Abstract
Compact photonic elements that control both the diffraction and interference of light offer superior performance at ultra-compact dimensions. Unlike conventional optical structures, these diffractive optical elements can provide simultaneous control of spectral and spatial profiles of light. However, the inverse design of such a diffractive optical element is time-consuming with current algorithms, and the designs generally lack experimental validation. Here, we develop a neural network model to experimentally design and validate SpliCons; a special type of diffractive optical element that can achieve spectral splitting and simultaneous concentration of broadband light. We use neural networks to exploit nonlinear operations that result from wavefront reconstruction through a phase plate. Our results show that the neural network model yields enhanced spectral splitting performance for phase plates with quantitative assessment compared to phase plates that are optimized via the local search optimization algorithm. The capabilities of the phase plates optimized via the neural network are experimentally validated by comparing the intensity distribution at the output plane. Once the neural networks are trained, we manage to design SpliCons with 96.6% ± 2.3% accuracy within 2 s, which is orders of magnitude faster than iterative search algorithms. We openly share the fast and efficient framework that we develop in order to contribute to the design and implementation of diffractive optical elements that can lead to transformative effects in microscopy, spectroscopy, and solar energy applications.
Highlights
Miniaturized optical elements is an advancing research field aimed to reduce the size, weight, and cost of optical systems while in the meantime enhancing the performance in a variety of application areas such as controlling the phase, polarization1 and absorption2 of light beams in a medium that provides superior performance in spectroscopy,3 sensing,4 solar energy harvesting,5 wavelength demultiplexing,6 particle tracking,7 imaging,8 image classification,9 and quantum computing applications
The phase plate that allows us to disperse the broadband light is presented in Fig. 3(a), and it is scitation.org/journal/app the ground-truth phase plate
We presented the design of SpliCons using a neural network model
Summary
Miniaturized optical elements is an advancing research field aimed to reduce the size, weight, and cost of optical systems while in the meantime enhancing the performance in a variety of application areas such as controlling the phase, polarization and absorption of light beams in a medium that provides superior performance in spectroscopy, sensing, solar energy harvesting, wavelength demultiplexing, particle tracking, imaging, image classification, and quantum computing applications. One of the promising optical elements is phase plates, which provide control over intensity, polarization, and phase distribution of light with a high degree of freedom. Their outperforming functionalities are especially required in spectrally splitting broadband light as conventional lenses lack control in the spectral domain. during the designing of the phase plates, a high number of optimization parameters result in a long computation time that seriously hampers their implementation.17Spectral and spatial dispersion of broadband light finds diverse application areas such as microscopy, digital imaging, projection, and solar energy. With the rise in energy demand, intelligent conversion of solar energy is becoming more of a necessity to be addressed fundamentally. One of the promising optical elements is phase plates, which provide control over intensity, polarization, and phase distribution of light with a high degree of freedom.. One of the promising optical elements is phase plates, which provide control over intensity, polarization, and phase distribution of light with a high degree of freedom.11–15 Their outperforming functionalities are especially required in spectrally splitting broadband light as conventional lenses lack control in the spectral domain.. Unlike conventional diffractive optical elements that are generally designed for one task, SpliCons provide simultaneous spectral splitting and concentration of light.. Unlike conventional diffractive optical elements that are generally designed for one task, SpliCons provide simultaneous spectral splitting and concentration of light.15 These multi-functional structures can be optimized with iterative approaches. The neural network architecture of deep learning could figure out the one-to-many mapping problem faced in the inverse design of SpliCons and provide fast and accurate control over light beams
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