Integrated Optical-to-Optical Gain in a Silicon Photonic Modulator Neuron
Publication Date: 12/1/2025
Event: Optica
Reference: 12(12): 1904-1911, 2025
Authors: Joshua C. Lederman, Princeton University; Yusuf O. Jimoh, Princeton University; Yuxin Wang, Princeton University; Simon Bilodeau, Princeton University; Eric C. Blow, NEC Laboratories America, Inc.; Bhavin J. Shastri, Queens University; Paul R. Prucnal, Princeton University
Abstract: Silicon photonic neural networks can achieve higher throughputs and lower latencies than digital electronic alternatives.However, recently reported implementations of such networks have lacked integrated signal gain, instead utilizingoff-chip amplifiers or co-processors to complete the signal processing pipeline. Photonic neural networks without gainface substantial limitations in network depth and inter-layer fan-out. Here, we demonstrate a fully integrated siliconphotonic modulator neuron capable of up to 14.1 dBgain, achieved by modeling and addressing self-heating behavior inour output PN-junction micro-ring modulator.We use our experimental neuron to emulate a small network subject tohigh loss, achieving superior accuracy on an automated modulation classification benchmark to that of an optimal linearsystem. Our high-gain neuron can serve as a building block vastly expanding the range of neural network architecturesthat can be implemented with silicon photonics.
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