Photonic independent component analysis using an on-chip microring weight bank
Photonic independent component analysis using an on-chip microring weight bank
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DOI：
10.1364/oe.383603
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发表时间：
2020-01-20
期刊：
影响因子：
3.8
通讯作者：
Prucnal, Paul R.
中科院分区：
文献类型：
--
作者：
Ma, Philip Y.;Tait, Alexander N.;Prucnal, Paul R.
独立成分分析（伊卡）是一种分析多维数据的通用技术，用于揭示最大程度上相互独立的潜在隐藏因素。我们报告的第一个光子伊卡的混合物的未知信号，采用片上麦克风（MRR）的重量银行。MRR权重组执行所谓的加权加法（即，乘法-累加）运算，并输出感兴趣信号的单个降维表示。我们提出了一种新的伊卡算法，恢复独立分量的加权加法输出的统计信息的基础上，同时保持盲的原始源，但也混合的波形信息。我们研究了通道可分性和远近问题，我们的双通道光子伊卡实验表明，我们的方案具有与传统的基于软件的伊卡方法相当的性能。我们的数值模拟验证了所提出的方法的保真度，并研究噪声的影响，以确定我们的方法的操作制度。所提出的技术可以为未来的盲源分离，微波光子学和片上信息处理的研究开辟新的领域。(C)根据OSA开放获取出版协议的条款，2020年美国光学学会
Independent component analysis (ICA) is a general-purpose technique for analyzing multi-dimensional data to reveal the underlying hidden factors that are maximally independent from each other. We report the first photonic ICA on mixtures of unknown signals by employing an on-chip microring (MRR) weight bank. The MRR weight bank performs so-called weighted addition (i.e., multiply-accumulate) operations on the received mixtures, and outputs a single reduced-dimensional representation of the signal of interest. We propose a novel ICA algorithm to recover independent components solely based on the statistical information of the weighted addition output, while remaining blind to not only the original sources but also the waveform information of the mixtures. We investigate both channel separability and near-far problems, and our two-channel photonic ICA experiment demonstrates our scheme holds comparable performance with the conventional software-based ICA method. Our numerical simulation validates the fidelity of the proposed approach, and studies noise effects to identify the operating regime of our method. The proposed technique could open new domains for future research in blind source separation, microwave photonics, and on-chip information processing. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement