Strictly Non-Blocking 8 × 8 Silicon Photonics Switch Operating in the O-Band

Suzuki, Keijiro; Konoike, Ryotaro; Cong, Guangwei; Yamada, Koji; Namiki, Shu; Kawashima, Hitoshi; Ikeda, Kazuhiro

doi:10.1109/jlt.2020.3024016

Cited by 11 publications

(2 citation statements)

References 17 publications

(21 reference statements)

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“…The current training time is also limited by the current setting time (2-3 ms for one channel) of our current sources, limiting onchip training for a large dataset, which can also be improved if integrating drivers for heaters 34,35 . If the driver has a >100 MHz bandwidth, the training time will be dominated by the heater response, which is about tens of microseconds (μs) (~30 μs for our heaters, which can be shortened to <10 μs 34 ).…”

Section: Discussionmentioning

confidence: 99%

“…All experiments were performed in normal lab environment without any thermal management (Supplementary Section 5.3). One reason for the chip reproducibility is due to our advanced 12 inch silicon photonic platform (AIST-SCR) on which our large-scale photonic switches were fabricated with high reproducibility 34,35 . The other reason is that the PPC device for classification application presented in this work inherently has higher robustness to device imperfections than other devices for switching applications (see Supplementary Section 5.4).…”

Section: Discussionmentioning

confidence: 99%

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On-chip bacterial foraging training in silicon photonic circuits for projection-enabled nonlinear classification

et al. 2022

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On-chip training remains a challenging issue for photonic devices to implement machine learning algorithms. Most demonstrations only implement inference in photonics for offline-trained neural network models. On the other hand, artificial neural networks are one of the most deployed algorithms, while other machine learning algorithms such as supporting vector machine (SVM) remain unexplored in photonics. Here, inspired by SVM, we propose to implement projection-based classification principle by constructing nonlinear mapping functions in silicon photonic circuits and experimentally demonstrate on-chip bacterial foraging training for this principle to realize single Boolean logics, combinational Boolean logics, and Iris classification with ~96.7 − 98.3 per cent accuracy. This approach can offer comparable performances to artificial neural networks for various benchmarks even with smaller scales and without leveraging traditional activation functions, showing scalability advantage. Natural-intelligence-inspired bacterial foraging offers efficient and robust on-chip training, and this work paves a way for photonic circuits to perform nonlinear classification.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%