Optical array is proposed and widely used in processing, communication, microscopy, storage and other fields. However, the current methods to generate optical array are only effective in specific scenarios. In this work, we present an efficient and general method, which separates iterative calculation and parameter selection by introducing a special mapping space, allowing the generation of optical arrays with arbitrary dimensions, optional parameters and any energy ratios. This method is compared with traditional methods from both simulation and experiment, and is superior in diffraction efficiency, focus amount, and calculation speed. Additionally, a 5-dimentional optical array with specially-designed parameters is produced to show the versatility for different parameters. It shows advantages in beam shaping and parallelization, and will be instrumental for applications in information storage, industrial processing, and three-dimensional imaging.
We propose a redefinable neural network (RediNet), realizing general modulation on diverse structured light arrays through a single approach. Exploiting the information sparsity of the array distribution, a redefinable dimension designation is used in RediNet, removing the burden of processing pixel-wise distributions. The prowess of originally generating arbitraryresolution holographs with fixed network is firstly demonstrated. The versatility is showcased in the generation of 2D/3D foci arrays, Bessel and Airy beams arrays, (perfect) vortex beam arrays, multi-channel compound vortex arrays and even snowflake-intensity arrays with arbitrarily-built phase functions. Considering the fine resolution, high speed, and unprecedented universality, RediNet can serve extensive applications such as next-generation optical communication, parallel laser direct writing, optical traps, and so on.
Neural networks have provided faster and more straightforward solutions for laser modulation. However, their effectiveness when facing diverse structured lights and various output resolutions remains vulnerable because of the specialized end-to-end training and static model. Here, we propose a redefinable neural network (RediNet), realizing customized modulation on diverse structured light arrays through a single general approach. The network input format features a redefinable dimension designation, which ensures RediNet wide applicability and removes the burden of processing pixel-wise light distributions. The prowess of originally generating arbitrary-resolution holograms with a fixed network is first demonstrated. The versatility is showcased in the generation of 2D/3D foci arrays, Bessel and Airy beam arrays, (perfect) vortex beam arrays, and even snowflake-intensity arrays with arbitrarily built phase functions. A standout application is producing multichannel compound vortex beams, where RediNet empowers a spatial light modulator (SLM) to offer comprehensive multiplexing functionalities for free-space optical communication. Moreover, RediNet has the hitherto highest efficiency, only consuming 12 ms (faster than the mainstream SLM framerate of 60 Hz) for a 10002-resolution holograph, which is critical in real-time required scenarios. Considering the fine resolution, high speed, and unprecedented universality, RediNet can serve extensive applications, such as next-generation optical communication, parallel laser direct writing, and optical traps.
In the framework of laser precision machining, spherical aberrations of the laser beam increase gradually along the machining depth, which is widely observed due to the refractive index difference between the material of the working pieces and the surrounding medium. In this paper, we report on a simple and effective approach for spherical-aberration-free 3D beam forming inside the materials. This new technique is based on the modified Ewald cap which is related to the numerical aperture of the objective lens, the machining depth, and the refractive index of the material. This method is verified on a laser machining platform, where the phase loaded on the spatial light modulator is acquired by the modified 3D Gerchberg-Saxton algorithm. In the experiment, we have realized line and helical structures with SA compensation, which demonstrate that customized arbitrary intensity distribution inside the material can be realized.
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