Head of the Research Group: Prof. György CSABA

Members of the Research Group: Levente MAUCHA, Dr. Ádám PAPP

Contact: csaba.gyorgy@itk.ppke.hu

For decades, progress came from making transistors smaller — but that road is running out, just as artificial intelligence is demanding more computing power than ever before. The way forward may not lie in better chips, but in better physics. Our lab works exactly at this frontier, where physics, computing, and AI meet: we ask how the natural dynamics of magnetic and electronic systems can become computation, rather than merely running it . Our focus is physics-inspired computing, where realizing a complex physical process directly yields the result of a computation. We pursue this idea on three fronts: analog circuits, oscillatory neural networks, and magnonic devices. Oscillatory neural networks (ONNs) encode information not in voltage levels but in the phase relationships between coupled oscillators . In magnonic devices, wave interference performs complex computations.

Our main instrument is a unique TR-MOKE setup (Time-Resolved Magneto-Optical Kerr Effect microscope) that measures the dynamic behavior of nanomagnets and spin waves at high frequencies, down to the picosecond and nanometer scales. We can perform a wide range of optical and high-frequency electrical measurements, allowing us to characterize both the physical properties of our samples and their performance as computing elements.

Fabrication and measurement of a magnonic computing system — as done in collaboration with our colleagues at TU Munich. Kiechle, M., Papp, Á., Mendisch, S., Ahrens, V., Golibrzuch, M., Bernstein, G. H., Porod, W., Csaba, Gy. and Becherer, M. (2023). “Spin-Wave Optics in YIG Realized by Ion-Beam Irradiation.” Small 19, no. 21: 2207293.

 

Future research directions, collaboration opportunities: participation in EU projects, collaborations with TU Munich and other leading German, French, and Italian universities.

Key publications

  • Greil, , Angotti, A., Kohl, F., Papp, Á., Wagner, M., Cocconcelli, M., Del Giacco, A. et al. (2026). Microscaled Tunable Magnonic RF Phase Shifters. ARXIV PREPRINT ARXIV, 2606.14280.
  • Rudner, , Porod, W. and Csaba, Gy. (2024). Design of oscillatory neural networks by machine learning. FRONTIERS IN NEUROSCIENCE, 18: 1307525.
  • Papp, Á., Porod, W. and Csaba, Gy. (2021). Nanoscale neural network using non-linear spin-wave interference. NATURE COMMUNICATIONS 12(1): 6422.