Broadband physical layer cognitive radio with an integrated photonic processor for blind source separation

Zhang, Weipeng; Tait, Alexander N.; Huang, Chaoran; Lima, Thomas Ferreira de; Bilodeau, Simon; Blow, Eric C.; Jha, Aashu; Shastri, Bhavin J.; Prucnal, Paul R.

doi:10.1038/s41467-023-36814-4

Cited by 34 publications

(27 citation statements)

References 42 publications

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“…These challenges promote the research enthusiasm of optical neural networks (ONNs) 2 , 3 . This is attributed to the high bandwidth and high parallelism characteristics of light, which are manifested in the ONNs composed of Mach–Zehnder interferometers (MZIs), 4 – 6 micro-rings resonators (MRRs), 7 – 9 scattering 10 and diffraction 11 – 15 structures. It is worth noting that on-chip ONN is more competitive on portability and footprint, and even some commercial companies have been established 16 .…”

Section: Introductionmentioning

confidence: 99%

Multimode diffractive optical neural network

Sun,

Fu,

Huang

et al. 2024

Adv. Photon. Nexus

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On-chip diffractive optical neural networks (DONNs) bring the advantages of parallel processing and low energy consumption. However, an accurate representation of the optical field's evolution in the structure cannot be provided using the previous diffraction-based analysis method. Moreover, the loss caused by the open boundaries poses challenges to applications. A multimode DONN architecture based on a more precise eigenmode analysis method is proposed. We have constructed a universal library of input, output, and metaline structures utilizing this method, and realized a multimode DONN composed of the structures from the library. On the designed multimode DONNs with only one layer of the metaline, the classification task of an Iris plants dataset is verified with an accuracy of 90% on the blind test dataset, and the performance of the one-bit binary adder task is also validated. Compared to the previous architectures, the multimode DONN exhibits a more compact design and higher energy efficiency.

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Section: Introductionmentioning

confidence: 99%

Multimode diffractive optical neural network

Sun,

Fu,

Huang

et al. 2024

Adv. Photon. Nexus

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“…This simpliőcation enables its implementation in common multi-project-wafer runs. The components of our design can be tuned to generate spike rates in the gigahertz range, showcasing its potential for high-speed applications in adaptive control [23], learning [24], and cognitive processing of the radio frequency spectrum [25].…”

Section: Introductionmentioning

confidence: 99%

Proposal for a monolithic silicon-on-insulator resonator spiking neuron

Tamura,

Morison,

Tait

et al. 2024

Preprint

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Increasingly, artificial intelligent systems look to neuromorphic photonics for its speed and its low loss, high bandwidth interconnects. Silicon photonics has shown remarkable promise to enable the creation of large scale neural networks. Here, we propose a monolithic silicon opto-electronic resonator spiking neuron. It uses optical feedback from the transmission of a continuously pumped microring PN modulator to achieve excitable dynamics. It is cascadable, capable of operating at GHz speeds, and compatible with wavelength-division multiplexing schemes for linear weighting. It is a Class 2 excitable device via a subcritical Hopf bifurcation constructed from devices commonly found in many silicon photonic chip foundries.

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“…[4][5][6][7][8][9][10][11] With the good performance of light for long distances communication, integrated photonics also gets great achievements in sensing and computational operations. [12][13][14][15][16][17][18][19][20] Integrated photonics benefits from the batch microfabrication methods with the development of semiconductor fabrication. [21][22][23] Compared to silicon electronic devices, integrated photonics has three key obvious advantages.…”

Section: Introductionmentioning

confidence: 99%

Recent Progress in Silicon‐Based Photonic Integrated Circuits and Emerging Applications

Xiao,

Liu,

et al. 2023

Advanced Optical Materials

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In recent years, with the further ministration of the semiconductor device in integrated circuits, power consumption and data transmission bandwidth have become insurmountable obstacles. As an integrated technology, photonic integrated circuits (PICs) have a promising potential in the post‐Moore era with more advantages in data processing, communication, and diversified sensing applications for their ultra‐high process speed and low power consumption. Silicon photonics is believed to be an encouraging solution to realize PICs because of the mature CMOS process. The past decades have witnessed a huge growth in silicon PICs. However, there is still a demand for the development of silicon PICs to enable powerful chip‐scale systems and new functionalities. In this paper, a review of the photonic components, functional blocks, and emerging applications for PICs is offered. The common photonic components are classified into several sections, including on‐chip light sources, fiber‐to‐chip couplers, photonic resonators, waveguide‐based sensors, on‐chip photodetectors, and modulators. The functional blocks of the PICs mentioned in this review are photonic memories and photonic neural networks. Finally, the paper concludes with emerging applications for further study.

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Broadband physical layer cognitive radio with an integrated photonic processor for blind source separation

Cited by 34 publications

References 42 publications

Multimode diffractive optical neural network

Multimode diffractive optical neural network

Proposal for a monolithic silicon-on-insulator resonator spiking neuron

Recent Progress in Silicon‐Based Photonic Integrated Circuits and Emerging Applications

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