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Updated: Sep 5, 2025

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Scalable Quantum Integrated Circuits on Superconducting Two-Dimensional Electron Gas Platform
Published on: August 2, 2019
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From Quantum Materials to Microsystems
Riccardo Bertacco1,2, Giancarlo Panaccione3, Silvia Picozzi4
1Dipartimento di Fisica, Politecnico di Milano, 20133 Milan, Italy.
Materials (Basel, Switzerland)
|July 9, 2022
Summary
This study proposes a roadmap to integrate quantum materials, specifically ferroics, into microsystems for novel computing. By leveraging ferroelectric Rashba semiconductors like GeTe, we aim to bridge fundamental research with CMOS-compatible technology.
Area of Science:
- Condensed Matter Physics
- Materials Science
- Quantum Mechanics
Background:
- Quantum materials exhibit complex properties due to intertwined degrees of freedom.
- These materials are currently distant from micro-nano integrated devices and microsystems.
- Ferroic materials, with magnetic or electric order, offer potential for integration.
Purpose of the Study:
- To bridge the gap between fundamental quantum materials research and microsystem applications.
- To design a technology platform for unconventional computing using ferroic quantum materials.
- To outline a research pipeline from microscopic modeling to device applications.
Main Methods:
- Examining the integration challenges of quantum materials with microsystems.
- Using ferroic materials as a case study, inspired by semiconductor technology.
- Focusing on the paradigmatic example of GeTe, a ferroelectric Rashba semiconductor.
Main Results:
- Identified ferroic quantum materials as a promising avenue for bridging fundamental research and technology.
- Demonstrated the potential of GeTe as a foundational material for a new class of semiconductors.
- Outlined a pathway for developing CMOS-compatible technology from quantum materials.
Conclusions:
- An integrated approach involving academia and industry is crucial for advancing quantum materials.
- Ferroic quantum materials can enable novel computing paradigms.
- The research pipeline from modeling to application can translate discoveries into viable technologies.
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