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Deep-learning-powered photonic analog-to-digital conversion.

Shaofu Xu1, Xiuting Zou1, Bowen Ma1

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A novel deep-learning photonic analog-to-digital converter (ADC) architecture merges electronics and photonics. This advanced ADC overcomes speed, bandwidth, and accuracy tradeoffs for next-generation information systems.

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Area of Science:

  • Photonics and Electronics
  • Artificial Intelligence
  • Signal Processing

Background:

  • Modern information systems demand high-speed, broadband, and accurate analog-to-digital converters (ADCs).
  • Photonic technologies offer potential for meeting these advanced ADC requirements.
  • Existing ADCs face tradeoffs between speed, bandwidth, and accuracy.

Purpose of the Study:

  • To present a deep-learning-powered photonic ADC architecture.
  • To overcome the inherent bottlenecks of purely electronic or photonic ADCs.
  • To enhance the tradeoff among speed, bandwidth, and accuracy in ADCs.

Main Methods:

  • Developed a hybrid photonic-electronic ADC architecture.
  • Employed deep neural networks (DNNs) for supervised training.
  • DNNs were trained to identify and correct photonic system defects in real-time.

Main Results:

  • The proposed architecture successfully integrates photonic and electronic advantages.
  • Deep learning effectively recovers distorted data caused by photonic defects.
  • Demonstrated superior performance compared to state-of-the-art ADCs in numerical and experimental tests.

Conclusions:

  • The deep-learning photonic ADC architecture offers a viable solution to ADC performance limitations.
  • This approach provides a pathway for developing high-throughput, high-performance ADCs.
  • The architecture is expected to drive advancements in future information systems.