Cascaded diffractive optical elements for improved multiplane image reconstruction

Gulses, A. Alkan; Jenkins, B. Keith

doi:10.1364/ao.52.003608

Cited by 16 publications

(10 citation statements)

References 25 publications

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“…A popular approach is to use look-up tables15,22, which is limited to reconstructing simple, low-resolution images. Projection quality can be improved with cascaded diffractive elements31, which is a costly and overly complicated method. While projections of up to several tens of planes have been demonstrated17, it was only for a single dot in each plane and could not be obtained simultaneously, but had to be created sequentially.…”

mentioning

confidence: 99%

Breaking crosstalk limits to dynamic holography using orthogonality of high-dimensional random vectors

et al. 2019

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Holography is the most promising route to true-to-life 3D projections, but the incorporation of complex images with full depth control remains elusive. Digitally synthesised holograms 1 – 7 , which do not require real objects to create a hologram, offer the possibility of dynamic projection of 3D video 8 , 9 . Extensive efforts aimed 3D holographic projection 10 – 17 , however available methods remain limited to creating images on a few planes 10 – 12 , over a narrow depth-of-field 13 , 14 or with low resolution 15 – 17 . Truly 3D holography also requires full depth control and dynamic projection capabilities, which are hampered by high crosstalk 9 , 18 . The fundamental difficulty is in storing all the information necessary to depict a complex 3D image in the 2D form of a hologram without letting projections at different depths contaminate each other. Here, we solve this problem by preshaping the wavefronts to locally reduce Fresnel diffraction to Fourier holography, which allows inclusion of random phase for each depth without altering image projection at that particular depth, but eliminates crosstalk due to near-orthogonality of large-dimensional random vectors. We demonstrate Fresnel holograms that form on-axis with full depth control without any crosstalk, producing large-volume, high-density, dynamic 3D projections with 1000 image planes simultaneously, improving the state-of-the-art 12 , 17 for number of simultaneously created planes by two orders of magnitude. While our proof-of-principle experiments use spatial light modulators, our solution is applicable to all types of holographic media.

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confidence: 99%

Breaking crosstalk limits to dynamic holography using orthogonality of high-dimensional random vectors

et al. 2019

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“…Thus owing to the excellent Bragg selectivity properties of the CGVH, the total SBWP of this system is SBWP = 2.2 × 10 9 . This is 3 orders of magnitude higher than the current state of the art in multiplexed cascaded CGH systems [50], where a system with SBWP = 2.6 × 10 6 was demonstrated. In this case, 2 holograms were encoded in a system of CGHs, each with discreet levels of quantization C = 64, pixel sizes of (∆x = 5 µm, ∆y = 5 µm, ∆z = 0.3 mm) and a hologram size of (L x = 320 µm, L y = 320 µm, L z = 1.5 mm).…”

Section: Dynamic Wave Field Synthesis: Angular Multiplexing and Supermentioning

confidence: 77%

“…This limitation has led to an ever increasing demand of developing 3D holographic optical elements. For instance, stratified CGHs in a cascaded setup have been proposed [49] and it has been shown that by adding more degrees of freedom they expand the system's solution space thus facilitating both angular and frequency multiplexing [50,51]. Another example is presented by thick volume holograms [52].…”

Section: Introductionmentioning

confidence: 99%

Dynamic wave field synthesis: enabling the generation of field distributions with a large space-bandwidth product

Kamau¹,

Heine²,

Falldorf³

et al. 2015

Opt. Express

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We present a novel approach for the design and fabrication of multiplexed computer generated volume holograms (CGVH) which allow for a dynamic synthesis of arbitrary wave field distributions. To achieve this goal, we developed a hybrid system that consists of a CGVH as a static element and an electronically addressed spatial light modulator as the dynamic element. We thereby derived a new model for describing the scattering process within the inhomogeneous dielectric material of the hologram. This model is based on the linearization of the scattering process within the Rytov approximation and incorporates physical constraints that account for voxel based laser-lithography using micro-fabrication of the holograms in a nonlinear optical material. In this article we demonstrate that this system basically facilitates a high angular Bragg selectivity on the order of 1°. Additionally, it allows for a qualitatively low cross-talk dynamic synthesis of predefined wave fields with a much larger space-bandwidth product (SBWP ≥ 8.7 × 10(6)) as compared to the current state of the art in computer generated holography.

