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Supervised learning and pattern recognition in photonic spiking neural networks based on MRR and phase-change materials

Optics Communications, ISSN: 0030-4018, Vol: 549, Page: 129870
2023
  • 5
    Citations
  • 0
    Usage
  • 7
    Captures
  • 1
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    5
  • Captures
    7
  • Mentions
    1
    • News Mentions
      1
      • 1

Most Recent News

Reports from Xidian University Add New Data to Findings in Phase Change Materials (Supervised Learning and Pattern Recognition In Photonic Spiking Neural Networks Based On Mrr and Phase-change Materials)

2023 DEC 18 (NewsRx) -- By a News Reporter-Staff News Editor at Network Daily News -- Fresh data on Nanotechnology - Phase Change Materials are

Article Description

Traditional computers are limited by the separation of memory and processor units, is difficult to achieve fast, efficient, and low-power computing. While photonic spiking neural networks (SNNs) can overcome these shortcomings, they encounter limitations in large-scale integration. Silicon photonics platform, compatible with mature Complementary Metal Oxide Semiconductor (CMOS) platforms, is a promising candidate for realizing large-scale photonic SNNs. In this work, we proposed an integrated photonic SNN by exploiting the photonic properties of phase-change material (PCM) Ge 2 Sb 2 Te 5 (GST) and micro-ring resonators (MRR), and demonstrated its integrate-and-fire (IF) behavior. Based on a system-level behavioral model, we adopt an improved Tempotron-like ReSuMe supervised learning algorithm to train the proposed photonic SNNs and complete a pattern recognition task for the clock's 12 clockwise directions. Then the influence of different noise levels is considered, and the accuracy is close to 1 when the noise level is less than 0.2. We propose a photonic implementation of such an SNN system, and use wavelength division multiplexing to achieve a scalable architecture for the pattern recognition task. The collaborative design and optimization of hardware architecture and algorithm are realized, providing a theoretical basis for the realization of photonic SNN based on MRRs and PCM.

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