Reading Responses To Journal Articles, Computational Emulation Of Published Research

Students responded to sets of journal articles in computational optics and imaging every week. Articles investigated scientific questions, visualization of scientific data, ethical questions, and international collaborative projects (such as the Event Horizon Telescope). Students also completed labs to gain proficiency in computational tools

Computational Optics (ENGR 030) Syllabus

This course provides an introduction to computational optics and imaging, where camera hardware is co-designed with processing algorithms. Topics may include: geometrical and wave optics, PSF engineering, light field imaging, compressed sensing, time-of-flight imaging, Fourier optics, super-resolution, medical imaging, and virtual and augmented reality. Students will also investigate ethical and artistic questions in image collection and processing

Intermediate Mirrors to Reach Theoretical Efficiency Limits of Multi-Bandgap Solar Cells

Creating a single bandgap solar cell that approaches the Shockley-Queisser limit requires a highly reflective rear mirror. This mirror enhances the voltage of the solar cell by providing photons with multiple opportunities for escaping out the front surface. Efficient external luminescence is a pre-requisite for high voltage. Intermediate mirrors in a multijunction solar cell can enhance the voltage for each cell in the stack. These intermediate mirrors need to have the added function of transmitting the below bandgap photons to the next cell in the stack. In this work, we quantitatively establish the efficiency increase possible with the use of intermediate selective reflectors between cells in a tandem stack. The absolute efficiency increase can be up to ~6% in dual bandgap cells with optimal intermediate and rear mirrors. A practical implementation of an intermediate selective mirror is an air gap sandwiched by antireflection coatings. The air gap provides perfect reflection for angles outside the escape cone, and the antireflection coating transmits angles inside the escape cone. As the incoming sunlight is within the escape cone, it is transmitted on to the next cell, while most of the internally trapped luminescence is reflected

Light Trapping Textures Designed by Electromagnetic Optimization for Sub-Wavelength Thick Solar Cells

Light trapping in solar cells allows for increased current and voltage, as well as reduced materials cost. It is known that in geometrical optics, a maximum 4n^2 absorption enhancement factor can be achieved by randomly texturing the surface of the solar cell, where n is the material refractive index. This ray-optics absorption enhancement limit only holds when the thickness of the solar cell is much greater than the optical wavelength. In sub-wavelength thin films, the fundamental questions remain unanswered: (1) what is the sub-wavelength absorption enhancement limit and (2) what surface texture realizes this optimal absorption enhancement? We turn to computational electromagnetic optimization in order to design nanoscale textures for light trapping in sub-wavelength thin films. For high-index thin films, in the weakly absorbing limit, our optimized surface textures yield an angle- and frequency-averaged enhancement factor ~39. They perform roughly 30% better than randomly textured structures, but they fall short of the ray optics enhancement limit of 4n^2 ~ 50

Optimal Physical Preprocessing for Example-Based Super-Resolution

In example-based super-resolution, the function relating low-resolution images to their high-resolution counterparts is learned from a given dataset. This data-driven approach to solving the inverse problem of increasing image resolution has been implemented with deep learning algorithms. In this work, we explore modifying the imaging hardware in order to collect more informative low-resolution images for better ultimate high-resolution image reconstruction. We show that this "physical preprocessing" allows for improved image reconstruction with deep learning in Fourier ptychographic microscopy. Fourier ptychographic microscopy is a technique allowing for both high resolution and high field-of-view at the cost of temporal resolution. In Fourier ptychographic microscopy, variable illumination patterns are used to collect multiple low-resolution images. These low-resolution images are then computationally combined to create an image with resolution exceeding that of any single image from the microscope. We use deep learning to jointly optimize the illumination pattern with the post-processing reconstruction algorithm for a given sample type, allowing for single-shot imaging with both high resolution and high field-of-view. We demonstrate, with simulated data, that the joint optimization yields improved image reconstruction as compared with sole optimization of the post-processing reconstruction algorithm

Illumination Pattern Design With Deep Learning For Single-Shot Fourier Ptychographic Microscopy

Fourier ptychographic microscopy allows for the collection of images with a high space-bandwidth product at the cost of temporal resolution. In Fourier ptychographic microscopy, the light source of a conventional widefield microscope is replaced with a light-emitting diode (LED) matrix, and multiple images are collected with different LED illumination patterns. From these images, a higher-resolution image can be computationally reconstructed without sacrificing field-of-view. We use deep learning to achieve single-shot imaging without sacrificing the space-bandwidth product, reducing the acquisition time in Fourier ptychographic microscopy by a factor of 69. In our deep learning approach, a training dataset of high-resolution images is used to jointly optimize a single LED illumination pattern with the parameters of a reconstruction algorithm. Our work paves the way for high-throughput imaging in biological studies

Ultraefficient Thermophotovoltaic Power Conversion By Band-Edge Spectral Filtering

Thermophotovoltaic power conversion utilizes thermal radiation from a local heat source to generate electricity in a photovoltaic cell. It was shown in recent years that the addition of a highly reflective rear mirror to a solar cell maximizes the extraction of luminescence. This, in turn, boosts the voltage, enabling the creation of record-breaking solar efficiency. Now we report that the rear mirror can be used to create thermophotovoltaic systems with unprecedented high thermophotovoltaic efficiency. This mirror reflects low-energy infrared photons back into the heat source, recovering their energy. Therefore, the rear mirror serves a dual function; boosting the voltage and reusing infrared thermal photons. This allows the possibility of a practical \u3e50% efficient thermophotovoltaic system. Based on this reflective rear mirror concept, we report a thermophotovoltaic efficiency of 29.1 ± 0.4% at an emitter temperature of 1,207 °C