The interest in the development of silicon photonic integrated devices is rapidly expanding from the telecom/datacom sector to new emerging application domains such as artificial intelligence or quantum photonics. Silicon benefits from CMOS-compatible fabrication processes and a high index contrast. However, the implementation of new functionalities or the achievement of a superior performance necessarily requires the integration of new materials in current silicon photonics platforms. In this context, phase change materials have been established as promising material technologies for optical switching. In this work, the benefits and challenges for enabling optical switching with VO2/Si and GST/Si devices will be analyzed and discussed.
Reconfigurable photonics enable the realization of diverse functionalities using a single photonic device. Such devices could play a prominent role in a large variety of applications, including neuromorphic computing, telecommunications, data communication networks, or optical sensing. The silicon photonics platform is the ideal candidate to implement those devices due to its unique capability for handling large scalability and mass-manufacturing at low cost. However, switching and modulation functionalities offered by current silicon platforms are based on the plasma dispersion effect or the thermo-optic effect, which yields devices with large footprints or reduced bandwidth, preventing scalability. Therefore, combining silicon photonics with materials with unique optoelectronic properties is emerging as the most promising path towards developing ultra-compact devices with competitive performance. In this context, new functionalities not yet offered by current silicon platforms may be implemented by the integration of phase change materials (PCMs) and transparent conducting oxides (TCOs) in silicon structures. Hybrid PCM/Si and TCO/Si devices will be presented for implementing weighting operation, reconfigurable activation functions, and optical storage, which are crucial functionalities in artificial neural networks and the emerging field of neuromorphic computing.
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