Self-Calibrated Microring Weight Function for Neuromorphic Optical Computing

This paper presents a microring resonator-based weight function for neuromorphic photonic applications achieving a record-high precision of 11.3 bits and accuracy of 9.3 bits for 2 Gbps input optical signals. The system employs an all-analog self-referenced proportional-integral-derivative (PID) controller to perform real-time temperature stabilization within a range of up to 60 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula>C. A self-calibrated weight function is demonstrated for a range of 6 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula>C with a single initial calibration and minimal accuracy and precision degradation. By monitoring the through and drop ports of the microring with variable gain transimpedance amplifiers, accurate and precise weight adjustment is achieved, ensuring optimal performance and reliability. These findings underscore the system's robustness to dynamic thermal environments, highlighting the potential for high-speed reconfigurable analog photonic networks.

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Publication Details

Journal
Journal of Lightwave Technology
Published
2024-09-17
DOI
https://doi.org/10.1109/jlt.2024.3462534
Citations
5
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
1.74
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article

Self-Calibrated Microring Weight Function for Neuromorphic Optical Computing

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5 citations
Journal of Lightwave Technology
Neural Networks and Reservoir Computing
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article

Self-Calibrated Microring Weight Function for Neuromorphic Optical Computing

Hassan Rahbardar Mojaver, D.R. Rolston, José García Echeverría, Glenn Cowan, Ataollah Mahsafar, Odile Liboiron-Ladouceur, Daniel Musat
article en
5 citations

Abstract

This paper presents a microring resonator-based weight function for neuromorphic photonic applications achieving a record-high precision of 11.3 bits and accuracy of 9.3 bits for 2 Gbps input optical signals. The system employs an all-analog self-referenced proportional-integral-derivative (PID) controller to perform real-time temperature stabilization within a range of up to 60 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula>C. A self-calibrated weight function is demonstrated for a range of 6 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula>C with a single initial calibration and minimal accuracy and precision degradation. By monitoring the through and drop ports of the microring with variable gain transimpedance amplifiers, accurate and precise weight adjustment is achieved, ensuring optimal performance and reliability. These findings underscore the system's robustness to dynamic thermal environments, highlighting the potential for high-speed reconfigurable analog photonic networks.

Journal of Lightwave TechnologyVol. 43(2)
Quebec Rehabilitation Research Network (CA), Concordia University (US), McGill University (CA), Concordia University (CA)
Affordable and clean energy
Openalex Percentile: Top 13%
Neural Networks and Reservoir Computing
1.74
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