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9 March 2024 Neuromorphic silicon photonics
Lorenzo Pavesi
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Proceedings Volume PC12890, Smart Photonic and Optoelectronic Integrated Circuits 2024; PC1289004 (2024) https://doi.org/10.1117/12.2692855
Event: SPIE OPTO, 2024, San Francisco, California, United States
Abstract
The recent progress of artificial intelligence (AI) has boosted the computational possibilities in fields where standard computers are not able to perform. The AI paradigm is to emulate human intelligence and therefore breaks the familiar architecture on which digital computers are based. In particular, neuromorphic computing, artificial neural networks (ANN) and deep learning models mimic how the brain computes. With this respect, photonics is a suitable platform to implement ANN hardware thanks to its speed, low power dissipation and multi-wavelength opportunities. One photonic device candidate to perform as an optical neuron is the optical microring resonator. Indeed microring resonators show both a nonlinear response and a capability of optical energy storage, which can be interpreted as a fading memory. Here, we describe the physics of silicon microring resonators and of arrays of microring resonators for application in neuromorphic computing. We describe different types of ANNs from feed-forward networks to photonics extreme learning machines and reservoir computing. In addition, we discuss also hybrid systems where silicon microresonators are coupled to other active material
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Lorenzo Pavesi "Neuromorphic silicon photonics", Proc. SPIE PC12890, Smart Photonic and Optoelectronic Integrated Circuits 2024, PC1289004 (9 March 2024); https://doi.org/10.1117/12.2692855
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KEYWORDS
Silicon photonics
Artificial neural networks
Neural networks
Microresonators
Microrings
Machine learning
Resonators
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