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Silicon photonics has emerged as a mature technology that is expected to play a key role in critical emerging applications, including very high data rate optical communications, distance sensing for autonomous vehicles, photonic-accelerated computing, and quantum information processing. The success of silicon photonics has been enabled by the unique combination of performance, high yield, and high-volume capacity that can only be achieved by standardizing manufacturing technology. Today, standardized silicon photonics technology platforms implemented by foundries provide access to optimized library components, including low-loss optical routing, fast modulation, continuous tuning, high-speed germanium photodiodes, and high-efficiency optical and electrical interfaces. However, silicon's relatively weak electro-optic effects result in modulators with a significant footprint and thermo-optic tuning devices that require high power consumption, which are substantial impediments for very large-scale integration in silicon photonics. Microelectromechanical systems (MEMS) technology can enhance silicon photonics with building blocks that are compact, low-loss, broadband, fast and require very low power consumption. Here, we introduce a silicon photonic MEMS platform consisting of high-performance nano-opto-electromechanical devices fully integrated alongside standard silicon photonics foundry components, with wafer-level sealing for long-term reliability, flip-chip bonding to redistribution interposers, and fibre-array attachment for high port count optical and electrical interfacing. Our experimental demonstration of fundamental silicon photonic MEMS circuit elements, including power couplers, phase shifters and wavelength-division multiplexing devices using standardized technology lifts previous impediments to enable scaling to very large photonic integrated circuits for applications in telecommunications, neuromorphic computing, sensing, programmable photonics, and quantum computing.
Camille Sophie Brès, Marco Clementi, Jiaye Wu, Qian Li
Tobias Kippenberg, Mikhail Churaev, Xinru Ji, Zihan Li, Alisa Davydova, Junyin Zhang, Yang Chen, Xi Wang, Kai Huang