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Cross-architecture tuning of silicon and SiGe-based quantum devices using machine learning.
Severin, B; Lennon, D T; Camenzind, L C; Vigneau, F; Fedele, F; Jirovec, D; Ballabio, A; Chrastina, D; Isella, G; de Kruijf, M; Carballido, M J; Svab, S; Kuhlmann, A V; Geyer, S; Froning, F N M; Moon, H; Osborne, M A; Sejdinovic, D; Katsaros, G; Zumbühl, D M; Briggs, G A D; Ares, N.
Afiliación
  • Severin B; Department of Materials, University of Oxford, Parks Road, Oxford, OX1 3PH, UK.
  • Lennon DT; Department of Materials, University of Oxford, Parks Road, Oxford, OX1 3PH, UK.
  • Camenzind LC; Department of Physics, University of Basel, Basel, 4056, Switzerland.
  • Vigneau F; Department of Materials, University of Oxford, Parks Road, Oxford, OX1 3PH, UK.
  • Fedele F; Department of Materials, University of Oxford, Parks Road, Oxford, OX1 3PH, UK.
  • Jirovec D; Institute of Science and Technology Austria, Am Campus 1, Klosterneuburg, 3400, Austria.
  • Ballabio A; L-NESS, Dipartimento di Fisica, Politecnico di Milano, Polo di Como, ViaAnzani 42, Como, 22100, Italy.
  • Chrastina D; L-NESS, Dipartimento di Fisica, Politecnico di Milano, Polo di Como, ViaAnzani 42, Como, 22100, Italy.
  • Isella G; L-NESS, Dipartimento di Fisica, Politecnico di Milano, Polo di Como, ViaAnzani 42, Como, 22100, Italy.
  • de Kruijf M; Department of Physics, University of Basel, Basel, 4056, Switzerland.
  • Carballido MJ; Department of Physics, University of Basel, Basel, 4056, Switzerland.
  • Svab S; Department of Physics, University of Basel, Basel, 4056, Switzerland.
  • Kuhlmann AV; Department of Physics, University of Basel, Basel, 4056, Switzerland.
  • Geyer S; Department of Physics, University of Basel, Basel, 4056, Switzerland.
  • Froning FNM; Department of Physics, University of Basel, Basel, 4056, Switzerland.
  • Moon H; Department of Materials, University of Oxford, Parks Road, Oxford, OX1 3PH, UK.
  • Osborne MA; Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK.
  • Sejdinovic D; Department of Statistics, University of Oxford, 24-29 St Giles, Oxford, OX1 3LB, UK.
  • Katsaros G; Institute of Science and Technology Austria, Am Campus 1, Klosterneuburg, 3400, Austria.
  • Zumbühl DM; Department of Physics, University of Basel, Basel, 4056, Switzerland.
  • Briggs GAD; Department of Materials, University of Oxford, Parks Road, Oxford, OX1 3PH, UK.
  • Ares N; Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK. natalia.ares@eng.ox.ac.uk.
Sci Rep ; 14(1): 17281, 2024 Jul 27.
Article en En | MEDLINE | ID: mdl-39068242
ABSTRACT
The potential of Si and SiGe-based devices for the scaling of quantum circuits is tainted by device variability. Each device needs to be tuned to operation conditions and each device realisation requires a different tuning protocol. We demonstrate that it is possible to automate the tuning of a 4-gate Si FinFET, a 5-gate GeSi nanowire and a 7-gate Ge/SiGe heterostructure double quantum dot device from scratch with the same algorithm. We achieve tuning times of 30, 10, and 92 min, respectively. The algorithm also provides insight into the parameter space landscape for each of these devices, allowing for the characterization of the regions where double quantum dot regimes are found. These results show that overarching solutions for the tuning of quantum devices are enabled by machine learning.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: Reino Unido Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: Reino Unido Pais de publicación: Reino Unido