Angular constraint on light-trapping absorption enhancement in solar cells

Light trapping for solar cells can reduce production cost and improve energy conversion efficiency. Understanding some of the basic theoretical constraints on light trapping is therefore of fundamental importance. Here, we develop a general angular constraint on the absorption enhancement in light trapping. We show that there is an upper limit for the angular integration of absorption enhancement factors. This limit is determined by the number of accessible resonances supported by an absorber

Sparsity for Ultrafast Material Identification

Mid-infrared spectroscopy is often used to identify material. Thousands of spectral points are measured in a time-consuming process using expensive table-top instrument. However, material identification is a sparse problem, which in theory could be solved with just a few measurements. Here we exploit the sparsity of the problem and develop an ultra-fast, portable, and inexpensive method to identify materials. In a single-shot, a mid-infrared camera can identify materials based on their spectroscopic signatures. This method does not require prior calibration, making it robust and versatile in handling a broad range of materials

Training deep neural networks for the inverse design of nanophotonic structures

Data inconsistency leads to a slow training process when deep neural networks are used for the inverse design of photonic devices, an issue that arises from the fundamental property of non-uniqueness in all inverse scattering problems. Here we show that by combining forward modeling and inverse design in a tandem architecture, one can overcome this fundamental issue, allowing deep neural networks to be effectively trained by data sets that contain non-unique electromagnetic scattering instances. This paves the way for using deep neural networks to design complex photonic structures that requires large training sets