Design of a silicon Mach-Zehnder modulator via deep learning and evolutionary algorithms.

As an essential block in optical communication systems, silicon (Si) Mach-Zehnder modulators (MZMs) are approaching the limits of possible performance for high-speed applications. However, due to a large number of design parameters and the complex simulation of these devices, achieving high-performance configuration employing conventional optimization methods result in prohibitively long times and use of resources. Here, we propose a design methodology based on artificial neural networks and heuristic optimization that significantly reduces the complexity of the optimization process.