Controlled Formation of Conduction Channels in Memristive Devices Observed by X-ray Multimodal Imaging.

Journal: Advanced materials (Deerfield Beach, Fla.)
Published Date:

Abstract

Neuromorphic computing provides a means for achieving faster and more energy efficient computations than conventional digital computers for artificial intelligence (AI). However, its current accuracy is generally less than the dominant software-based AI. The key to improving accuracy is to reduce the intrinsic randomness of memristive devices, emulating synapses in the brain for neuromorphic computing. Here using a planar device as a model system, the controlled formation of conduction channels is achieved with high oxygen vacancy concentrations through the design of sharp protrusions in the electrode gap, as observed by X-ray multimodal imaging of both oxygen stoichiometry and crystallinity. Classical molecular dynamics simulations confirm that the controlled formation of conduction channels arises from confinement of the electric field, yielding a reproducible spatial distribution of oxygen vacancies across switching cycles. This work demonstrates an effective route to control the otherwise random electroforming process by electrode design, facilitating the development of more accurate memristive devices for neuromorphic computing.

Authors

  • Huajun Liu
    Materials Science Division, Argonne National Laboratory, Lemont, IL, 60439, USA.
  • Yongqi Dong
    Materials Science Division, Argonne National Laboratory, Lemont, IL, 60439, USA.
  • Mirza Galib
    Department of Mechanical Engineering, University of Louisville, Louisville, KY, 40208, USA.
  • Zhonghou Cai
    X-ray Science Division, Advanced Photon Source, Argonne National Laboratory, Lemont, IL, 60439, USA.
  • Liliana Stan
    Center for Nanoscale Materials, Nanoscience and Technology Division, Argonne National Laboratory, Lemont, IL, 60439, USA.
  • Lei Zhang
    Division of Gastroenterology, Union Hospital, Tongji Medical College Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Ady Suwardi
    Institute of Materials Research and Engineering, A*STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore.
  • Jing Wu
    School of Pharmaceutical Science, Jiangnan University, Wuxi, 214122, Jiangsu, China.
  • Jing Cao
    Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, Zhejiang, People's Republic of China.
  • Chee Kiang Ivan Tan
    Institute of Materials Research and Engineering, A*STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Singapore, 138634, Singapore.
  • Subramanian K R S Sankaranarayanan
    Center for Nanoscale Materials, Argonne National Laboratory, Lemont, Illinois 60439, United States.
  • Badri Narayanan
    Department of Mechanical Engineering, University of Louisville, Louisville, KY, 40208, USA.
  • Hua Zhou
    Department of Biostatistics, UCLA.
  • Dillon D Fong
    Materials Science Division, Argonne National Laboratory, Lemont, IL, 60439, USA.