Multi-Plane Light Converter based on metasurface and Machine Learning - PhD thesis - mode sorter applications

Aug 5, 2026

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This scientific study investigates metasurfaces through their core principles, practical applications, and fabrication approaches required for realisation. Central emphasis is placed on documenting the fabrication workflow for the Multi-Plane Light Convertor (MPLC), using captured images to confirm the accuracy of key steps. Early measurements show satisfactory MPLC operation, though additional analysis and optimisation are required to reach full capability. The MPLC enables uses spanning telecommunications, fiber sensing, medical imaging, and biological tomoholography, alongside development of a fiber bend sensor based on inter-modal coupling and modal decomposition with machine learning-based mode sorting for robust millimetric curvature detection.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages218

Reading Time9 h 9 min

AccessFree to download

General introduction

Metasurfaces classification

Metasurface’s application

Amorphous silicon (α-Si) metasurfaces

Flat optics

Fibre sensing technology

Walkthrough

Background theory

Introduction

Snell’s law and generalised Snell’s Law

Modes of Light

Hermite-Gaussian (HG) modes

Laguerre-Gaussian (LG) modes

Laguerre-polynomial (LP) modes

Angelucci, Sara (2025) Multi-Plane Light Converter based on metasurface and Machine Learning to understand the mode sorter’s applications. PhD thesis [https://theses.gla.ac.uk/84862/](https://theses.gla.ac.uk/84862/) Copyright and moral rights for this work are retained by the author A copy can be downloaded for personal non-commercial research or study, without prior permission or charge This work cannot be reproduced or quoted extensively from without first obtaining permission in writing from the author The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the author When referring to this work, full bibliographic details including the author, title, awarding institution and date of the thesis must be given Enlighten: Theses [https://theses.gla.ac.uk/](https://theses.gla.ac.uk/) [research-enlighten@glasgow.ac.uk](research-enlighten@glasgow.ac.uk) Multi-Plane Light Converter based on metasurface and Machine Learning to understand the mode sorter’s applications by Sara Angelucci SUBMITTED IN FULlFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY James Watt School of Engineering College of Science and Engineering ABSTRACT This scientific study delves into the realm of metasurfaces, offering an exhaustive investigation into their underlying principles, practical applications, and the fabrication methods imperative for their realisation. A focal point of this exploration is the detailed exposition of the fabrication process for the Multi-Plane Light Convertor (MPLC) device, supported by captured images validating the precision of each critical step. Initial results indicate the satisfactory functioning of the MPLC, yet further analyses and optimisations are deemed essential to unlock its full potential. The MPLC device demonstrates versatile applications across telecommunications, energy-related fiber sensing, medical imaging, and biological tomoholography. However, at present, no physical devices based on metasurfaces are available that can fully implement these functions. In parallel, a novel and robust fibre bend sensor has been developed, showcasing the capability to precisely locate bends through inter-modal coupling. Modal decomposition reduces sensitivity to relative phase, revealing features providing accurate information about the shape or position of bends within the fiber. The simplicity and cost-effectiveness of this approach offer potential applications in wearable technology, motion sensors and aircraft wing shape sensing. Both experiments revolve around the concept of a mode sorter. The first experiment focuses on creating a novel device not yet available on the market, specifically the MultiPlane Light Converter (MPLC) . The second set of experiments, on the other hand, is centered around the practical application of the mode sorter as an instrumental component. The combination of mode de-multiplexing with machine learning holds promise for powerful applications, particularly in scenarios where constant variations in relative phase can be treated as noise, such as monitoring atmospheric conditions or extracting information from environments with dense scattering. Practical deployment considerations include the need for retraining in cases of significant system or fiber type changes. Once fully trained, retraining intervals are typically weeks to months under normal temperature fluctuations, necessitating further research into extreme temperature variations encountered in applications like aviation. The use of multi-core fibers is recommended to enhance sensitivity to multiple directions. In summary, the study demonstrates the feasibility of utilizing machine learning for accurate millimetric-scale curvature detection by incorporating a mode sorter into the optical setup. While exhibiting robust performance, limitations exist in detecting bends or movements not introducing changes in i

FAQs

What device is designed and characterised in this PhD study?

The study focuses on the Multi-Plane Light Convertor (MPLC), a metasurface-based device built to support mode sorting and demultiplexing for downstream sensing and imaging tasks.

Which application areas are linked to the MPLC concept in the thesis?

The abstract highlights telecommunications, energy-related fiber sensing, medical imaging, and biological tomoholography as intended application domains enabled by the MPLC and related mode-sorting principles.

How does the thesis connect mode sorting with machine learning?

It combines mode demultiplexing with machine learning to infer curvature and bend information, treating variations in relative phase as noise in scenarios such as atmospheric monitoring or environments with dense scattering.

Aug 5, 2026

Upload by Liam