Photonic machine learning with on-chip diffractive optics

Tingzhao Fu, Yubin Zang, Yuyao Huang, Zhenmin Du, Honghao Huang, Chengyang Hu, Minghua Chen, Sigang Yang and Hongwei Chen ()
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Tingzhao Fu: Tsinghua University
Yubin Zang: Tsinghua University
Yuyao Huang: Tsinghua University
Zhenmin Du: Tsinghua University
Honghao Huang: Tsinghua University
Chengyang Hu: Tsinghua University
Minghua Chen: Tsinghua University
Sigang Yang: Tsinghua University
Hongwei Chen: Tsinghua University

Nature Communications, 2023, vol. 14, issue 1, 1-10

Abstract: Abstract Machine learning technologies have been extensively applied in high-performance information-processing fields. However, the computation rate of existing hardware is severely circumscribed by conventional Von Neumann architecture. Photonic approaches have demonstrated extraordinary potential for executing deep learning processes that involve complex calculations. In this work, an on-chip diffractive optical neural network (DONN) based on a silicon-on-insulator platform is proposed to perform machine learning tasks with high integration and low power consumption characteristics. To validate the proposed DONN, we fabricated 1-hidden-layer and 3-hidden-layer on-chip DONNs with footprints of 0.15 mm2 and 0.3 mm2 and experimentally verified their performance on the classification task of the Iris plants dataset, yielding accuracies of 86.7% and 90%, respectively. Furthermore, a 3-hidden-layer on-chip DONN is fabricated to classify the Modified National Institute of Standards and Technology handwritten digit images. The proposed passive on-chip DONN provides a potential solution for accelerating future artificial intelligence hardware with enhanced performance.

Date: 2023
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DOI: 10.1038/s41467-022-35772-7

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