Machine Learning Optimal Ordering in Global Routing Problems in Semiconductors
In this work, we propose a new method for ordering nets during the process of layer assignment in global routing problems. The global routing problems that we focus on in this work are based on routing problems that occur in the design of substrates in multilayered semiconductor packages. The proposed new method is based on machine learning techniques and we show that the proposed method supersedes conventional net ordering techniques based on heuristic score functions. We perform global routing experiments in multilayered semiconductor package environments in order to illustrate that the routing order based on our new proposed technique outperforms previous methods based on heuristics. Our approach of using machine learning for global routing targets specifically the net ordering step which we show in this work can be significantly improved by deep learning.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
A Deep Reinforcement Learning Approach for Global Routing
Global routing has been a historically challenging problem in electronic circuit design, where the challenge is to connect a large and arbitrary number of circuit components with wires without violating the design rules …
Deep Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Machine Learning Algorithms for Improving Exact Classical Solvers in Mixed Integer Continuous Optimization
Integer and mixed-integer nonlinear programming (INLP, MINLP) are central to logistics, energy, and scheduling, but remain computationally challenging. This survey examines how machine learning and reinforcement learning…
Reinforcement LearningRPT*: Global Planning with Probabilistic Terminals for Target Search in Complex Environments
Routing problems such as Hamiltonian Path Problem (HPP), seeks a path to visit all the vertices in a graph while minimizing the path cost. This paper studies a variant, HPP with Probabilistic Terminals (HPP-PT), where ea…
Efficiently Exploring Ordering Problems through Conflict-directed Search
In planning and scheduling, solving problems with both state and temporal constraints is hard since these constraints may be highly coupled. Judicious orderings of events enable solvers to efficiently make decisions over…
BenchmarkingSchedulingSymmetry-preserving graph attention network to solve routing problems at multiple resolutions
Travelling Salesperson Problems (TSPs) and Vehicle Routing Problems (VRPs) have achieved reasonable improvement in accuracy and computation time with the adaptation of Machine Learning (ML) methods. However, none of the …
Graph Attention