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用于超快速数据处理的光子复合感知器。

A photonic complex perceptron for ultrafast data processing.

作者信息

Mancinelli Mattia, Bazzanella Davide, Bettotti Paolo, Pavesi Lorenzo

机构信息

NanoLab, Department of Physics, University of Trento, Via Sommarive 14, 38123, Trento, Italy.

出版信息

Sci Rep. 2022 Mar 10;12(1):4216. doi: 10.1038/s41598-022-08087-2.

Abstract

In photonic neural network a key building block is the perceptron. Here, we describe and demonstrate a complex-valued photonic perceptron that combines time and space multiplexing in a fully passive silicon photonics integrated circuit to process data in the optical domain. A time dependent input bit sequence is broadcasted into a few delay lines and detected by a photodiode. After detection, the phases are trained by a particle swarm algorithm to solve the given task. Since only the phases of the propagating optical modes are trained, signal attenuation in the perceptron due to amplitude modulation is avoided. The perceptron performs binary pattern recognition and few bit delayed XOR operations up to 16 Gbps (limited by the used electronics) with Bit Error Rates as low as [Formula: see text].

摘要

在光子神经网络中,一个关键构建模块是感知器。在此,我们描述并演示了一种复值光子感知器,它在全无源硅光子集成电路中结合了时间和空间复用,以在光域中处理数据。一个随时间变化的输入比特序列被广播到几条延迟线中,并由光电二极管进行检测。检测后,通过粒子群算法对相位进行训练以解决给定任务。由于仅对传播光模式的相位进行训练,因此避免了感知器中由于幅度调制导致的信号衰减。该感知器能够执行二进制模式识别以及高达16 Gbps(受所用电子设备限制)的少比特延迟异或操作,误码率低至[公式:见原文] 。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e11e/8913748/185c2ba62892/41598_2022_8087_Fig2_HTML.jpg

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