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

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number070904
JournalAPL Materials
Issue number7
StatePublished - Jul 1 2022

ASJC Scopus subject areas

  • General Materials Science
  • General Engineering


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