Unsupervised Graph Neural Network Framework for Balanced Multipatterning in Advanced Electronic Design Automation Layouts

TL;DR

This paper introduces an unsupervised graph neural network framework for multipatterning in electronic design automation, effectively balancing layout decomposition constraints with improved solution quality.

Contribution

It presents a novel hybrid GNN-based workflow that addresses multipatterning as a constrained graph coloring problem, enhancing scalability and solution balance.

Findings

Achieves conflict-free layout decomposition

Ensures consistent feature balancing across masks

Demonstrates effectiveness on proprietary and open data sets

Abstract

Multipatterning is an essential decomposition strategy in electronic design automation (EDA) that overcomes lithographic limitations when printing dense circuit layouts. Although heuristic-based backtracking and SAT solvers can address these challenges, they often struggle to simultaneously handle both complex constraints and secondary objectives. In this study, we present a hybrid workflow that casts multipatterning as a variant of a constrained graph coloring problem with the primary objective of minimizing feature violations and a secondary objective of balancing the number of features on each mask. Our pipeline integrates two main components: (1) A GNN-based agent, trained in an unsupervised manner to generate initial color predictions, which are refined by (2) refinement strategies (a GNN-based heuristic and simulated annealing) that together enhance solution quality and balance. Experimental evaluation in both proprietary data sets and publicly available open source layouts demonstrate complete conflict-free decomposition and consistent color balancing. The proposed framework provides a reproducible, data-efficient and deployable baseline for scalable layout decomposition in EDA workflows.