Performance enhancement of on-chip optical switch and memory using Ge2Sb2Te5 slot-assisted microring resonator

• Published in: Optics and lasers in engineering 2023-03, Vol.162, p.107436, Article 107436
• Main Authors: Cai, Linying, Lu, Yegang, Zhu, Honghui
• Format: Article
• Language: English
• ISSN: 0143-8166; 1873-0302
• DOI: 10.1016/j.optlaseng.2022.107436

•Non-volatile, reconfigurable multifunctional optical devices based on phase change materials (PCMs).•High extinction ratio and low insertion loss optical switch based on microring resonator and slot waveguide.•The free spectral range (FSR) is expanded by adding a U-bend feedback loop structure.•Changing GST states to achieve a reliable multi-level memory. Phase change material Ge2Sb2Te5 (GST) has recently emerged as a highly promising candidate for reconfigurable photonic devices owing to its non-volatility, high optical contrast, and fast transition speed between the amorphous and crystalline states. We propose a novel nonvolatile 1 × 2 optical switching and multi-level memory based on a GST slot-assisted microring resonator (MRR). GST slot is embedded in silicon microring resonator to enhance the light-matter interaction. The device demonstrates a low insertion loss of 0.7 dB in the amorphous state at the drop port and 0.1 dB in the crystalline state at the through port. The high extinction ratios of around 28.32 dB and 22.71 dB can be achieved at the drop and through ports, respectively. A multi-level photonic memory was proposed by combining MRR and a U-bend feedback loop embedded with GST slot. Free spectral range can be expanded from 21.6 nm to 43.2 nm by modifying the length of the feedback loop instead of the microring diameter. The minimum read contrast of 19% among each level can be achieved by changing two GST states individually for a reliable 4-level memory. The 2-level memory demonstrates a read contrast of 90% which is around 4.3 times higher than the traditional device. Our all-optical approach represents a significant step towards the development of low-loss and reliable photonic memory for neuromorphic computing and artificial intelligence.