Temporal pattern generation with tunable repetition rate using semiconductor laser periodic dynamics

In this work, a long-cavity semiconductor laser subject to optical feedback is exploited to generate repetitive temporal patterns with enhanced intra-pattern sample diversity. Stable limit cycle dynamics characterized by multiple frequency harmonics are experimentally demonstrated by considering a 2 m external cavity with appropriate optical feedback conditions. This simple configuration can enable stable and continuous generation of periodic waveforms with a tunable repetition rate ranging from 3.28 to 4.21 GHz. Distinct, highly consistent temporal patterns are obtained by applying high-pass filtering to adjust the relative power of the harmonics. The resulting system operates as a physical waveform generator capable of producing diverse and repeatable signal patterns, making it well-suited for use in the masking stage of time delay reservoir computing architectures.

Publication Details

Journal
arXiv (Cornell University)
Published
2025-06-23
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
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article

Temporal pattern generation with tunable repetition rate using semiconductor laser periodic dynamics

arXiv (Cornell University)
Neural Networks and Reservoir Computing
article

Temporal pattern generation with tunable repetition rate using semiconductor laser periodic dynamics

article en

Abstract

In this work, a long-cavity semiconductor laser subject to optical feedback is exploited to generate repetitive temporal patterns with enhanced intra-pattern sample diversity. Stable limit cycle dynamics characterized by multiple frequency harmonics are experimentally demonstrated by considering a 2 m external cavity with appropriate optical feedback conditions. This simple configuration can enable stable and continuous generation of periodic waveforms with a tunable repetition rate ranging from 3.28 to 4.21 GHz. Distinct, highly consistent temporal patterns are obtained by applying high-pass filtering to adjust the relative power of the harmonics. The resulting system operates as a physical waveform generator capable of producing diverse and repeatable signal patterns, making it well-suited for use in the masking stage of time delay reservoir computing architectures.

arXiv (Cornell University)
Openalex Percentile: Top 73%
Neural Networks and Reservoir Computing
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