Neural optimization network for minimum-via layer assignment

Abstract Since Hopfield introduced the crossbar associative network (Hopfield network) [1] in 1982, numerous modifications of this model have been proposed for purposes such as to improve the stability, to prevent the local minima, for digital computer simulation or analog VLSI implementation, etc. A new modification of the structure of the Hopfield neural network is suggested. This new model employs a special feedback loop called delayed self-feedback in order to improve the system stability. This model is tested for the VLSI layer assignment problem. The average number of vias achieved by this layer assignment method is close to minimum (95.8%).

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