Ultra-high endurance silicon photonic memory using vanadium dioxide

[EN] Silicon photonics arises as a viable solution to address the stringent resource demands of emergent technologies, such as neural networks. Within this framework, photonic memories are fundamental building blocks of photonic integrated circuits that have not yet found a standardized solution due...

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Detalles Bibliográficos
Autores: Seoane-Martínez, Juan José, Navarro-Arenas, Juan, Recaman, Maria, Koen Schouteden, Locquet, Jean-Pierre, Parra Gomez, Jorge|||0000-0003-4610-3411, Sanchis Kilders, Pablo|||0000-0003-2984-4218
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/214594
Acceso en línea:https://riunet.upv.es/handle/10251/214594
Access Level:acceso abierto
Palabra clave:Optical memory
Vanadium dioxide
Silicon photonics
TEORÍA DE LA SEÑAL Y COMUNICACIONES
Descripción
Sumario:[EN] Silicon photonics arises as a viable solution to address the stringent resource demands of emergent technologies, such as neural networks. Within this framework, photonic memories are fundamental building blocks of photonic integrated circuits that have not yet found a standardized solution due to several trade-offs among different metrics such as energy consumption, speed, footprint, or fabrication complexity, to name a few. In particular, a photonic memory exhibiting ultra-high endurance performance (>106¿cycles) has been elusive to date. Here, we report an ultra-high endurance silicon photonic volatile memory using vanadium dioxide (VO2) exhibiting a record cyclability of up to 107¿cycles without degradation. Moreover, our memory features an ultra-compact footprint below 5¿µm with the potential for nanosecond and picojoule programming performance. Our silicon photonic memory could find application in emerging photonic applications demanding a high number of memory updates, such as photonic neural networks with in situ training.