Quantum materials for energy-efficient neuromorphic computing: Opportunities and challenges
Axel Hoffmann,
Shriram Ramanathan,
Julie Grollier,
Andrew D. Kent,
Marcelo J. Rozenberg,
Ivan K. Schuller,
Oleg G. Shpyrko,
Robert C. Dynes,
Yeshaiahu Fainman,
Alex Frano,
Eric E. Fullerton,
Giulia Galli,
Vitaliy Lomakin,
Shyue Ping Ong,
Amanda K. Petford-Long,
Jonathan A. Schuller,
Mark D. Stiles,
Yayoi Takamura,
Yimei Zhu
Affiliations
Axel Hoffmann
Materials Research Laboratory and Department of Materials Science and Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, USA
Shriram Ramanathan
School of Materials Engineering, Purdue University, West Lafayette, Indiana 47907, USA
Julie Grollier
Unité Mixte de Physique CNRS/Thales, Université Paris-Saclay, 91767 Palaiseau, France
Andrew D. Kent
Center for Quantum Phenomena, Department of Physics, New York University, New York, New York 10003, USA
Marcelo J. Rozenberg
Université Paris-Saclay, CNRS Laboratoire de Physique des Solides, Orsay 91405, France
Ivan K. Schuller
Department of Physics, University of California–San Diego, La Jolla, California 92093, USA
Oleg G. Shpyrko
Department of Physics, University of California–San Diego, La Jolla, California 92093, USA
Robert C. Dynes
Department of Physics, University of California–San Diego, La Jolla, California 92093, USA
Yeshaiahu Fainman
Department of Electrical and Computer Engineering, University of California–San Diego, La Jolla, California 92093, USA
Alex Frano
Department of Physics, University of California–San Diego, La Jolla, California 92093, USA
Eric E. Fullerton
Department of Electrical and Computer Engineering, University of California–San Diego, La Jolla, California 92093, USA
Giulia Galli
Pritzker School of Molecular Engineering and Department of Chemistry, The University of Chicago, Chicago, Illinois 60637, USA
Vitaliy Lomakin
Department of Electrical and Computer Engineering, University of California–San Diego, La Jolla, California 92093, USA
Shyue Ping Ong
Department of NanoEngineering, University of California–San Diego, La Jolla, California 92093, USA
Amanda K. Petford-Long
Materials Science Division, Argonne National Laboratory, Lemont, Illinois 60439, USA
Jonathan A. Schuller
Department of Electrical and Computer Engineering, University of California Santa Barbara, Santa Barbara, California 93106, USA
Mark D. Stiles
Physical Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, Maryland 20899-6202, USA
Yayoi Takamura
Department of Materials Science and Engineering, University of California, Davis, Davis, California 95616, USA
Yimei Zhu
Department of Consdensed Matter Physics and Materials Science, Brookhaven National Laboratory, Upton, New York 11973, USA
Neuromorphic computing approaches become increasingly important as we address future needs for efficiently processing massive amounts of data. The unique attributes of quantum materials can help address these needs by enabling new energy-efficient device concepts that implement neuromorphic ideas at the hardware level. In particular, strong correlations give rise to highly non-linear responses, such as conductive phase transitions that can be harnessed for short- and long-term plasticity. Similarly, magnetization dynamics are strongly non-linear and can be utilized for data classification. This Perspective discusses select examples of these approaches and provides an outlook on the current opportunities and challenges for assembling quantum-material-based devices for neuromorphic functionalities into larger emergent complex network systems.
