Computational imaging concerns the joint design of physical hardware with image post-processing algorithms. The data recorded by the image sensor may be garbled, but as long as it contains the necessary information, the final image can be reconstructed with image post-processing. The advantages of computational imaging are reduced hardware costs and the possible recovery of quantities difficult to image directly, such as depth and the phase of light. We optimize the end-to-end computational imaging pipeline with deep learning, in order to surpass existing imaging tradeoffs among speed, resolution, and field-of-view.
Thermophotovoltaics (TPV) convert radiative heat to electricity with photovoltaics cells. This is a generalization of solar photovoltaics, which convert the radiation of the sun to electricity. TPV has the potential for high-efficiency electrical power generation from the combustion of fuel. TPV can also be used for converting waste heat or stored thermal energy to electricity. Recently, we have achieved 28.8% efficiency of heat to electricity conversion.
We proposed low-index intermediate "mirrors" to increase the efficiency of multi-bandgap solar cells. This theoretical result was validated with a 38.8% efficiency 4-bandgap solar cell developed at the National Renewable Energy Laboratory, setting the efficiency record for flat-plate solar cells.
Solar cells are routinely textured in order to increase light absorption and efficiency. The benefits of texturing are generally evaluated using ray optics approximations. These approximations fail when the solar cell thickness is on the order of the wavelength of light. We use inverse design and shape calculus to optimize surface textures for subwavelength-thick solar cells. We show that with optimized designs we can approach ray-optics limits for these thin cells.