Integrated optical-to-optical gain in a silicon photonic modulator neuron

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 utilizing off-chip amplifiers or co-processors to complete the signal processing pipeline. Photonic neural networks without gain face substantial limitations in network depth and inter-layer fan-out. Here, we demonstrate a fully integrated silicon photonic modulator neuron capable of up to 14.1 dB gain, achieved by modeling and addressing self-heating behavior in our output PN-junction micro-ring modulator. We use our experimental neuron to emulate a small network subject to high loss, achieving superior accuracy on an automated modulation classification benchmark to that of an optimal linear system. Our high-gain neuron can serve as a building block vastly expanding the range of neural network architectures that can be implemented with silicon photonics.