Integrated lithium niobate photonic computing circuit based on efficient and high-speed electro-optic conversion

The surge in artificial intelligence applications calls for scalable, high-speed, and low-energy computation methods. Computing with photons is promising due to the intrinsic parallelism, high bandwidth, and low latency of photons. However, current photonic computing architectures are limited by the...

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Vydané v:Nature communications Ročník 16; číslo 1; s. 8178 - 11
Hlavní autori: Hu, Yaowen, Song, Yunxiang, Zhu, Xinrui, Guo, Xiangwen, Lu, Shengyuan, Zhang, Qihang, He, Lingyan, Franken, Cornelis A. A., Powell, Keith, Warner, Hana, Assumpcao, Daniel, Renaud, Dylan, Wang, Ying, Magalhães, Letícia, Rosborough, Victoria, Shams-Ansari, Amirhassan, Li, Xudong, Cheng, Rebecca, Luke, Kevin, Yang, Kiyoul, Barbastathis, George, Zhang, Mian, Zhu, Di, Johansson, Leif, Beling, Andreas, Sinclair, Neil, Lončar, Marko
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Nature Publishing Group UK 01.09.2025
Nature Publishing Group
Nature Portfolio
Predmet:
639/624/1075/1079
639/624/1075/1081
Artificial intelligence
Bandwidths
Binary data
Computation
Data transfer (computers)
Diffractive optics
Distributed feedback lasers
Energy consumption
Energy efficiency
High speed
Humanities and Social Sciences
Latency
Light
Lithium
Lithium niobates
multidisciplinary
Optics
Photodiodes
Photonics
Photons
Science
Science (multidisciplinary)
Sensors
Signal processing
Silicon
Task complexity
Thin films
ISSN:2041-1723, 2041-1723
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Shrnutí:The surge in artificial intelligence applications calls for scalable, high-speed, and low-energy computation methods. Computing with photons is promising due to the intrinsic parallelism, high bandwidth, and low latency of photons. However, current photonic computing architectures are limited by the speed and energy consumption associated with electronic-to-optical data transfer, i.e., electro-optic conversion. Here, we demonstrate a thin-film lithium niobate (TFLN) computing circuit that addresses this challenge, leveraging both highly efficient electro-optic modulation and the spatial scalability of TFLN photonics. Our circuit is capable of computing at 43.8 GOPS/channel while consuming 0.0576 pJ/OP, and we demonstrate various inference tasks with high accuracy, including the classification of binary data and complex images. Heightening the integration level, we show another TFLN computing circuit that is combined with a hybrid-integrated distributed-feedback laser and heterogeneous-integrated modified uni-traveling carrier photodiode. Our results show that the TFLN photonic platform holds promise to complement silicon photonics and diffractive optics for photonic computing, with extensions to ultrafast signal processing and ranging. Efficient electro-optic conversion is central to photonic computing, and thin-film lithium niobate (TFLN) offers this capability. Here, the authors demonstrate computing circuits on the TFLN platform, enabling the next generation of photonic computing systems featuring both high-speed and low-power.
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ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-025-62635-8