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

Yaowen Hu (), Yunxiang Song (), Xinrui Zhu, Xiangwen Guo, Shengyuan Lu, Qihang Zhang, Lingyan He, Cornelis A. A. Franken, Keith Powell, Hana Warner, Daniel Assumpcao, Dylan Renaud, Ying Wang, Letícia Magalhães, Victoria Rosborough, Amirhassan Shams-Ansari, Xudong Li, Rebecca Cheng, Kevin Luke, Kiyoul Yang, George Barbastathis, Mian Zhang, Di Zhu, Leif Johansson, Andreas Beling, Neil Sinclair and Marko Lončar ()
Additional contact information
Yaowen Hu: Harvard University
Yunxiang Song: Harvard University
Xinrui Zhu: Harvard University
Xiangwen Guo: University of Virginia
Shengyuan Lu: Harvard University
Qihang Zhang: Massachusetts Institute of Technology
Lingyan He: 675 Massachusetts Ave
Cornelis A. A. Franken: Harvard University
Keith Powell: Harvard University
Hana Warner: Harvard University
Daniel Assumpcao: Harvard University
Dylan Renaud: Harvard University
Ying Wang: 675 Massachusetts Ave
Letícia Magalhães: Harvard University
Victoria Rosborough: 41 Aero Camino
Amirhassan Shams-Ansari: Harvard University
Xudong Li: Harvard University
Rebecca Cheng: Harvard University
Kevin Luke: 41 Aero Camino
Kiyoul Yang: Harvard University
George Barbastathis: Massachusetts Institute of Technology
Mian Zhang: 675 Massachusetts Ave
Di Zhu: National University of Singapore
Leif Johansson: 41 Aero Camino
Andreas Beling: University of Virginia
Neil Sinclair: Harvard University
Marko Lončar: Harvard University

Nature Communications, 2025, vol. 16, issue 1, 1-11

Abstract: Abstract 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.

Date: 2025
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DOI: 10.1038/s41467-025-62635-8

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