Superconducting Nanowire Integrated Circuits for Scalable Cryogenic Memory

Superconducting nanowire integrated circuits (SNICs) are a promising class of cryogenic electronics that harness the zero resistance, high kinetic inductance, and nanoscale geometry of ultrathin superconducting wires to implement logic, memory, amplification, and sensing with minimal energy dissipation. Unlike Josephson-junction-based circuits, SNICs support compact, planar layouts compatible with single-layer fabrication and operation in unshielded cryogenic environments. This thesis develops superconducting nanowire memory (SNM) as a scalable implementation of SNICs. A modular cell architecture is introduced, exploiting hysteretic switching and inductive asymmetry to enable nonvolatile digital state storage with zero static power consumption. A hierarchical design framework is established, combining automated layout generation, electrothermal simulation in LTspice, and microscopic modeling using the time-dependent Ginzburg–Landau (TDGL) formalism. To enable scalable integration, this work implements a row–column SNM array layout and demonstrates fabrication across full 4-inch wafers using a planar, singlelayer process. Cryogenic measurements validate reliable operation in both single cells and multi-cell arrays, confirming the predictive accuracy of the design and modeling framework. Tradeoffs in bias current levels, pulse timing, and read/write conditions are systematically evaluated through cryogenic measurements, revealing their impact on bit error rate, operational margins, and energy efficiency across single cells and arrays. Together, these contributions establish SNICs as a viable and extensible platform for cryogenic memory, providing the tools, models, and infrastructure needed to enable broader adoption in quantum computing, neuromorphic systems, and other energy-constrained cryogenic applications.

Name thesis:degree_name

Doctoral

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

2025

Author dc:creator

• Medeiros, Owen A.

Advisor dc:contributor.advisor

• Berggren, Karl K.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/164062

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/164062