High-performance all-optical photonic crystal synapse based on Mach-Zehnder interferometer and directional coupler utilizing GSST phase-change material
This paper presents a novel architecture for all-optical photonic crystals, leveraging the integration of Mach-Zehnder interferometers and directional couplers for advanced optical neuromorphic synapses. By utilizing photonic crystals and germanium-antimony-selenium-tellurium (GSST) phase-change mat...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-08-01
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| Series: | Results in Physics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2211379725002323 |
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| Summary: | This paper presents a novel architecture for all-optical photonic crystals, leveraging the integration of Mach-Zehnder interferometers and directional couplers for advanced optical neuromorphic synapses. By utilizing photonic crystals and germanium-antimony-selenium-tellurium (GSST) phase-change materials, we achieve precise control over optical transmission. The use of photonic crystals enables a compact footprint and significantly reduces the device size compared to conventional silicon photonics, offering a key advantage in achieving higher integration density for optical neuromorphic systems. Comprehensive finite-difference time-domain (FDTD) simulations demonstrate that incorporating a photonic crystal cavity at the input port significantly enhances single-mode operation, leading to an output signal transmission of more than 99 %. Furthermore, variations in the crystallinity fraction of phase-change material (PCM) rods significantly influence the output signal transmission, enabling precise control of the signal dynamics. Under amorphous and fully crystalline conditions of the GSST-PCM rods, the signal transmission rate varies between −0.02 dB and −13.5 dB, highlighting the profound impact of phase-state changes on system performance. This innovative photonic crystal platform offers a promising avenue for the realization of next-generation optical synapses, paving the way for advanced optical neural networks and machine learning. |
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| ISSN: | 2211-3797 |