Atreya Majumdar

Atreya Majumdar

Position Junior Research Leader
Email atreya.majumdar@uni-due.de
Phone 0203-379-4717
Office MG 384
Address Twist Group, Faculty of Physics
University of Duisburg-Essen
Campus Duisburg
Lotharstraße 1
D 47057 Duisburg
Contact ORCID-ID: 0000-0002-6547-2231
Google Scholar Profile

Dr. Atreya Majumdar

Fakultät für Physik, Theoretische Physik

Address
Lotharstraße 1
47057 Duisburg
Room
MG 384
Phone
+49 203 37 94717
E-Mail
atreya.majumdar@uni-due.de

Functions

  • Wissenschaftliche/r Mitarbeiter/in, Arbeitsgruppe Prof. Everschor-Sitte

The following publications are listed in the online university bibliography of the University of Duisburg-Essen. Further information may also be found on the person's personal web pages.

    Journal articles

  • Ezzadeen, Mona; Majumdar, Atreya; Valorge, Olivier; Castellani, Niccolo; Gherman, Valentin; Regis, Guillaume; Giraud, Bastien; Noel, Jean-Philippe; Meli, Valentina; Bocquet, Marc; Andrieu, Francois; Querlioz, Damien; Portal, Jean-Michel
    Implementation of binarized neural networks immune to device variation and voltage drop employing resistive random access memory bridges and capacitive neurons
    In: Communications Engineering Vol. 3 (2024) 80
    Online Full Text: dx.doi.org/ (Open Access)
  • Jebali, Fadi; Majumdar, Atreya; Turck, Clément; Harabi, Kamel-Eddine; Faye, Mathieu-Coumba; Muhr, Eloi; Walder, Jean-Pierre; Bilousov, Oleksandr; Michaud, Amadéo; Vianello, Elisa; Hirtzlin, Tifenn; Andrieu, François; Bocquet, Marc; Collin, Stéphane; Querlioz, Damien; Portal, Jean-Michel
    Powering AI at the edge: A robust, memristor-based binarized neural network with near-memory computing and miniaturized solar cell
    In: Nature Communications Vol. 15 (2024) 741
    Online Full Text: dx.doi.org/ (Open Access)
  • Everschor-Sitte, Karin; Majumdar, Atreya; Wolk, Katharina; Meier, Dennis
    Topological magnetic and ferroelectric systems for reservoir computing
    In: Nature Reviews Physics Vol. 6 (2024) Nr. 7, pp. 455 - 462
    Online Full Text: dx.doi.org/
  • Bonnet, Djohan; Hirtzlin, Tifenn; Majumdar, Atreya; Dalgaty, Thomas; Esmanhotto, Eduardo; Meli, Valentina; Castellani, Niccolo; Martin, Simon; Nodin, Jean-François; Bourgeois, Guillaume; Portal, Jean-Michel; Querlioz, Damien; Vianello, Elisa
    Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks
    In: Nature Communications Vol. 14 (2023) Nr. 1, 7530
    Online Full Text: dx.doi.org/ (Open Access)
  • Majumdar, Atreya; Bocquet, Marc; Hirtzlin, Tifenn; Laborieux, Axel; Klein, Jacques-Olivier; Nowak, Etienne; Vianello, Elisa; Portal, Jean-Michel; Querlioz, Damien
    Model of the Weak Reset Process in HfOₓ Resistive Memory for Deep Learning Frameworks
    In: IEEE Transactions on Electron Devices (T-ED) Vol. 68 (2021) Nr. 10, pp. 4925 - 4932
    Online Full Text: dx.doi.org/ (Open Access)
  • Majumdar, Atreya; Jansen, Thomas L. C.
    Quantum-Classical Simulation of Molecular Motors Driven only by Light
    In: The Journal of Physical Chemistry Letters Vol. 12 (2021) Nr. 23, pp. 5512 - 5518
    Online Full Text: dx.doi.org/ (Open Access)
  • Das, Arijit; Jousma, S.T.; Majumdar, Atreya; Banerjee, T.
    Evolution of the magnetoresistance lineshape with temperature and electric field across Nb-doped SrTiO₃ interface
    In: Applied Physics Letters (APL) Vol. 112 (2018) Nr. 18, 182405
    Online Full Text: dx.doi.org/ Online Full Text (Open Access)

    • Book articles / Proceedings papers

  • Ezzadeen, Mona; Majumdar, Atreya; Thomas, Sigrid; Noël, Jean-Philippe; Giraud, Bastien; Bocquet, Marc; Andrieu, Francois; Querlioz, Damien; Portal, Jean-Michel
    Binary ReRAM-based BNN first-layer implementation
    In: 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE): Proceedings / Design, Automation & Test in Europe Conference & Exhibition (DATE), 17-19 April 2023, Antwerp, Belgium / Institute of Electrical and Electronics Engineers (Eds.) 2023
    Online Full Text: dx.doi.org/ Online Full Text (Open Access)
  • Jebali, F.; Majumdar, Atreya; Laborieux, A.; Hirtzlin, T.; Vianello, E.; Walder, J.P.; Bocquet, M.; Querlioz, D.; Portal, J.M.
    CAPC : A Configurable Analog Pop-Count Circuit for Near-Memory Binary Neural Networks
    In: 2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS): on-line proceedings / IEEE International Midwest Symposium on Circuits and Systems (MWSCAS), August 9 – 11, 2021, East Lansing, Michigan, USA, online / Institute of Electrical and Electronics Engineers (Eds.) 2021, pp. 158 - 161
    Online Full Text: dx.doi.org/ Online Full Text (Open Access)
  • Ezzadeen, M.; Majumdar, Atreya; Bocquet, M.; Giraud, B.; Noel, J.-P.; Andrieu, F.; Querlioz, D.; Portal, J.-M.
    Low-Overhead Implementation of Binarized Neural Networks Employing Robust 2T2R Resistive RAM Bridges
    In: ESSCIRC 2021: IEEE 47th European Solid State Circuits Conference (ESSCIRC) ; on-line proceedings / IEEE 47th European Solid State Circuits Conference (ESSCIRC), September 6 – 9, 2021, online / Institute of Electrical and Electronics Engineers (Eds.) 2021, pp. 83 - 86
    Online Full Text: dx.doi.org/ Online Full Text (Open Access)

    • Thesis

  • Majumdar, Atreya
    Embracing Imperfections : Hardware-compatible Neural Networks for Neuromorphic Computing
    Paris (2023) XII, 179 Seiten
    Online Full Text

Advancements in the 21st century are driven by innovations in both materials science and artificial intelligence, fueling progress across technology and industry. My research work lies at the intersection of these fields. I explore in-materio unconventional computing, specifically leveraging the unique physics of nanomagnetic systems (see Figure).

 

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Everschor-Sitte, K., Majumdar, A., Wolk, K. et al. Topological magnetic and ferroelectric systems for reservoir computing. Nat Rev Phys 6, 455–462 (2024).
​​

Additionally, I apply machine learning to investigate and optimize material properties, aiming to harness these functionalities for practical applications. Ultimately, my goal is to push forward our understanding of materials and contribute to more efficient, sustainable computing technologies.