Date of Award
2019
Document Type
Thesis
Degree Name
M.S. in Engineering Science
Department
Electrical Engineering
First Advisor
Matthew A. Morrison
Second Advisor
Yixin Chen
Third Advisor
Richard Gordon
Relational Format
dissertation/thesis
Abstract
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.
Recommended Citation
Gunter, Alexander Keith, "Design and Investigation of Genetic Algorithmic and Reinforcement Learning Approaches to Wire Crossing Reductions for pNML Devices" (2019). Electronic Theses and Dissertations. 1597.
https://egrove.olemiss.edu/etd/1597