Memlumor: A Luminescent Memory Device for Energy-Efficient Photonic Neuromorphic Computing

Alexandr Marunchenko, Jitendra Kumar, Alexander Kiligaridis, Dmitry Tatarinov, Anatoly Pushkarev, Yana Vaynzof, Ivan G. Scheblykin

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskriftPeer review


Neuromorphic computing promises to transform the current paradigm of traditional computing toward non-von Neumann dynamic energy-efficient problem solving. To realize this, a neuromorphic platform must possess intrinsic complexity reflected in the built-in diversity of its physical operation mechanisms. We propose and demonstrate the concept of a memlumor, an all-photonic device combining memory and a luminophore, and being mathematically a full equivalence of the electrically driven memristor. Using CsPbBr3 perovskites as a material platform, we demonstrate the synergetic coexistence of memory effects within a broad time scale from nanoseconds to minutes and switching energy down to 3.5 fJ. We elucidate the origin of such a complex response to be related to the phenomena of photodoping and photochemistry activated by a tunable light input. When the existence of a history-dependent photoluminescence quantum yield is leveraged in various material platforms, the memlumor device concept will trigger multiple new research directions in both material science and photonics.

Sidor (från-till)2075-2082
Antal sidor8
TidskriftACS Energy Letters
StatusE-pub ahead of print - 2024

Ämnesklassifikation (UKÄ)

  • Den kondenserade materiens fysik


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