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基于光子雪崩纳米晶光子突触的全光数据处理

All-Optical Data Processing with Photon-Avalanching Nanocrystalline Photonic Synapse.

作者信息

Bednarkiewicz Artur, Szalkowski Marcin, Majak Martyna, Korczak Zuzanna, Misiak Małgorzata, Maćkowski Sebastian

机构信息

Institute of Low Temperature and Structure Research, Polish Academy of Sciences, ul. Okólna 2, Wroclaw, 50-422, Poland.

Nanophotonics Group, Institute of Physics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University in Toruń, 87-100, Toruń, ul. Grudziądzka 5, Poland.

出版信息

Adv Mater. 2023 Oct;35(42):e2304390. doi: 10.1002/adma.202304390. Epub 2023 Sep 17.

Abstract

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.

摘要

电子设备中的数据处理和存储通常作为一系列基本二进制操作来执行。诸如神经形态计算或储层计算等替代方法正迅速受到关注,在这些方法中,数据处理相对较慢,但可以以更全面的方式或大规模并行执行,就像在神经元回路中那样。在此,发现了光子雪崩(PA)纳米颗粒在室温下的时域全光信息处理能力。所展示的功能类似于在神经元突触中发现的特性,例如:双脉冲易化和短期内在记忆、原位可塑性、多输入处理以及全或无阈值响应。类似PA记忆的行为展示了无需机器学习算法的特征提取能力,以及使用简单的双输入人工神经网络对二维图案进行进一步识别的能力。此外,发光强度对光激发的高非线性响应模拟并增强了与动物神经元回路中声源定位方式本质上一致的尖峰时间依赖性可塑性。不仅研究了光子雪崩发光动力学尚未探索的基本特性,而且这种方法与光子学、光限制和引导方面的最新成果相结合,有望实现光子芯片上的全光数据处理、控制、自适应响应和存储。

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