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Massachusetts Institute of Technology

Development of a scalable superconducting memory

Abstract

dc:description.abstract

Superconducting computers promise very high computation speeds while also consuming far less power than their conventional counterparts. However, much of the progress in this field has been stymied by the lack of a scalable superconducting memory technology. In this thesis, I present the design of, and demonstrate the operation of, a superconducting nanowire-based memory cell. In contrast to existing designs, this cell operates by means of kinetic rather than geometric inductance. Thus, the cell size can be made much smaller than would otherwise be possible. With the successful operation of the single cell, paths to larger arrays are explored, and a small array demonstrated. The further development of the technology demonstrated in this work will allow for the production of large-scale superconducting processors, and the eventual development of superconducting supercomputers.

Degree

thesis:*
Department dc:contributor.department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2018

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Butters, Brenden A
Advisor dc:contributor.advisor
  • Karl K. Berggren.

Subjects

dc:subject × 1

Rights

dc:rights
Statement dc:rights
  • MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
Language dc:language.iso
eng

Identifiers

dc:identifier.*
Handle dc:identifier.uri
http://hdl.handle.net/1721.1/120406
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/120406

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Butters, Brenden A. Development of a scalable superconducting memory. Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120406