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
John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA.
State Key Laboratory for Mesoscopic Physics and Frontiers Science Center for Nano-optoelectronics, School of Physics, Peking University, Beijing, 100871, China.
Nat Commun. 2025 Sep 1;16(1):8178. doi: 10.1038/s41467-025-62635-8.
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.
人工智能应用的激增需要可扩展、高速且低能耗的计算方法。由于光子具有固有的并行性、高带宽和低延迟，光子计算颇具前景。然而，当前的光子计算架构受到与电光数据传输（即电光转换）相关的速度和能耗的限制。在此，我们展示了一种薄膜铌酸锂（TFLN）计算电路，该电路利用高效的电光调制和TFLN光子学的空间可扩展性来应对这一挑战。我们的电路能够以43.8 GOPS/通道的速度进行计算，同时每操作消耗0.0576 pJ的能量，并且我们展示了各种高精度的推理任务，包括二进制数据和复杂图像的分类。为了提高集成度，我们展示了另一种TFLN计算电路，它与混合集成分布式反馈激光器和异质集成改进型单载流子光电二极管相结合。我们的结果表明，TFLN光子平台有望在光子计算中补充硅光子学和衍射光学，并扩展到超快信号处理和测距领域。
