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Thin-film lithium niobate photonic circuit for ray tracing acceleration

Shiji Zhang, Zixi Liu, Xueyi Jiang, Haojun Zhou, Bo Wu, Hailong Zhou, Bo Xu, Qing Zhu, Yuhao Guo, Cheng Zeng (), Jinsong Xia (), Jianji Dong () and Xinliang Zhang ()
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Shiji Zhang: Huazhong University of Science and Technology
Zixi Liu: Huazhong University of Science and Technology
Xueyi Jiang: Huazhong University of Science and Technology
Haojun Zhou: Huazhong University of Science and Technology
Bo Wu: Huazhong University of Science and Technology
Hailong Zhou: Huazhong University of Science and Technology
Bo Xu: Huawei Technologies
Qing Zhu: Huawei Technologies
Yuhao Guo: Huawei Technologies
Cheng Zeng: Huazhong University of Science and Technology
Jinsong Xia: Huazhong University of Science and Technology
Jianji Dong: Huazhong University of Science and Technology
Xinliang Zhang: Huazhong University of Science and Technology

Nature Communications, 2025, vol. 16, issue 1, 1-10

Abstract: Abstract Real-time, physically realistic rendering is a significant challenge in spatial computing systems due to the excessive computational intensity of ray tracing and the performance limitations of current electronic platform. Here, we propose and demonstrate the first photonic counterpart for ray tracing acceleration, capable of performing ray-box intersection tests in the optical domain. Leveraging the high bandwidth, high linearity, and superior efficiency of thin-film lithium niobate (TFLN), our photonic ray tracing core (PRTC) achieves significantly more rapid and energy-efficient computation compared to traditional electronic hardware. Furthermore, by exploiting the binary nature of ray-box intersection tests, we reduce the analog-to-digital converter (ADC) bit-width requirement to a single bit, effectively overcoming the primary bottleneck in analog computing accelerators—the power consumption dominated by ADCs. As a result, our PRTC achieves an energy efficiency of 326 femtojoules per operation (fJ/OP) and demonstrates a modulator bandwidth exceeding 100 GHz. This advancement achieves significant improvements in both speed and energy efficiency by orders of magnitude. Our work demonstrates the feasibility of using photonic chips for ray tracing, effectively circumventing the ADC bottleneck of optical computing systems, and paves the way for future innovations in high-performance, low-power spatial computing applications.

Date: 2025
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DOI: 10.1038/s41467-025-61234-x

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