ArticleNature communications2025
Integrated lithium niobate photonic computing circuit based on efficient and high-speed electro-optic conversion.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Emerging integrated photonic neural network technologies for artificial intelligence: from devices to systems.National science review · 2026Review
- Quantum nonlinearity for optical neural computing.Nature communications · 2026Article
- Engineering and Tuning of High Quality Hexagonal Boron Nitride Nanophotonic Resonators.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Ultrafast reconfigurable topological-modulators-based photonic processing accelerator.Nature communications · 2026Article
- Inverse-designed nanophotonic neural network accelerators for ultra-compact optical computing.Nature communications · 2026Article
- High-clockrate free-space optical in-memory computing.Light, science & applications · 2026Article
- Scalable unitary computing using time-parallelized photonic lattices.Nanophotonics (Berlin, Germany) · 2025Article
- Strong-coupling and high-bandwidth cavity electro-optic modulation for advanced pulse-comb synthesis.Light, science & applications · 2025Article
- Integrated electro-optic digital-to-analog link for efficient computing and arbitrary waveform generation.Nature photonics · 2025Article
- In-situ training in programmable photonic frequency circuits.Nanophotonics (Berlin, Germany) · 2025Article
- Thin-film lithium niobate photonic circuit for ray tracing acceleration.Nature communications · 2025Article
Corrections and comments
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Authors and funding
27 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.