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“…Liao et al [ 28 ] studied terahertz reconfigurable holographic imaging by mechanical translation of an absorber relative to two fixed discrete dielectric DOEs. Cascaded computer‐generated phase‐only diffractive optical elements for multiplane image formation were simulated by Alkan et al [ 29 ] A lens was utilized in such a cascaded configuration for Fourier transform to project patterns on two imaging planes. In the visible range, Wang et al [ 30 ] demonstrated azimuthal multiplexing with a stratified DOE layout optimized by iterative optimization algorithms.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Enables Multi‐Degree‐of‐Freedom Reconfigurable Terahertz Holograms with Cascaded Diffractive Optical Elements

Jia

Lin

Sensale‐Rodriguez

2023

Advanced Optical Materials

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In this context, terahertz holograms enabled by single-layer diffractive optical elements have been widely explored. [11,18,19,26] However, the reconfigurability of single-layer passive diffractive optical elements is very limited. Cascaded DOE structures have the advantage of enabling more functionality and information capacity compared to single-layer DOEs, as discussed in ref. [27]. Liao et al. [28] studied terahertz reconfigurable holographic imaging by mechanical translation of an absorber relative to two fixed discrete dielectric DOEs. Cascaded computer-generated phase-only diffractive optical elements for multiplane image formation were simulated by Alkan et al. [29] A lens was utilized in such a cascaded configuration for Fourier transform to project patterns on two imaging planes. In the visible range, Wang et al. [30] demonstrated azimuthal multiplexing with a stratified DOE layout optimized by iterative optimization algorithms. The optical information was encoded azimuthally in the DOEs and retrieved by rotating the DOEs relative to each other. It is to highlight that reconfigurable materials such as phase change materials, semiconductor layers, and MEMS structures integrated with metasurface structures [31][32][33][34][35][36] are also good candidates to realize reconfigurable terahertz devices and holograms.Diffractive optical neural networks (DONN) [12] have been successfully applied for the classification of handwritten digit images, [12,37] the design of spatially controlled wavelength demultiplexing systems, [38] and terahertz pulse shaping. [39] Such a machine learning method is an efficient and powerful tool for designing diffractive optics inverse problems in cascaded configurations. The pixel phase (height distributions) in the diffractive layers can be trained by stochastic gradient descent and error-backpropagation to achieve the desired diffraction pattern as the incident beam passes through the diffractive layers.The purpose of this work is to show that through such a DONN machine learning method, it is possible to realize holograms with a tailored reconfigurable operation when altering multiple degrees of freedom, i.e., either the number, spacing, rotational alignment, and/or order of the cascaded diffractive layers. The height distributions of the diffractive layers are trained by the network on the basis of an initial incident terahertz field distribution and predefined (target) diffraction patterns.For our proof-of-concept demonstration, five scenarios are demonstrated, each corresponding to the variation of an individual degree of freedom in the cascaded configuration. A Machine learning can empower the design of cascaded diffractive optical elements (DOEs) at terahertz frequencies enabling the realization of holograms with a tailored multi-degree-of-freedom reconfigurable operation when altering either the number, spacing, rotational alignment, and/or order of the elements. This unprecedented control over the spatial terahertz light distribution can profoundly impact multiple terahe...

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Cascaded diffractive optical elements for improved multiplane image reconstruction

Cited by 16 publications

References 25 publications

Breaking crosstalk limits to dynamic holography using orthogonality of high-dimensional random vectors

Breaking crosstalk limits to dynamic holography using orthogonality of high-dimensional random vectors

Dynamic wave field synthesis: enabling the generation of field distributions with a large space-bandwidth product

Machine Learning Enables Multi‐Degree‐of‐Freedom Reconfigurable Terahertz Holograms with Cascaded Diffractive Optical Elements

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