Seminarium Advanced Methods of Semiconductor Research – Tuesday 26th of May 2026
We cordially invite you to Advanced Methods of Semiconductor Research Seminar on Tuesday 26th of May 2026 at 13:15 in room 321, building A-1, where there will be delivered a lecture:
A Topological Insulator Field Effect Memristor
by Dr Fabian Hartmann
from Chair for Applied Physics, University of Würzburg
The lecture abstract is attached below.
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Overcoming the limitations of conventional computing and the von Neumann architecture requires new computational paradigms capable of solving complex problems efficiently and within realistic time scales. Quantum and neuromorphic computing rely on unconventional materials and device functionalities, yet achieving resilience to imperfections and reliable operation remains a major challenge [1,2]. This has motivated growing interest in topological materials [3] that provide robust and low-power operation while pre-serving coherence. However, integrating topological transport with memory functionality into a reconfigurable device has remained elusive. Here, we present a topological field-effect memristor in which topo-logical protection preserves edge state coherence, enabling the coexistence of coherent transport and non-volatile memory functionality. Utilizing inverted InAs/GaInSb/InAs trilayer quantum wells [4,5], we realize a quantum spin Hall insulator that can be reconfigured between the conventional field-effect transistor operation into memristive functionality. Unlike other memristor implementations, one resistance state is entirely governed by dissipationless, coherent transport through topologically protected helical edge channels. Our results establish a prototypical topological electronic device that merges coherent transport and neuromorphic principles within a single platform, paving the way for hybrid quantum-neuromorphic architectures and expanding the functional landscape of topological electronics.
References:
[1] Campbell, E. T., Terhal, B. M. & Vuillot, Nature 549, 172–179 (2017).
[2] Kudithipudi, D. et al. Neuromorphic computing at scale. Nature 637, 801–812 (2025).
[3] Kane, C. L. & Mele, E. J. Phys. Rev. Lett. 95, 146802 (2005).
[4] Krishtopenko, S. S. & Teppe, F. Sci. Adv. 4, eaap7529 (2018).
[5] Meyer, M. et al. Sci. Adv. 11, eadz2408 (2025)