Bias Control for Mach-Zehnder Modulators:A Gradient-Based Optimization Approach

Abstract

In this paper, we explore the use of gradient basedoptimization algorithms for automated bias control in Mach-Zehnder Modulators (MZM). We present and demonstrate, experimentally,five gradient descent algorithms; Stochastic GradientDescent (SGD), Stochastic Gradient Descent with Momentum(SGD+M), Adagrad, RMSProp, and Adam, applied to the biascontrol problem in MZMs. We present a method of creating anerror signal from the measured output of an MZM with a lowfrequency pilot tone, and provide a detailed explanation of howeach algorithm is used to both identify the set bias conditionand track the bias condition in the presence of disturbances.Our implementation is capable of identifying and holding thenull condition and the quadrature condition. We evaluate thebias point identification for each algorithm by measuring andanalysing the step response for each method. We test the biastracking of each algorithm using three forms of disturbance;RF power disturbances, temperature disturbance, and long-termbias drift. All tests were conducted at 20GHz. To the best ofour knowledge, this is the first investigation into the applicationon gradient based learning approaches for MZM bias control.This work has great importance on future bias control designand implementations for telecommunications, the space sector,Microwave Photonics (MWP), and defence