Compact optical convolution processing unit based on multimode interference

Convolutional neural networks are an important category of deep learning, currently facing the limitations of electrical frequency and memory access time in massive data processing. Optical computing has been demonstrated to enable significant improvements in terms of processing speeds and energy efficiency. However, most present optical computing schemes are hardly scalable since the number of optical elements typically increases quadratically with the computational matrix size. Here, a compact on-chip optical convolutional processing unit is fabricated on a low-loss silicon nitride platform to demonstrate its capability for large-scale integration. Three 2 × 2 correlated real-valued kernels are made of two…

• NN
  National Natural Science Foundation of China

  Awards: 62235011, 62075212, 61925505
• CA
  Chinese Academy of Sciences
• YI
  Youth Innovation Promotion Association of the Chinese Academy of Sciences

  Award: 2022111
• YI
  Youth Innovation Promotion Association