We present subwavelength imaging of amplitude- and phase-encoded objects based on a solid-immersion diffractive processor designed through deep learning. Subwavelength features from the objects are resolved by the collaboration between a jointly-optimized diffractive encoder and decoder pair. We experimentally demonstrated the subwavelength-imaging performance of solid immersion diffractive processors using terahertz radiation and achieved all-optical reconstruction of subwavelength phase features of objects (with linewidths of ~λ/3.4, where λ is the wavelength) by transforming them into magnified intensity images at the output field-of-view. Solid-immersion diffractive processors would provide cost-effective and compact solutions for applications in bioimaging, sensing, and material inspection, among others.
We present a diffractive terahertz sensor using a single-pixel detector to rapidly sense hidden defects within a target sample volume. Leveraging multiple spatially-engineered diffractive layers optimized via deep learning, this diffractive sensor can all-optically process the sample scattered waves and generate an output spectrum encoding information for indicating the presence/absence of hidden defects. We experimentally validated this framework using a single-pixel terahertz time-domain spectroscopy set-up and 3D-printed diffractive layers, successfully detecting unknown hidden defects within silicon samples. By circumventing raster scanning and digital image formation/reconstruction, this framework holds vast potential for various applications requiring high-throughput, non-destructive defect detection.
We present a plasmonic photoconductive terahertz focal-plane array that can provide spatial amplitude and phase, ultrafast temporal and spectral information simultaneously with a high imaging speed. Utilizing the high dynamic range (> 60 dB) and wide bandwidth (> 3 THz) in all focal-plane array pixels, we demonstrate super-resolution imaging on partially-etched high-resistivity silicon objects and reconstruct both the 2D shape and depth with a 16-fold enhancement in the space-bandwidth product and an effective number of pixels larger than 1-kilo pixels. We also demonstrate a 16-fps terahertz time-domain video, which can be further super-resolved by a holographic algorithm.
We report an all-optical object classification framework using a single-pixel diffractive network and spectrum encoding, classifying unknown objects through unknown random phase diffusers at the speed of light. Using this single-pixel diffractive network design, we numerically achieved a blind testing accuracy of 88.53%, classifying unknown handwritten digits through 80 unknown random diffusers that were never used during training. This framework presents a time- and energy-efficient all-optical solution for directly sensing through unknown random diffusers using a single pixel and will be of broad interest to various fields, such as security, biosensing and autonomous driving.
We present a terahertz focal-plane array comprised of ~0.3 million plasmonic nano-antennas that can generate ultrafast temporal and hyperspectral terahertz images with more than 3 THz bandwidth and 60 dB signal-to-noise ratio. Utilizing the rich spectral information of the focal-plane array, a deep convolutional neural network can be trained to super-resolve images of objects. As the first proof-of-concept, a 16-fold resolution enhancement with more than a 1-kilo effective pixel count is accomplished on etched silicon objects with subwavelength thickness variations. We also resolve terahertz videos of dynamic objects at 16 frames per second with the spectral information preserved.
We report a single-pixel machine vision framework based on deep learning-designed diffractive surfaces to perform a desired machine learning task. The object within the input field-of-view is illuminated with a broadband light source and the subsequent diffractive surfaces are trained to encode the spatial information of the object features onto the power spectrum of the diffracted light that is collected by a single-pixel detector in a single-shot. We experimentally demonstrated the all-optical inference capabilities of this single-pixel machine vision platform by classifying handwritten digits using 3D-printed diffractive layers and a plasmonic nanoantenna-based time-domain spectroscopy setup operating at THz wavelengths.
Most state-of-the-art terahertz time-domain imaging technologies are based on single-pixel systems, which mechanically scan either the imaging object or the terahertz system, limiting the imaging speed. We present a new terahertz time-domain imaging modality using a terahertz photoconductive focal-plane array. The focal-plane array consists of plasmonic nano-antenna arrays on an LT-GaAs substrate. The dynamic range of a single pixel can reach up to 75 dB with more than a 4 THz bandwidth. We demonstrate clear terahertz images up to 2.5 THz. We also demonstrate that the focal-plane array can operate at video-rate imaging speeds.
Using deep learning-based training of diffractive layers we designed single-pixel machine vision systems to all-optically classify images by maximizing the output power of the wavelength corresponding to the correct data-class. We experimentally validated our diffractive designs using a plasmonic nanoantenna-based time-domain spectroscopy setup and 3D-printed diffractive layers to successfully classify the images of handwritten-digits using a single-pixel and snap-shot illumination. Furthermore, we trained a shallow electronic neural network as a decoder to reconstruct the images of the input objects, solely from the power detected at ten distinct wavelengths, also demonstrating the success of this platform as a task-specific, single-pixel imager.
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