Improved SPGD Algorithm for Optical Phased Array Phase Calibration

Abstract

A chip-level optical beam steerer is an inevitable choice for next-generation light detection and ranging (LiDAR). The research on optical phased array (OPA) is the most intriguing. However, the complexity of control and calibration speed limit the full potential as the number of channels increases. In this paper, an improved stochastic parallel gradient-descent algorithm combined with the Nesterov accelerated gradient method (NSPGD) is presented and applied in a 512-channel OPA. This algorithm can reduce the phase calibration time of large-scale OPA and demonstrates a better convergence performance than traditional SPGD. Compared with the traditional SPGD and hill-climbing (HC) algorithm, optimized convergence performance of NSPGD is shown. The side mode suppression ratio (SMSR) of over 10dB for 512-channel OPA is obtained with the NSPGD algorithm, and the convergence speed is twice that of traditional SPGD. In addition, a temperature-controlled OPA is also studied to stabilize the whole calibration system.

Accelerating optical phased array calibration with improved SPGD algorithm

“…As a result, optical phased array requires precise calibration before deployment to reduce inherent errors. Various calibration algorithms, such as genetic [4], stochastic parallel gradient descent (SPGD) [5], and improved SPGD with Nesterov acceleration (NSPGD) [6], have been proposed. However, when dealing with large-scale phased array, genetic algorithm exhibits sluggish convergence towards local rather than global optimal solutions.…”