RulePlanner: All-in-One Reinforcement Learner for Unifying Design Rules in 3D Floorplanning

Researchers propose RulePlanner, a deep reinforcement learning framework that unifies the handling of complex hardware design rules in 3D integrated circuit floorplanning. The approach addresses a critical bottleneck in chip design by automating compliance with multiple design rules simultaneously, reducing manual post-processing and accelerating the path from design to manufacturing.

The scaling of modern semiconductor technology has created an increasingly complex design challenge: ensuring that circuit layouts comply with numerous hardware design rules across multiple layers. Traditional floorplanning tools handle specific rules in isolation, forcing engineers to manually adjust designs repeatedly to satisfy overlooked constraints—a process that consumes significant time and expertise. RulePlanner tackles this systemic inefficiency by treating design rule compliance as a unified reinforcement learning problem.

The framework's innovation lies in three architectural components: matrix-based representations that mathematically encode design rules, action space constraints that prevent invalid placement decisions before they occur, and reward signals that quantitatively measure constraint satisfaction. This design prevents the model from exploring invalid solution spaces, improving both convergence speed and reliability. The approach proves transferable to unseen circuits, suggesting it can generalize across different chip designs without retraining.

For the semiconductor industry, this addresses a genuine pain point in the design-to-manufacturing pipeline. EDA (Electronic Design Automation) tools represent a multi-billion-dollar market segment, and any framework that meaningfully reduces engineering labor while improving design quality carries substantial commercial value. The research also signals how AI is penetrating hardware design workflows, an area historically resistant to automation due to domain complexity.

The extensibility claim—that new design rules can be incorporated into the framework—positions this work as foundational rather than incremental. As semiconductor rules continue to evolve with new technology nodes, this adaptability becomes increasingly valuable. The forthcoming code release will likely accelerate adoption within academia and potentially influence commercial EDA tool development.

• →RulePlanner unifies multiple hardware design rules into a single RL framework, eliminating manual post-processing bottlenecks in chip floorplanning.
• →The approach uses constrained action spaces to prevent rule violations before they occur, improving solution quality and convergence efficiency.
• →Demonstrated transferability to unseen circuits suggests the model can generalize across different chip designs without retraining.
• →The framework's extensibility enables accommodation of emerging design rules as semiconductor technology continues to evolve.
• →This work represents meaningful AI penetration into EDA tools, addressing a high-value segment of the semiconductor design pipeline.