Presentation
3 October 2024 Modeling the RSA-based all-optical perceptrons for photonic neural networks
Alexander V. Kildishev, Omer Yesilurt, Ludmila J. Prokopeva
Author Affiliations +
Abstract
The recent advancement of photonic neural networks (PNNs) enables high-throughput, low-power neuromorphic computing, bypassing capacitive wire limitations. Here, we discuss advanced approaches to modeling an optical perceptron based on reverse saturated absorption (RSA). To enable real-life models beyond classical Lorentz and Debye media, we derive the novel frequency dispersion equations of RSA materials with disorder and couple them with the master equations for singlet and triplet state carrier kinetics in a joint finite difference time domain framework. Our numerical results match the experimental measurements; the proposed framework can enable efficient optimization of all-optical RSA perceptrons for next-generation photonic hardware.
Conference Presentation
© (2024) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Alexander V. Kildishev, Omer Yesilurt, and Ludmila J. Prokopeva "Modeling the RSA-based all-optical perceptrons for photonic neural networks", Proc. SPIE PC13113, Photonic Computing: From Materials and Devices to Systems and Applications, PC131130P (3 October 2024); https://doi.org/10.1117/12.3028581
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KEYWORDS
Modeling

Neural networks

Dispersion

Electrooptical modeling

Electrooptics

Finite-difference time-domain method

Integrated photonics

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