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].
Authors
- C. David Wright (ORCID: https://orcid.org/0000-0003-4087-7467)
- Rui Xu (ORCID: https://orcid.org/0000-0002-8254-2668)
- Jie Song (ORCID: https://orcid.org/0000-0002-2243-3902)
- Harish Bhaskaran (ORCID: https://orcid.org/0000-0003-0774-8110)
- Whp Pernice
- Nikolaos Farmakidis (ORCID: https://orcid.org/0000-0001-9974-1607)
- Joe Pady
Institutions
- Heidelberg University (DE)
- University of Exeter (GB)
- University of Oxford (GB)
- Kirchhoff (Germany) (DE)
- MetaMateria (United States) (US)
Publication Details
- Published
- 2025-06-23
- DOI
- https://doi.org/10.1109/cleo/europe-eqec65582.2025.11109713
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00