Focus shaping of high numerical aperture lens using physics-assisted artificial neural networks

We present a physics-assisted artificial neural network (PhyANN) scheme to efficiently achieve focus shaping of high numerical aperture lens using a diffractive optical element (DOE) divided into a series of annular regions with fixed widths. Unlike the conventional ANN, the PhyANN does not require the training using labeled data, and instead output the transmission coefficients of each annular region of the DOE by fitting weights of networks to minimize the delicately designed loss function in term of focus profiles. Several focus shapes including sub-diffraction spot, flattop spot, optical needle, and multi-focus region are successfully obtained. For instance, we achieve an optical needle with 10 lambda depth of focus, 0.41 lambda lateral resolution beyond diffraction limit and high flatness of almost the same intensity distribution. Compared to typical particle swarm optimization algorithm, the PhyANN has an advantage in DOE design that generates three-dimensional focus profile. Further, the hyperparameters of the proposed PhyANN scheme are also discussed. It is expected that the obtained results benefit various applications including super-resolution imaging, optical trapping, optical lithography and so on. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Automated summary

The PhyANN scheme efficiently achieves focus shaping of high numerical aperture lens using a diffractive optical element, successfully obtaining various focus shapes without the need for labeled data training. The scheme shows potential for applications in super-resolution imaging, optical trapping, optical lithography, and more.