Sponsor
Portland State University. Department of Electrical and Computer Engineering
First Advisor
Malgorzata Chrzanowska-Jeske
Term of Graduation
Fall 2025
Date of Publication
12-17-2025
Document Type
Dissertation
Degree Name
Doctor of Philosophy (Ph.D.) in Electrical and Computer Engineering
Department
Electrical and Computer Engineering
Language
English
Subjects
Machine Learning, Monolithic 3D IC, Placement, Reinforcement Learning
Physical Description
1 online resource (xvi, 193 pages)
Abstract
This dissertation explores reinforcement learning (RL)–based approaches to address placement challenges in monolithic three-dimensional (3D) integrated circuits (ICs). Placement in 3D ICs introduces unique constraints—such as inter-tier connectivity and volume limitations—that compound the inherent complexity of the already NP-complete 2D placement problem. To tackle these issues, this work proposes new algorithms that combine RL with simulated annealing (SA) for efficient design-space exploration and high-quality placement solutions. A framework for dynamic hierarchical cluster assignment is introduced to guide the transformation from 2D to 3D while preserving layout structure and reducing search complexity. The proposed methods are evaluated on a range of benchmarks, demonstrating improvements in wirelength and area metrics compared to baseline techniques. The results highlight the feasibility of integrating RL and machine learning techniques in electronic design automation for next-generation 3D systems.
This dissertation is composed of a review book chapter, two peer-reviewed conference papers, and one journal paper under review. The introductory chapter explains the relationship between these contributions and situates them within the broader research context.
Rights
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Persistent Identifier
https://archives.pdx.edu/ds/psu/44350
Recommended Citation
Mansoor, Abdullah, "Monolithic 3D IC Placement Using Reinforcement Learning" (2025). Dissertations and Theses. Paper 6979.