Routability-driven ordered escape routing method based on neural network

This study presents an innovative approach that utilizes neural network-based techniques to address the Ordered Escape Routing (OER) problem. The OER problem holds a crucial position in integrated circuit design, requiring multiple pins to be connected to the boundary of a chip in a specific order while ensuring that the routing paths do not cross. Additionally, the total wire length must be minimized during the process. To tackle this challenging problem, we propose a novel routability-driven method that leverages neural networks (NN) to predict feasible routing paths that satisfy the required conditions. This approach not only ensures non-crossing paths but also strictly adheres to the specified pin connection order, while further minimizing the total wire length under these constraints. The core of this technique lies in the efficient predictive capabilities of neural networks.