神经形态工程学
光子学
计算机科学
冯·诺依曼建筑
发光体
高效能源利用
记忆电阻器
材料科学
纳米技术
光电子学
电子工程
发光
人工智能
工程类
人工神经网络
电气工程
操作系统
作者
Alexandr Marunchenko,Jitendra Kumar,Alexander Kiligaridis,Dmitry Tatarinov,Anatoly P. Pushkarev,Yana Vaynzof,Ivan G. Scheblykin
出处
期刊:ACS energy letters
[American Chemical Society]
日期:2024-04-09
卷期号:9 (5): 2075-2082
被引量:21
标识
DOI:10.1021/acsenergylett.4c00691
摘要
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.
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