Metal Halide Perovskite Memlumors

Alexandr Marunchenko^a, Jitendra Kumar^a, Shraddha M. Rao^a, Alexander Kiligaridis^a, Dmitry Tatarinov^b, Anatoly Pushkarev^b, Yana Vaynzof^c^,^d, Ivan Scheblykin^a

^aChemical Physics and NanoLund, Lund University, P.O. Box 124, 22100 Lund, Sweden

^bSchool of Physics and Engineering, ITMO University, 49 Kronverksky, St. Petersburg 197101, Russian Federation

^cChair for Emerging Electronic Technologies, Technical University of Dresden, Nöthnitzer Str. 61, 01187 Dresden, Germany

^dLeibniz-Institute for Solid State and Materials Research Dresden, Helmholtzstraße 20, 01069 Dresden, Germany

Proceedings of Emerging Light Emitting Materials 2024 (EMLEM24)

La Canea, Greece, 2024 October 16th - 18th

Organizers: Grigorios Itskos, Sohee Jeong and Jacky Even

Oral, Alexandr Marunchenko, presentation 015
DOI: https://doi.org/10.29363/nanoge.emlem.2024.015
Publication date: 13th July 2024

Neuromorphic computing holds the potential to revolutionize traditional computing paradigms, shifting away from the von Neumann architecture towards dynamic, energy-efficient problem-solving. In this talk, I will discuss a new element of photonic neuromorphic computing: Memlumor. It essentially represents a luminescent material with memory. I will show that the metal halide perovskite material class can be used as very efficient Memlumors. The widely considered instability of the physicochemical properties of metal halide perovskites is essential for the operation of Memlumors. To reveal the memory of Memlumors' luminescence, I will additionally present the advanced multi-pulse time-correlated single-photon counting technique. By using this method, I will show the presence of memory in perovskite luminescence over a very wide range of times (from nanoseconds to minutes). The native memory of perovskites, which allows for performing computing operations at the femtojoule energy scale, will revolutionize our perception of luminescence in materials. Further study of memory in the luminescence of different perovskites is essential for making all-perovskite photonic neuromorphic computing processors.

References:

[1] Marunchenko, A., Kumar, J., Kiligaridis, A., Tatarinov, D., Pushkarev, A., Vaynzof, Y., & Scheblykin, I. G. (2024). Memlumor: A Luminescent Memory Device for Energy-Efficient Photonic Neuromorphic Computing. ACS Energy Letters, 9(5), 2075-2082.

[2] Marunchenko, A., Kumar, J., Kiligaridis, A., Rao, S. M., Tatarinov, D., Matchenya, I., Sapozhnikova, L., Ran, J., Telshow, O., Yulin, A., Pushkarev A., Vaynzof, Y., & Scheblykin, I. G. (2024). Charge Trapping and Defect Dynamics as Origin of Memory Effects in Metal Halide Perovskite Memlumors. The Journal of Physical Chemistry Letters, 15, 6256-6265.

Acknowledgements:

The work was supported Swedish Research Council (Grant2020-03530), Crafoord Foundation (Grant 20230552), andNanoLund (Grant 12-2023) and Wenner-GrenFoundation for the postdoctoral scholarship (GrantUPD2022-0132). This project has also received funding from the European ResearchCouncil (ERC) under the European Union’s Horizon 2020research and Innovation program (ERC Grant Agreement714067, ENERGYMAPS) and the Deutsche Forschungsge-meinschaft (DFG) in the framework of the Special PriorityProgram (SPP 2196) Project PERFECT PVs (Grant424216076) and the DFG for the generous support within the framework of the GRK 2767 (Project A7)

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