Related Experiment Video
Updated: May 24, 2025
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Visualizing Uniaxial-strain Manipulation of Antiferromagnetic Domains in Fe1+YTe Using a Spin-polarized Scanning Tunneling Microscope
Published on: March 24, 2019
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Field-Induced Phase Transitions in Cuprate Superconductors for Cryogenic in-Memory Computing
Thomas Günkel1, Jordi Alcalà1, Alejandro Fernández1
1Insititut de Ciència de Materials de Barcelona, ICMAB-CSIC, Campus de la UAB, Bellaterra, 08193, Spain.
Small (Weinheim an Der Bergstrasse, Germany)
|March 3, 2025
Summary
This study explores YBCO/LSMO superconducting structures for energy-efficient cryogenic memory. Hole-induced phase transitions drive memristive switching, enabling high-performance in-memory computing at low temperatures.
Area of Science:
- Materials Science
- Condensed Matter Physics
- Superconducting Electronics
Background:
- Energy-efficient cryogenic memory is crucial for quantum computing and superconducting electronics.
- Neuromorphic systems offer superior energy efficiency for in-memory computing.
- Strongly correlated oxides with Mott transitions are promising for analog memory.
Purpose of the Study:
- Investigate YBCO/LSMO superconducting structures for high-performance cryogenic memristive switching.
- Understand switching mechanisms in these materials at low temperatures.
- Develop a physics-based model for circuit-level design.
Main Methods:
- Fabrication of YBCO/LSMO superconducting heterostructures.
- Characterization of memristive switching effects at cryogenic temperatures.
- Analysis of switching mechanisms through experimental data.
Main Results:
- Observed non-volatile multilevel memristive switching in YBCO/LSMO structures.
- Identified two competing switching mechanisms: oxygen vacancy and electric carrier movement.
- Determined that hole-induced phase transitions dominate low-temperature switching dynamics.
Conclusions:
- YBCO/LSMO structures show potential for high-performance cryogenic memory.
- Understanding switching mechanisms is key to optimizing device performance.
- A validated physics-based model facilitates circuit design for neuromorphic applications.
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