ArticleACS energy letters2025
Classification of 5‑bit Binary Light Pulse Sequences Using Photoluminescence of Metal Halide Perovskite Memlumors.
Article in ACS energy letters, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Physical Implementation of Optical Material-Based Neural Networks Processing Enabled by Long-Persistent Luminescence.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Excited State Opto-Ionic Reservoir Computing in Hybrid Perovskite Electrochemically-Gated Luminescent Cells.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Short-Term Bienenstock-Cooper-Munro Learning in Optoelectrically-Driven Flexible Halide Perovskite Single Crystal Memristors.Small methods · 2025Article
- Extreme Electron-Photon Interaction in Disordered Perovskites.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A memlumor is an innovative neuromorphic luminescent device with a state-dependent photoluminescence quantum yield (PLQY) designed for optical neuromorphic computing applications. Metal halide perovskite memlumors leverage charge trapping and photodoping to modulate the PLQY, making it dependent on the excitation light history. Here, we demonstrate the ability of perovskite memlumors to classify time-dependent binary optical signals on sub-microsecond timescales. Sequences of laser pulses (representing ones) and gaps (absence of pulses representing zeros) were used to excite photoluminescence in MAPbBr
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