### Abstract

This paper presents a new approach using a neural network model for the multilayer topological via minimization problem in a switchbox. Our algorithm consists of three steps: 1) dividing multiterminal nets into two-terminal nets, 2) finding a layer-assignment of the twoterminal nets by a neural network model so as to minimize the number of unassigned nets, and 3) embedding one via for each imassigned net by Marek-Sadowska's algorithm. The neural network model is composed of N x M processing elements to assign N two-terminal nets in an M -layer switchbox. The performante of our algorithm is verified by 15 benchmark problems where it can find optimum or near-optimum solutions. In the two-layer Burstein's switchbox, our algorithm finds a 15-via solution while the best known solution requires 20 vias.

Original language | English |
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Pages (from-to) | 1012-1020 |

Number of pages | 9 |

Journal | IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems |

Volume | 15 |

Issue number | 8 |

DOIs | |

Publication status | Published - 1996 |

Externally published | Yes |

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### ASJC Scopus subject areas

- Electrical and Electronic Engineering
- Hardware and Architecture
- Computer Science Applications
- Computational Theory and Mathematics

### Cite this

**A neural network model for multilayer topological via minimization in a switchbox.** / Funabiki, Nobuo; Nishikawa, Seishi.

Research output: Contribution to journal › Article

*IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems*, vol. 15, no. 8, pp. 1012-1020. https://doi.org/10.1109/43.511580

}

TY - JOUR

T1 - A neural network model for multilayer topological via minimization in a switchbox

AU - Funabiki, Nobuo

AU - Nishikawa, Seishi

PY - 1996

Y1 - 1996

N2 - This paper presents a new approach using a neural network model for the multilayer topological via minimization problem in a switchbox. Our algorithm consists of three steps: 1) dividing multiterminal nets into two-terminal nets, 2) finding a layer-assignment of the twoterminal nets by a neural network model so as to minimize the number of unassigned nets, and 3) embedding one via for each imassigned net by Marek-Sadowska's algorithm. The neural network model is composed of N x M processing elements to assign N two-terminal nets in an M -layer switchbox. The performante of our algorithm is verified by 15 benchmark problems where it can find optimum or near-optimum solutions. In the two-layer Burstein's switchbox, our algorithm finds a 15-via solution while the best known solution requires 20 vias.

AB - This paper presents a new approach using a neural network model for the multilayer topological via minimization problem in a switchbox. Our algorithm consists of three steps: 1) dividing multiterminal nets into two-terminal nets, 2) finding a layer-assignment of the twoterminal nets by a neural network model so as to minimize the number of unassigned nets, and 3) embedding one via for each imassigned net by Marek-Sadowska's algorithm. The neural network model is composed of N x M processing elements to assign N two-terminal nets in an M -layer switchbox. The performante of our algorithm is verified by 15 benchmark problems where it can find optimum or near-optimum solutions. In the two-layer Burstein's switchbox, our algorithm finds a 15-via solution while the best known solution requires 20 vias.

UR - http://www.scopus.com/inward/record.url?scp=0030215548&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0030215548&partnerID=8YFLogxK

U2 - 10.1109/43.511580

DO - 10.1109/43.511580

M3 - Article

VL - 15

SP - 1012

EP - 1020

JO - IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

JF - IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

SN - 0278-0070

IS - 8

ER -