% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Kumar:910701,
      author       = {Kumar, Suhas and Wang, Xinxin and Strachan, John Paul and
                      Yang, Yuchao and Lu, Wei D.},
      title        = {{D}ynamical memristors for higher-complexity neuromorphic
                      computing},
      journal      = {Nature reviews},
      volume       = {7},
      number       = {7},
      issn         = {2058-8437},
      address      = {Basingstoke},
      publisher    = {Nature Publishing Group},
      reportid     = {FZJ-2022-04073},
      pages        = {575 - 591},
      year         = {2022},
      abstract     = {Research on electronic devices and materials is currently
                      driven by both the slowing down of transistor scaling and
                      the exponential growth of computing needs, which make
                      present digital computing increasingly capacity-limited and
                      power-limited. A promising alternative approach consists in
                      performing computing based on intrinsic device dynamics,
                      such that each device functionally replaces elaborate
                      digital circuits, leading to adaptive ‘complex
                      computing’. Memristors are a class of devices that
                      naturally embody higher-order dynamics through their
                      internal electrophysical processes. In this Review, we
                      discuss how novel material properties enable complex
                      dynamics and define different orders of complexity in
                      memristor devices and systems. These native complex dynamics
                      at the device level enable new computing architectures, such
                      as brain-inspired neuromorphic systems, which offer both
                      high energy efficiency and high computing capacity.},
      cin          = {PGI-14},
      ddc          = {600},
      cid          = {I:(DE-Juel1)PGI-14-20210412},
      pnm          = {5234 - Emerging NC Architectures (POF4-523)},
      pid          = {G:(DE-HGF)POF4-5234},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000783161600001},
      doi          = {10.1038/s41578-022-00434-z},
      url          = {https://juser.fz-juelich.de/record/910701},
}