4.6 Article

All-optical microwave waveform transformation based on photonic temporal processors

Journal

OPTICS EXPRESS
Volume 30, Issue 7, Pages 10428-10442

Publisher

Optica Publishing Group
DOI: 10.1364/OE.455500

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Funding

  1. NationalNatural Science Foundation of China [61835003, 62105076]
  2. High Level Innovation Talent Program of Guizhou Province, China [2015-4010]
  3. Platform and Talent Program of Guizhou Province, China [2018-5781-1]
  4. Guizhou Provincial Science and Technology Projects [ZK-2021-327]

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An all-photonic approach for generating and transforming microwave waveforms is proposed and experimentally demonstrated. Differentiators and a multiplier are used to transform an initial triangular waveform into square waveform and sawtooth (or reversed-sawtooth) waveform. Parabolic pulses are achieved and further transformed into sawtooth (or reversed-sawtooth) waveform. The feasibility of the system is verified through theoretical analysis and simulation, and the experimental results agree well with the theoretical analysis. This scheme provides a novel access to implement all-optical microwave waveforms generation, transformation, signal processing, and computing.
An all-photonic approach of microwave waveforms generation and transformation is proposed and experimentally demonstrated. From the perspective of envelope function operation in time domain, an initial triangular waveform is transformed into square waveform and sawtooth (or reversed-sawtooth) waveform via two types of differentiators, respectively. In addition, by using a SOA as a multiplier, both bright and dark parabolic pulses are achieved, which are further transformed into sawtooth (or reversed-sawtooth) waveform by taking the first derivative operation. The feasibility of the system is verified by theoretical analysis and simulation. In experiment, all of the expected results are successfully demonstrated and agree with the theoretical analysis well. This scheme provides a novel access to implement all-optical microwave waveforms generation, transformation, signal processing and computing. (C) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

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