Readout Optimisation of Phase-Change Integrated Photonic Memory

Phase-change materials can be used for the realisation of non-volatile optical memory in integrated photonic systems [1]. These devices consist of patches of Ge<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>Sb<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>Te<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</inf> (GST) phase-change material deposited onto Si<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf>N<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</inf> or Si waveguides (Fig. 1a). Light propagating in the waveguide couples evanescently to the GST, triggering heating which can cause a switch between amorphous and crystalline phases, altering the device's optical transmission, so providing a memory and in-memory computing functionality [2]. Here, the geometry of the GST patch is investigated to optimise the readout contrast when the device undergoes the write process, where an amorphous mark is written into a fully crystalline starting state. Optimisations are carried out using finite-element thermo-optic simulations coupled to a bespoke phase-change model based on a Gillespie-type cellular automata and classical nucleation and growth theory [3], [4].

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Published
2025-06-23
DOI
https://doi.org/10.1109/cleo/europe-eqec65582.2025.11109713
Primary Topic
Neural Networks and Reservoir Computing
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article
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Readout Optimisation of Phase-Change Integrated Photonic Memory

C. David Wright, Rui Xu, Jie Song, Harish Bhaskaran et al.
Neural Networks and Reservoir Computing
article

Readout Optimisation of Phase-Change Integrated Photonic Memory

C. David Wright, Rui Xu, Jie Song, Harish Bhaskaran, Whp Pernice, Nikolaos Farmakidis, Joe Pady
article en

Abstract

Phase-change materials can be used for the realisation of non-volatile optical memory in integrated photonic systems [1]. These devices consist of patches of Ge<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>Sb<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>Te<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</inf> (GST) phase-change material deposited onto Si<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf>N<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</inf> or Si waveguides (Fig. 1a). Light propagating in the waveguide couples evanescently to the GST, triggering heating which can cause a switch between amorphous and crystalline phases, altering the device's optical transmission, so providing a memory and in-memory computing functionality [2]. Here, the geometry of the GST patch is investigated to optimise the readout contrast when the device undergoes the write process, where an amorphous mark is written into a fully crystalline starting state. Optimisations are carried out using finite-element thermo-optic simulations coupled to a bespoke phase-change model based on a Gillespie-type cellular automata and classical nucleation and growth theory [3], [4].

Heidelberg University (DE), University of Exeter (GB), University of Oxford (GB), Kirchhoff (Germany) (DE), MetaMateria (United States) (US)
Affordable and clean energy
Openalex Percentile: Top 89%
Neural Networks and Reservoir Computing
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