Programmable photonic matrices using hybrid silicon - phase change devices
Programmable photonic matrices using hybrid silicon - phase change devices
This thesis aims to develop and characterise hybrid silicon-on-insulator integrated photonic devices clad with low-loss optical phase change materials. Thin films of such materials can be optically addressed using direct writing with short pulses of above-bandgap laser light, thereby enabling reversible, non-volatile programming capabilities to be incorporated into a range of integrated devices without the need for any external regulatory electronics or equipment. These devices have far-reaching applications, particularly in the fields of optical and neuromorphic computing, as well as quantum simulation and the creation of optical analogue systems
In this work, I introduce a platform based on multi-mode interferometers and arrays of coupled silicon waveguides clad with thin films of the ultra-low-loss chalcogenide phase change material antimony tri-selenide. Devices are experimentally programmed and characterised using a contact-free prism coupler to address multiple output ports simultaneously. The systems under study showcase the ability of our platform to realise a wide variety of transmission matrices using a single device geometry, achieved by inscribing nanoscale refractive index perturbation patterns into the phase change film.
A crucial step in advancing this technology is the creation of an inverse design protocol capable of predicting the required refractive index pixel pattern for a given target transmission matrix. In the later stages of this work I present a machine-learning-based inverse design approach, built around neural network surrogate models, designed for near real-time prediction of pixel patterns to implement any arbitrary unitary transformation. We demonstrate full control over both the phase and amplitude of all matrix elements, matching the performance of several commercially available alternative technologies, before examining some of the challenges and constraints associated with the scaling of such an approach.
Integrated Photonics, Reconfigurable Photonics, Phase Change Materials, Machine Learning, Inverse Design, Optical Computing, Quantum Simulation
University of Southampton
Radford, Thomas William
71ac0576-afc9-43c4-b0bf-c51719afe551
2025
Radford, Thomas William
71ac0576-afc9-43c4-b0bf-c51719afe551
Muskens, Otto
2284101a-f9ef-4d79-8951-a6cda5bfc7f9
Politi, Alberto
cf75c0a8-d34d-4cbe-b9d5-e408c0edeeec
Radford, Thomas William
(2025)
Programmable photonic matrices using hybrid silicon - phase change devices.
University of Southampton, Doctoral Thesis, 173pp.
Record type:
Thesis
(Doctoral)
Abstract
This thesis aims to develop and characterise hybrid silicon-on-insulator integrated photonic devices clad with low-loss optical phase change materials. Thin films of such materials can be optically addressed using direct writing with short pulses of above-bandgap laser light, thereby enabling reversible, non-volatile programming capabilities to be incorporated into a range of integrated devices without the need for any external regulatory electronics or equipment. These devices have far-reaching applications, particularly in the fields of optical and neuromorphic computing, as well as quantum simulation and the creation of optical analogue systems
In this work, I introduce a platform based on multi-mode interferometers and arrays of coupled silicon waveguides clad with thin films of the ultra-low-loss chalcogenide phase change material antimony tri-selenide. Devices are experimentally programmed and characterised using a contact-free prism coupler to address multiple output ports simultaneously. The systems under study showcase the ability of our platform to realise a wide variety of transmission matrices using a single device geometry, achieved by inscribing nanoscale refractive index perturbation patterns into the phase change film.
A crucial step in advancing this technology is the creation of an inverse design protocol capable of predicting the required refractive index pixel pattern for a given target transmission matrix. In the later stages of this work I present a machine-learning-based inverse design approach, built around neural network surrogate models, designed for near real-time prediction of pixel patterns to implement any arbitrary unitary transformation. We demonstrate full control over both the phase and amplitude of all matrix elements, matching the performance of several commercially available alternative technologies, before examining some of the challenges and constraints associated with the scaling of such an approach.
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Published date: 2025
Keywords:
Integrated Photonics, Reconfigurable Photonics, Phase Change Materials, Machine Learning, Inverse Design, Optical Computing, Quantum Simulation
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Local EPrints ID: 505677
URI: http://eprints.soton.ac.uk/id/eprint/505677
PURE UUID: be9f710a-aa4e-4df4-ab6a-eb415c67cc73
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Date deposited: 16 Oct 2025 16:37
Last modified: 17 Oct 2025 01:52
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Author:
Thomas William Radford
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