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

In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).

Persistent Identifier

https://archives.pdx.edu/ds/psu/44350

Available for download on Friday, December 17, 2027

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