18 citations · 21 across the 7 of their papers we have counts for
7 papers
Quantum photonic frequency processor on thin-film lithium niobate
Ran Yang, Wei Zhou, Dong-Jie Guo +11
The rapid development of photonic quantum information processing necessitates precise and programmable control over optical frequency, a capability critical not only for achieving…
Precise and Robust Domain Engineering Based on Faraday Cage Effect for Thin-film Lithium Niobate Photonics
Yanqun Wang, Furong Zhong, Lin Liu +4
Thin-film lithium niobate (TFLN) waveguides are promising for efficient second-harmonic generation (SHG) owing to their strong optical confinement and large second-order nonlineari…
Programmable electro-optic frequency comb empowers integrated parallel convolution processing
Jinze He, Junzhe Qiang, Yiying Dong +11
Integrated photonic convolution processors make optical neural networks (ONNs) a transformative solution for artificial intelligence applications such as machine vision. To enhance…
On-chip real-time detection of optical frequency variations with ultrahigh resolution using the sine-cosine encoder approach
X. Steve Yao, Yulong Yang, Xiaosong Ma +6
Real-time measurement of optical frequency variations (OFVs) is crucial for various applications including laser frequency control, optical computing, and optical sensing. Traditio…
Roadmap on Neuromorphic Photonics
Daniel Brunner, Bhavin J. Shastri, Mohammed A. Al Qadasi +147
This roadmap consolidates recent advances while exploring emerging applications, reflecting the remarkable diversity of hardware platforms, neuromorphic concepts, and implementatio…
Temporal Multiplexing of Heralded Photons Based on Thin Film Lithium Niobate Photonics
Cagin Ekici, Yonghe Yu, Jeremy C. Adcock +8
Heralded photons from a silicon source are temporally multiplexed utilizing thin film lithium niobate photonics. The time-multiplexed source, operating at a rate of R = 62.2 MHz, e…
120 GOPS Photonic Tensor Core in Thin-film Lithium Niobate for Inference and in-situ Training
Zhongjin Lin, Bhavin J. Shastri, Shangxuan Yu +27
Photonics offers a transformative approach to artificial intelligence (AI) and neuromorphic computing by enabling low-latency, high-speed, and energy-efficient computations. Howeve…