RSOA-based colorless multilevel transmitter with electrical signal predistortion

In this paper we present the design and a numerical proof-of-concept of an RSOA-based colorless multilevel transmitter, suitable for use in optical network units at user premises. Simple electrical predistortion of the M-QAM modulation signal theoretically allows the transmitter to be operated at up to 4 GBd symbol rate with 16-QAM modulation accounting for the transmission channel impairments and noise at the detection stage, if forward error correction is implemented. We also numerically confirm that 4-QAM operation is possible without the modulation signal predistortion, as long as the constellation is derotated after detection, which can be easily done using digital feed-forward algorithm

Noise-resilient and high-speed deep learning with coherent silicon photonics

The explosive growth of deep learning applications has triggered a new era in computing hardware, targeting the efficient deployment of multiply-and-accumulate operations. In this realm, integrated photonics have come to the foreground as a promising energy efficient deep learning technology platform for enabling ultra-high compute rates. However, despite integrated photonic neural network layouts have already penetrated successfully the deep learning era, their compute rate and noise-related characteristics are still far beyond their promise for high-speed photonic engines. Herein, we demonstrate experimentally a noise-resilient deep learning coherent photonic neural network layout that operates at 10GMAC/sec/axon compute rates and follows a noise-resilient training model. The coherent photonic neural network has been fabricated as a silicon photonic chip and its MNIST classification performance was experimentally evaluated to support accuracy values of &gt;99% and &gt;98% at 5 and 10GMAC/sec/axon, respectively, offering 6× higher on-chip compute rates and &gt;7% accuracy improvement over state-of-the-art coherent implementations.</p