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The development of photonic technologies for machine learning is a promising avenue toward reducing the computational cost of image classification tasks. Here we investigate a convolutional neural network (CNN) where the first layer is replaced by an image sensor array consisting of recently developed angle-sensitive metasurface photodetectors. This array can visualize transparent phase objects directly by recording multiple anisotropic edge-enhanced images, analogous to the feature maps computed by the first convolutional layer of a CNN. The resulting classification performance is evaluated for a realistic task (the identification of transparent cancer cells from seven different lines) through computational-imaging simulations based on the measured angular characteristics of prototype devices. Our results show that this hybrid optoelectronic network can provide accurate classification (>90%) similar to its fully digital baseline CNN but with an order-of-magnitude reduction in the number of calculations.

 

We demonstrate the use of plasmonic gradient metasurfaces to tailor the angular response of generic planar photodetectors. The resulting devices are promising for a wide range of computational imaging applications with enhanced miniaturization and functionality.

 

We use a generative deep learning method based on denoising diffusion probabilistic model to design plasmonic phase-imaging sensors for broadband operation. This flexible method enables optimized inverse design for a wide range of nanophotonic devices.

 

Angle-sensitive photodetectors are a promising device technology for many advanced imaging functionalities, including lensless compound-eye vision, lightfield sensing, optical spatial filtering, and phase imaging. Here we demonstrate the use of plasmonic gradient metasurfaces to tailor the angular response of generic planar photodetectors. The resulting devices rely on the phase-matched coupling of light incident at select geometrically tunable angles into guided plasmonic modes, which are then scattered and absorbed in the underlying photodetector active layer. This approach naturally introduces sharp peaks in the angular response, with smaller footprint and reduced guided-mode radiative losses (and therefore improved spatial resolution and sensitivity) compared to analogous devices based on diffractive coupling. More broadly, these results highlight a promising new application space of flat optics, where gradient metasurfaces are integrated within image sensors to enable unconventional capabilities with enhanced system miniaturization and design flexibility.

 

We report a new technique for single-shot quantitative phase retrieval from transparent objects, based on plasmonic metasurface photodetectors featuring an asymmetric angular response around normal incidence combined with a particularly simple optical setup. 

Abstract The visualization of pure phase objects by wavefront sensing has important applications ranging from surface profiling to biomedical microscopy, and generally requires bulky and complicated setups involving optical spatial filtering, interferometry, or structured illumination. Here we introduce a new type of image sensors that are uniquely sensitive to the local direction of light propagation, based on standard photodetectors coated with a specially designed plasmonic metasurface that creates an asymmetric dependence of responsivity on angle of incidence around the surface normal. The metasurface design, fabrication, and angle-sensitive operation are demonstrated using a simple photoconductive detector platform. The measurement results, combined with computational imaging calculations, are then used to show that a standard camera or microscope based on these metasurface pixels can directly visualize phase objects without any additional optical elements, with state-of-the-art minimum detectable phase contrasts below 10 mrad. Furthermore, the combination of sensors with equal and opposite angular response on the same pixel array can be used to perform quantitative phase imaging in a single shot, with a customized reconstruction algorithm which is also developed in this work. By virtue of its system miniaturization and measurement simplicity, the phase imaging approach enabled by these devices is particularly significant for applications involving space-constrained and portable setups (such as point-of-care imaging and endoscopy) and measurements involving freely moving objects. 

We report the development of angle-sensitive photodetectors based on specially designed metasurfaces that can map the phase distribution of the incident light and visualize transparent phase objects without any external spatial-filtering elements. Pixel arrays of these devices can provide quantitative phase reconstruction in a single shot with state-of-the-art sensitivity. 

We report plasmonic metasurface photodetectors featuring a strong asymmetric angular response around normal incidence that can visualize transparent phase objects with high sensitivity in a simple and compact imaging setup.

 

Centromeres are long, often repetitive regions of genomes that bind kinetochore proteins and ensure normal chromosome segregation. Engineering centromeres that function in vivo has proven to be difficult. Here we describe a tethering approach that activates functional maize centromeres at synthetic sequence arrays. A LexA-CENH3 fusion protein was used to recruit native Centromeric Histone H3 (CENH3) to long arrays of LexO repeats on a chromosome arm. Newly recruited CENH3 was sufficient to organize functional kinetochores that caused chromosome breakage, releasing chromosome fragments that were passed through meiosis and into progeny. Several fragments formed independent neochromosomes with centromeres localized over the LexO repeat arrays. The new centromeres were self-sustaining and transmitted neochromosomes to subsequent generations in the absence of the LexA-CENH3 activator. Our results demonstrate the feasibility of using synthetic centromeres for karyotype engineering applications. 

We use specially designed plasmonic photodetectors to develop a new method for image differentiation that can produce edge-enhanced images without external optical elements and under incoherent illumination, unlike traditional optical spatial filters.