Design and Investigation of Genetic Algorithmic and Reinforcement Learning Approaches to Wire Crossing Reductions for pNML Devices

Perpendicular nanomagnet logic (pNML) is an emerging post-CMOS technology which encodes binary data in the polarization of single-domain nanomagnets and performs operations via fringing field interactions. Currently, there is no complete top-down workflow for pNML. Researchers must instead simultaneously handle place-and-route, timing, and logic minimization by hand. These tasks include multiple NP-Hard subproblems, and the lack of automated tools for solving them for pNML precludes the design of large-scale pNML circuits.

Name thesis:degree_name

M.S. in Engineering Science

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical Engineering

Year dc:date.available

2019

Author dc:creator

• Gunter, Alexander Keith

Contributors dc:contributor

• Matthew A. Morrison
• Yixin Chen
• Richard Gordon

• Crossing Reduction
• Electronic Design Automation
• Genetic Algorithm
• Nanomagnet Logic
• Quantum-Dot Cellular Automata
• Reinforcement Learning
• Electrical and Computer Engineering

Repository record dc:identifier

https://egrove.olemiss.edu/etd/1597

OAI identifier oai:identifier

oai:egrove.olemiss.edu:etd-2596