Abstract

A wide range of highly–relevant problems in programmable and integrated photonics, optical amplification, and communication deal with inverse system design. Typically, a desired output (usually a gain profile, a noise profile, a transfer function or a similar continuous function) is given and the goal is to determine the corresponding set of input parameters (usually a set of input voltages, currents, powers, and wavelengths). We present a novel method for inverse system design using machine learning and apply it to Raman amplifier design. Inverse system design for Raman amplifiers consists of selecting pump powers and wavelengths that would result in a targeted gain profile. This is a challenging task due to highly–complex interaction between pumps and Raman gain. Using the proposed framework, highly–accurate predictions of the pumping setup for arbitrary Raman gain profiles are demonstrated numerically in C and C+L–band, as well as experimentally in C band, for the first time. A low mean (0.46 and 0.35 dB) and standard deviation (0.20 and 0.17 dB) of the maximum error are obtained for numerical (C+L–band) and experimental (C–band) results, respectively, when employing 4 pumps and 100 km span length. The presented framework is general and can be applied to other inverse problems in optical communication and photonics in general.

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Inverse design of a Raman amplifier in frequency and distance domains using convolutional neural networks

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