Implementation of programmable optical filters with arbitrary spectral response in the core of a Field Programmable Photonics Gate Array (FPPGA)

Abstract

[ES] Until now all the photonic integrated circuits (PICs) designed have been the so-called Application Specific Photonic Integrated Circuits (ASPICs) where a particular circuit and chip configuration is designed to optimally perform a particular application. A Field Programmable Photonic Gate Array (FPPGA) is the evolution of an ASPIC, where a PIC is designed to be able to be configured to perform multitude applications. A software capable of obtaining the programmable parameters of the core of the FPPGA depending on the application to perform is needed. The objective of this thesis is to develop and demonstrate the performance of a synthesis algorithm capable of obtaining the programmable parameters of the FPPGA core to implement an optical filter with arbitrary spectral response. The thesis can be divided in two parts. The first one consists in the development and testing of the algorithm in a simulation environment in Python while the second part focuses on testing the algorithm using the FPPGA core.

[EN] Until now all the photonic integrated circuits (PICs) designed have been the so-called Application Specific Photonic Integrated Circuits (ASPICs) where a particular circuit and chip configuration is designed to optimally perform a particular application. A Field Programmable Photonic Gate Array (FPPGA) is the evolution of an ASPIC, where a PIC is designed to be able to be configured to perform multitude applications. A software capable of obtaining the programmable parameters of the core of the FPPGA depending on the application to perform is needed. The objective of this thesis is to develop and demonstrate the performance of a synthesis algorithm capable of obtaining the programmable parameters of the FPPGA core to implement an optical filter with arbitrary spectral response. The thesis can be divided in two parts. The first one consists in the development and testing of the algorithm in a simulation environment in Python while the second part focuses on testing the algorithm using the FPPGA core.