Abstract: A high dynamic range image sensors enabled by integrating broadband optical filters with individual sensor pixels of a pixel array. The broadband optical filters are formed of engineered micro or nanostructures that exhibit large differences in transmittance, e.g. up to 5 to 7 orders of magnitude. Such high transmittance difference can be achieved by using a single layer of individually designed filters, which show varied transmittance as a result of the distinct absorption of various material and structures. The high transmittance difference can also be achieved by controlling the polarization of light and using polarization-sensitive structures as filters. With the presence of properly designed integrated nanostructures, broadband transmission spectrum with transmittance spanning several orders of magnitude can be achieved. This enables design and manufacturing of image sensors with high dynamic range which is crucial for applications including autonomous driving and surveillance.

Abstract: A high dynamic range image sensors enabled by integrating broadband optical filters with individual sensor pixels of a pixel array. The broadband optical filters are formed of engineered micro or nanostructures that exhibit large differences in transmittance, e.g. up to 5 to 7 orders of magnitude. Such high transmittance difference can be achieved by using a single layer of individually designed filters, which show varied transmittance as a result of the distinct absorption of various material and structures. The high transmittance difference can also be achieved by controlling the polarization of light and using polarization-sensitive structures as filters. With the presence of properly designed integrated nanostructures, broadband transmission spectrum with transmittance spanning several orders of magnitude can be achieved. This enables design and manufacturing of image sensors with high dynamic range which is crucial for applications including autonomous driving and surveillance.

Abstract: A spectral sensing system includes an array of sampling optical elements (e.g. filters) and an array of optical sensors, and a deep learning model used to process output of the optical sensor array. The deep learning model is trained using a training dataset which includes, as training inputs, optical sensor output generated by shining each one of multiple different light waves with known spectra on the sampling optical element array for multiple different times, each time from a known angle of incidence, and as training labels, the known spectra of the multiple light waves. The trained deep learning model can be used to process output of the optical sensor array when light having an unknown spectrum is input on the sampling optical element array from unknown angles, to measure the spectrum of the light. The spectral sensing system can also be used to form a hyperspectral imaging system.

Abstract: A spectral sensing system includes an array of sampling optical elements (e.g. filters) and an array of optical sensors, and a deep learning model used to process output of the optical sensor array. The deep learning model is trained using a training dataset which includes, as training inputs, optical sensor output generated by shining each one of multiple different light waves with known spectra on the sampling optical element array for multiple different times, each time from a known angle of incidence, and as training labels, the known spectra of the multiple light waves. The trained deep learning model can be used to process output of the optical sensor array when light having an unknown spectrum is input on the sampling optical element array from unknown angles, to measure the spectrum of the light. The spectral sensing system can also be used to form a hyperspectral imaging system.

Abstract: A high dynamic range image sensors enabled by integrating broadband optical filters with individual sensor pixels of a pixel array. The broadband optical filters are formed of engineered micro or nanostructures that exhibit large differences in transmittance, e.g. up to 5 to 7 orders of magnitude. Such high transmittance difference can be achieved by using a single layer of individually designed filters, which show varied transmittance as a result of the distinct absorption of various material and structures. The high transmittance difference can also be achieved by controlling the polarization of light and using polarization-sensitive structures as filters. With the presence of properly designed integrated nanostructures, broadband transmission spectrum with transmittance spanning several orders of magnitude can be achieved. This enables design and manufacturing of image sensors with high dynamic range which is crucial for applications including autonomous driving and surveillance.

Abstract: Optical sensing devices employing light intensity detectors integrated with nanostructures. In some embodiments, the nanostructures are 3D nanostructures having feature sizes in all three dimensions comparable to a wavelength range of the incident light, and are used for hyperspectral sensing. In some other embodiments, the nanostructures are simultaneous sensitive to both the spectrum and one or more of polarization, angle and phase information of the incident light field, to provide multi-modal optical sensing devices. In some other embodiments, each spatial pixel of an image sensor includes a group of sampling pixels configured for hyperspectral sensing and another group of sampling pixels configured for sensing polarization, angle or phase of the incident light.

Abstract: A system and method for detecting defects in an object includes illuminating the object with a coherent light, recording the a speckle pattern of the coherent light reflected and/or scattered and/or transmitted from the object, and analyzing the speckle pattern using a trained artificial neural network to determine whether defects are present in the object and the types of defects. To train the neural network, sample objects having known types of defects or no defects are illuminated with a coherent light and the speckle patterns are recorded. The speckle patterns are labeled with the type of defects in the corresponding sample objects, and used as training data to train the network. The technique analyzes the speckle patterns directly, and does not require phase recovery and object shape reconstruction. The technique is useful for defect inspection in industrial production to detect defects such as scratches, air bubbles, deformation, stains, etc.