% 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”.

@INPROCEEDINGS{Yang:1021228,
      author       = {Yang, Ming-Jay and Paul Strachan, John},
      title        = {{S}tate-{S}pace {M}odeling and {T}uning of {M}emristors for
                      {N}euromorphic {C}omputing {A}pplications},
      publisher    = {ACM New York, NY, USA},
      reportid     = {FZJ-2024-00665},
      pages        = {1-8},
      year         = {2023},
      abstract     = {Analog memristive devices have the potential to merge
                      computing and memory, support local learning, reach high
                      densities, enable 3D stacking, and low energy consumption
                      for neuromorphic computing applications. Yet, integration is
                      challenged by the variability and complex nonlinear dynamics
                      involved in the tuning of memristors, which is required in
                      computing and memory applications. In this paper, we model
                      the dynamic analog switching of memristive devices with an
                      evolution-measurement state-space model. A physics-based
                      compact model is extended to capture statistical
                      distributions of the variability observed in memristors.
                      Based on metal-oxide memristors and electronic measurement
                      data, we applied Sequential-Monte Carlo (Particle Filter)
                      techniques to infer underlying memristor model parameters.
                      The result is validated by experimental data. Applying the
                      calibrated statistical model, we propose an efficient
                      adaptive pulse programming scheme, and performed a
                      comparative analysis across widely applied write-and-verify
                      techniques. We show improved programming control in the
                      metrics of error, energy, and time in reaching target
                      states.},
      month         = {Aug},
      date          = {2023-08-01},
      organization  = {ICONS '23: 2023 International
                       Conference on Neuromorphic Systems,
                       Santa Fe NM USA (USA), 1 Aug 2023 - 3
                       Aug 2023},
      cin          = {PGI-14},
      cid          = {I:(DE-Juel1)PGI-14-20210412},
      pnm          = {5233 - Memristive Materials and Devices (POF4-523) / BMBF
                      03ZU1106CB - NeuroSys: Algorithm-Hardware Co-Design (Projekt
                      C) - B (BMBF-03ZU1106CB)},
      pid          = {G:(DE-HGF)POF4-5233 / G:(DE-Juel1)BMBF-03ZU1106CB},
      typ          = {PUB:(DE-HGF)8},
      doi          = {10.1145/3589737.3605966},
      url          = {https://juser.fz-juelich.de/record/1021228},
}