학술논문

Reservoir Computing Using On-Chip XGM-Based Nonlinear Processing by Membrane SOAs on Si-MZI
Document Type
Periodical
Source
Journal of Lightwave Technology J. Lightwave Technol. Lightwave Technology, Journal of. 42(8):2859-2867 Apr, 2024
Subject
Communication, Networking and Broadcast Technologies
Photonics and Electrooptics
Optical feedback
Optical fibers
Semiconductor optical amplifiers
Fiber nonlinear optics
Optical saturation
Optical reflection
Bars
All-optical nonlinear processing
III-V/Si integration
reservoir computing
semiconductor optical amplifiers
Language
ISSN
0733-8724
1558-2213
Abstract
We propose and demonstrate reservoir computing (RC) by all-optical nonlinear processing based on cross-gain modulation (XGM) in III-V membrane semiconductor optical amplifiers (SOAs) on a Si Mach-Zehnder interferometer (MZI). In the proposed configuration, two counter-propagating optical signals are input into the SOA-MZI from opposite ports, where they nonlinearly modulate each other via XGM, and are output to the other two ports. This realizes an on-chip XGM-based nonlinear processor that separately takes two input signals and returns two output signals, not requiring optical circulators. We implement an all-optical time-delay RC circuit using the SOA-MZI chip coupled with a fiber feedback loop. The strong optical confinement of membrane SOAs brings significant XGM, requiring only 47-mW power consumption of the SOA-MZI under −1-dBm average fiber input power. This low power consumption leads to an energy per nonlinear processing of only 11 pJ within the virtual node interval of 237 ps. Processing performances of the RC system are evaluated through information processing capacity (IPC) and the Santa-Fe time-series prediction task, which clearly indicates that the system has a significant nonlinear processing ability. Those dependencies on the optical power of the delayed feedback signal are investigated, clarifying that larger feedback power brings higher processing performances and stronger nonlinear transformation of the information. Our scheme offers a novel all-optical nonlinear functionality that is fully integratable onto Si-based photonic neural network chips.