Fourier convolution-parallel neural network framework with library matching for multi-tool processing decision-making in optical fabrication.

Intelligent manufacturing of ultra-precision optical surfaces is urgently desired but rather difficult to achieve due to the complex physical interactions involved. The development of data-oriented neural networks provides a new pathway, but existing networks cannot be adapted for optical fabrication with a high number of feature dimensions and a small specific dataset. In this Letter, for the first time to the best of our knowledge, a novel Fourier convolution-parallel neural network (FCPNN) framework with library matching was proposed to realize multi-tool processing decision-making, including basically all combination processing parameters (tool size and material, slurry type and removal rate).