Escape routing using deep reinforcement learning

Hsu Fu Cheng; Dun Wei Cheng

doi:10.1117/12.3058004

5 February 2025 Escape routing using deep reinforcement learning

Hsu Fu Cheng, Dun Wei Cheng

Author Affiliations +

Proceedings Volume 13510, International Workshop on Advanced Imaging Technology (IWAIT) 2025; 135101B (2025) https://doi.org/10.1117/12.3058004
Event: International Workshop on Advanced Imaging Technology (IWAIT) 2025, 2025, Douliu City, Taiwan

Abstract

Escape routing is a critical task in the design of printed circuit boards (PCB) and integrated circuits (IC), aiming to establish effective connections among multiple pin points while avoiding path overlaps and interference from obstacles. This paper proposes a Deep Reinforcement Learning (DRL)-based solution utilizing a Deep Q-Network (DQN) to address the escape routing problem. The problem is modeled as a Markov Decision Process (MDP), and the agent learns effective routing strategies through interactions with the environment. Experiments were conducted in a 25×26 simulated grid environment, testing scenarios with 30, 60, and 90 pin points. This study highlights the potential of Deep Reinforcement Learning in solving escape routing problems, offering a novel approach to addressing routing challenges in PCB design.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hsu Fu Cheng and Dun Wei Cheng "Escape routing using deep reinforcement learning", Proc. SPIE 13510, International Workshop on Advanced Imaging Technology (IWAIT) 2025, 135101B (5 February 2025); https://doi.org/10.1117/12.3058004

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE