Neural computation network for global routing

Global routing is a crucial step in circuit layout. Under the constraint of the relative positions of circuit blocks enforced by placement, the global routing develops an effective plan such that the interconnections of nets can be completed efficiently. This problem has been proven to be NP-complete, and most of the currently available algorithms are heuristic. The paper proposes a new neural-computation-network architecture based on the Hopfield and Tank model for the global-routing problem. This network is constructed using two layers of neurons. One layer is used for minimizing the total path length and distributing interconnecting wires evenly between channels. The other layer is used for channel-capacity enforcement. This network is proven to be able to converge to a stable state. A set of randomly generated testing examples are used to verify the performance of the approach. A reduction in total path length of about 20% is attained by this network.