Modeling the RSA-based all-optical perceptrons for photonic neural networks

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.