神经形态工程学
材料科学
光子学
计算机科学
巨量平行
响应度
生物神经网络
信号处理
油藏计算
光电子学
人工神经网络
纳米技术
人工智能
计算机硬件
循环神经网络
光电探测器
数字信号处理
机器学习
并行计算
作者
Artur Bednarkiewicz,Marcin Szalkowski,Martyna Majak,Zuzanna Korczak,Małgorzata Misiak,Sebastian Maćkowski
标识
DOI:10.1002/adma.202304390
摘要
Data processing and storage in electronic devices are typically performed as a sequence of elementary binary operations. Alternative approaches, such as neuromorphic or reservoir computing, are rapidly gaining interest where data processing is relatively slow, but can be performed in a more comprehensive way or massively in parallel, like in neuronal circuits. Here, time-domain all-optical information processing capabilities of photon-avalanching (PA) nanoparticles at room temperature are discovered. Demonstrated functionality resembles properties found in neuronal synapses, such as: paired-pulse facilitation and short-term internal memory, in situ plasticity, multiple inputs processing, and all-or-nothing threshold response. The PA-memory-like behavior shows capability of machine-learning-algorithm-free feature extraction and further recognition of 2D patterns with simple 2 input artificial neural network. Additionally, high nonlinearity of luminescence intensity in response to photoexcitation mimics and enhances spike-timing-dependent plasticity that is coherent in nature with the way a sound source is localized in animal neuronal circuits. Not only are yet unexplored fundamental properties of photon-avalanche luminescence kinetics studied, but this approach, combined with recent achievements in photonics, light confinement and guiding, promises all-optical data processing, control, adaptive responsivity, and storage on photonic chips.
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