

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On the complexity of Wafer-to-Wafer Integration. / Bougeret, Marin ; Boudet, Vincent; Dokka Venkata Satyanaraya, Trivikram et al.
In: Discrete Optimization, 29.07.2016.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Bougeret, M, Boudet, V, Dokka Venkata Satyanaraya, T, Duvillié, G & Giroudeau, R 2016, 'On the complexity of Wafer-to-Wafer Integration', Discrete Optimization. https://doi.org/10.1016/j.disopt.2016.07.001

APA

Bougeret, M., Boudet, V., Dokka Venkata Satyanaraya, T., Duvillié, G., & Giroudeau, R. (2016). On the complexity of Wafer-to-Wafer Integration. Discrete Optimization. Advance online publication. https://doi.org/10.1016/j.disopt.2016.07.001

Vancouver

Bougeret M, Boudet V, Dokka Venkata Satyanaraya T, Duvillié G, Giroudeau R. On the complexity of Wafer-to-Wafer Integration. Discrete Optimization. 2016 Jul 29. Epub 2016 Jul 29. doi: 10.1016/j.disopt.2016.07.001

Author

Bougeret, Marin ; Boudet, Vincent ; Dokka Venkata Satyanaraya, Trivikram et al. / On the complexity of Wafer-to-Wafer Integration. In: Discrete Optimization. 2016.

Bibtex

@article{1151d1586a6549a7b25562cc89d41fa6,
title = "On the complexity of Wafer-to-Wafer Integration",
abstract = "In this paper we consider the Wafer-to-Wafer Integration problem. A wafer can be seen as a pp-dimensional binary vector. The input of this problem is described by mm multisets (called “lots”), where each multiset contains nn wafers. The output of the problem is a set of nn disjoint stacks, where a stack is a set of mm wafers (one wafer from each lot). To each stack we associate a pp-dimensional binary vector corresponding to the bit-wise AND operation of the wafers of the stack. The objective is to maximize the total number of “1” in the nn stacks. We provide m1−ϵm1−ϵ and p1−ϵp1−ϵ non-approximability results even for n=2n=2, f(n)f(n) non-approximability for any polynomial-time computable function ff, as well as a View the MathML sourcepr-approximation algorithm for any constant rr. Finally, we show that the problem is View the MathML sourceFPT when parameterized by pp, and we use this View the MathML sourceFPT algorithm to improve the running time of the View the MathML sourcepr-approximation algorithm.",
keywords = "Wafer-to-Wafer Integration, Min sum 0, Max sum 1, Approximability, FPT algorithm, Multidimensional binary vector assignment",
author = "Marin Bougeret and Vincent Boudet and {Dokka Venkata Satyanaraya}, Trivikram and Guillerme Duvilli{\'e} and Rodolphe Giroudeau",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in Discrete Optimization. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Discrete Optimization, ???, ?, 2016 DOI: 10.1016/j.disopt.2016.07.001",
year = "2016",
month = jul,
day = "29",
doi = "10.1016/j.disopt.2016.07.001",
language = "English",
journal = "Discrete Optimization",
issn = "1572-5286",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - On the complexity of Wafer-to-Wafer Integration

AU - Bougeret, Marin

AU - Boudet, Vincent

AU - Dokka Venkata Satyanaraya, Trivikram

AU - Duvillié, Guillerme

AU - Giroudeau, Rodolphe

N1 - This is the author’s version of a work that was accepted for publication in Discrete Optimization. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Discrete Optimization, ???, ?, 2016 DOI: 10.1016/j.disopt.2016.07.001

PY - 2016/7/29

Y1 - 2016/7/29

N2 - In this paper we consider the Wafer-to-Wafer Integration problem. A wafer can be seen as a pp-dimensional binary vector. The input of this problem is described by mm multisets (called “lots”), where each multiset contains nn wafers. The output of the problem is a set of nn disjoint stacks, where a stack is a set of mm wafers (one wafer from each lot). To each stack we associate a pp-dimensional binary vector corresponding to the bit-wise AND operation of the wafers of the stack. The objective is to maximize the total number of “1” in the nn stacks. We provide m1−ϵm1−ϵ and p1−ϵp1−ϵ non-approximability results even for n=2n=2, f(n)f(n) non-approximability for any polynomial-time computable function ff, as well as a View the MathML sourcepr-approximation algorithm for any constant rr. Finally, we show that the problem is View the MathML sourceFPT when parameterized by pp, and we use this View the MathML sourceFPT algorithm to improve the running time of the View the MathML sourcepr-approximation algorithm.

AB - In this paper we consider the Wafer-to-Wafer Integration problem. A wafer can be seen as a pp-dimensional binary vector. The input of this problem is described by mm multisets (called “lots”), where each multiset contains nn wafers. The output of the problem is a set of nn disjoint stacks, where a stack is a set of mm wafers (one wafer from each lot). To each stack we associate a pp-dimensional binary vector corresponding to the bit-wise AND operation of the wafers of the stack. The objective is to maximize the total number of “1” in the nn stacks. We provide m1−ϵm1−ϵ and p1−ϵp1−ϵ non-approximability results even for n=2n=2, f(n)f(n) non-approximability for any polynomial-time computable function ff, as well as a View the MathML sourcepr-approximation algorithm for any constant rr. Finally, we show that the problem is View the MathML sourceFPT when parameterized by pp, and we use this View the MathML sourceFPT algorithm to improve the running time of the View the MathML sourcepr-approximation algorithm.

KW - Wafer-to-Wafer Integration

KW - Min sum 0

KW - Max sum 1

KW - Approximability

KW - FPT algorithm

KW - Multidimensional binary vector assignment

U2 - 10.1016/j.disopt.2016.07.001

DO - 10.1016/j.disopt.2016.07.001

M3 - Journal article

JO - Discrete Optimization

JF - Discrete Optimization

SN - 1572-5286

ER -