Link loss analysis of integrated linear weight bank within silicon photonic neural network

Eric C. Blow; Jiawei Zhang; Weipeng Zhang; Simon Bilodeau; Josh Lederman; Bhavin Shastri; Paul R. Prucnal

doi:10.1117/12.3016786

18 June 2024 Link loss analysis of integrated linear weight bank within silicon photonic neural network

Eric C. Blow, Jiawei Zhang, Weipeng Zhang, Simon Bilodeau, Josh Lederman, Bhavin Shastri, Paul R. Prucnal

Proceedings Volume 13017, Machine Learning in Photonics; 130170H (2024) https://doi.org/10.1117/12.3016786
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Abstract

In the past decade, the field of neuromorphic photonics has experienced significant growth. To extend the reach of this technology, researchers continue to push the limits of these systems with respect to network size and bandwidth. However, without proper RF-optimized architectural designs, as operating frequencies are scaled up, significant losses of RF power can be incurred at each neuron. Within the broadcast and weight neuromorphic photonic architecture, this excess loss will be accumulated until processing is no longer feasible. If designed properly, RF loss can be minimized significantly, and residual loss could be compensated by cointegrated transimpedance amplifiers, thus enabling further scaling of the network. In this paper, the authors present broadband weighting of RF input signals with a 3-dB bandwidth of 4.28 GHz, utilizing the linear front-end of a silicon photonic neural network. Additionally, the authors present link loss measurements and analysis.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Eric C. Blow, Jiawei Zhang, Weipeng Zhang, Simon Bilodeau, Josh Lederman, Bhavin Shastri, and Paul R. Prucnal "Link loss analysis of integrated linear weight bank within silicon photonic neural network", Proc. SPIE 13017, Machine Learning in Photonics, 130170H (18 June 2024); https://doi.org/10.1117/12.3016786

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