% 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:1050578,
      author       = {Yang, Ming-Jay and Yu, Zhenming and Pedretti, Giacomo and
                      Neftci, Emre and Strachan, John Paul},
      title        = {{I}mproved {M}emristor {C}ontrol using {D}evice {P}hysics
                      and {D}eep {R}einforcement {L}earning},
      publisher    = {IEEE},
      reportid     = {FZJ-2026-00335},
      pages        = {1-4},
      year         = {2025},
      abstract     = {The steep non-linearity of memristive switching dynamics,
                      combined with variations and asymmetry, can pose challenges
                      in the accurate control of analog conductance updates. We
                      present an effective control framework, "Probe-then-Program
                      (PtP)", where the underlying physics of memristors is
                      leveraged to fit a statistically accurate model from
                      measured data, aiding the training of a programming agent.
                      During the probe phase, the parameters’ statistical
                      distributions in a physics-based memristor model are
                      inferred using sequential Bayesian inference in measured
                      data. This model then supports the training of a
                      reinforcement neural network (Proximal Policy Optimization,
                      PPO) that generates optimized write pulses. In the
                      programming phase, the optimized pulses are applied to
                      ensembles of devices, achieving significantly shorter tuning
                      sequences for multi-level conductance programming compared
                      to conventional write-and-verify technique. We
                      experimentally demonstrate the full pipeline and show the
                      efficacy by performing conductance mapping on a 4k
                      memristive crossbar array. Improved control over nanoscale
                      memristive devices in crossbar arrays supports many
                      in-memory computing applications, while highlighting broader
                      opportunities to integrate physics-based models and machine
                      learning techniques.},
      month         = {Apr},
      date          = {2025-04-28},
      organization  = {2025 IEEE 7th International Conference
                       on Artificial Intelligence Circuits and
                       Systems (AICAS), Bordeaux (France), 28
                       Apr 2025 - 30 Apr 2025},
      cin          = {PGI-14},
      cid          = {I:(DE-Juel1)PGI-14-20210412},
      pnm          = {5234 - Emerging NC Architectures (POF4-523)},
      pid          = {G:(DE-HGF)POF4-5234},
      typ          = {PUB:(DE-HGF)8},
      doi          = {10.1109/AICAS64808.2025.11173161},
      url          = {https://juser.fz-juelich.de/record/1050578},
}