Parameterization of an acousto-optic programmable dispersive filter for closed-loop learning experiments

Abstract

A representation of the search space in optical pulse shaping problems employing an acousto-optic programmable dispersive filter (AOPDF) is presented for use in closed-loop learning experiments where the optimal spectral phase function to some control problem is determined by an iterative learning algorithm. The representation allows the algorithm to select a value for the optical chirp at each frequency control point such that only acoustic grating functions which preserve the spectrum of the shaped pulses are tested. The limits of this space with respect to the rate of applied optical chirp, optical bandwidth and acoustic power are examined and tested through diffraction efficiency studies performed using a commercial AOPDF. The main benefits of this representation are the elimination of undesirable frequency mixing effects, reduction of diffraction efficiency variation between arbitrary pulse shapes and faster convergence of the evolutionary algorithm.