Highly efficient photonic convolver via lossless mode-division fan-in

Shangsen Sun, Shiji Zhang, Bo Wu, Shan Jiang, Baiheng Zhao, Hailong Zhou, Jianji Dong () and Xinliang Zhang
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Shangsen Sun: Huazhong University of Science and Technology
Shiji Zhang: Huazhong University of Science and Technology
Bo Wu: Huazhong University of Science and Technology
Shan Jiang: Huazhong University of Science and Technology
Baiheng Zhao: Huazhong University of Science and Technology
Hailong Zhou: 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, 1-9

Abstract: Abstract Optical neural networks (ONNs) leverage the parallelism and low-energy consumption of photonic signal processing to overcome the limitations of traditional electronic computing. Optics inherently enables fan-in and fan-out without the Resistor-Capacitor (RC) and Inductor-Capacitor (LC) delays of electrical interconnects. However, for single-mode photonic integrated circuits, reciprocity constraints introduce unavoidable loss during beam combining, hindering large-scale on-chip photonic fan-in. To overcome this challenge, we provide a photonic lossless mode-division fan-in solution for the convolution accelerators. Using inverse design, we developed a compact multimode photonic convolution accelerator (0.42 mm2) with ±15 nm fabrication tolerance and 35 nm optical bandwidth, enabling parallel computation across mode and wavelength dimensions. Experimental results in the C-band confirm a 6–7 bit convolution precision, leading to classification accuracies of 95.2% on MNIST and 87.9% on Fashion-MNIST. Moreover, the device offers a theoretical computational density of 125.14 TOPS/mm2, underscoring its potential for scalable and energy-efficient photonic computing accelerators.

Date: 2025
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DOI: 10.1038/s41467-025-62954-w

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