The ternary organic solar cell is a promising technology towards high power conversion efficiency.

Black silicon (b-Si) nanotextures are of interest for Si solar cells because of their enhanced light trapping properties. However, the wide range of complex nanotextured b-Si surface morphologies makes a systematic investigation of b-Si solar cells challenging. A comprehensive performance review is necessary to determine the promising b-Si nanotextures for solar cell applications. In this work, we use artificial-intelligence approaches to assist in compiling a systematic and highly refined performance review of b-Si solar cells. We also perform numerical simulations of electrical properties for various nanotextured b-Si morphologies. We find that the weighted average reflectance (WAR) is an effective surface morphology metric for a wide range of surface textures. By correlating solar cell performance parameters to WAR, we show that multicrystalline Si solar cell efficiency can be improved with b-Si nanotexturing, and this is predominately attributed to an increase in short-circuit current density via the blue response improvement. We also show that some b-Si nanotextures can improve the performance of monocrystalline Si solar cells. Device simulations show that the electrical performance of hierarchical (combination of microtexture and nanotexture) and inverted-pyramidal b-Si nanotextures and microtextures can be comparable to or even better than random pyramids. As such, these textures show great potential for monocrystalline Si solar cells.

Phosphorous dopant diffusion profiles feature in many silicon semiconductor devices, including the vast majority of silicon solar cells. Accurate spatially resolved dopant profiling is crucial for understanding the performance of these diffused regions, however, it is very challenging to obtain such profiles in non‐planar samples. Scanning electron microscopy for dopant contrast imaging (SEMDCI), where the secondary electron (SE) image contrast is used to determine the dopant level of a semiconductor sample, is an ideal candidate for Si dopant profiling, especially for silicon samples with surface nanotexturing or black silicon (BSi) technology. However, in previous SEMDCI studies, the dopant concentration of heavily doped n‐type layers in silicon samples have shown a poor correlation with the SE signal contrast. In this work, 1) good contrast for n‐type diffused silicon without contrast‐enhancing techniques; 2) a new contrast definition to account for imaging non‐uniformities; 3) clear correlations between SE contrast and sample work function for phosphorus‐diffused planar silicon specimens across a wide range of emitter profiles; 4) implementation of an empirical baseline correction to normalize scanning electron microscopy image condition variations, are presented. This SEMDCI method is subsequently used for the first time to obtain 2D electron concentration maps for both planar and BSi samples.

photodiodes, photodetectors, and photovoltaic (PV) devices. A particular branch of silicon material is black silicon (BSi), the surface of which is specially processed to create a micro-/nanoscale texture. [1,2] As such, the optical performance of BSi is superior to the unprocessed silicon wafer with a planar surface, with extremely high optical absorption and low reflectance over a broad spectral range. However, due to the complex nature of the BSi surface structure, adapting the academic level BSi into a commercial device is challenging. For example, BSi with an extremely high aspect ratio will present challenges for making acceptable screen-printed contacts as used in silicon solar cells. [3] Furthermore, the increased surface area can result in inferior surface passivation. [4][5][6] Therefore, the state-of-the-art commercialized BSi devices are typically compromised to a less aggressive surface structure with nonoptimal optical properties. [7,8] Semiconductor device fabrication involves multistep physical and chemical manufacturing process sequences. [9] Each of the variables involved in multistep processing could potentially bring uncertainties during manufacturing. As such, modeling and simulation are well-accepted concepts in the semiconductor industry. They provide guidelines for the optimal parameters for actual device fabrication and help researchers to rapidly understand Black silicon (BSi) is a branch of silicon material whose surface is specially processed to a micro/nanoscale structure, which can achieve ultra-low reflectance or ultra-high electrochemical reactivity. The diversity and complex surface structures of BSi make it challenging to commercialize BSi devices. Modeling and simulation are commonly used in the semiconductor industry to help in better understanding the material properties, predict the device performance, and provide guidelines for fabrication parameters' optimization. The biggest challenge for BSi device modeling and simulation is obtaining accurate input surface morphological data. In this work, the 3D models of challenging BSi textures are compared as obtained by atomic force microscopy (AFM) and plasma focused ion beam (PFIB) tomography techniques. In previous work, the PFIB tomography workflow toward the application of surface topography is optimized. In this work, the 3D models obtained from both AFM and PFIB are comprehensively compared, by using the surface models as inputs for finite-difference time-domain-based optical simulation. The results provide strong evidence that PFIB tomography is a better choice for characterizing highly roughened surface such as BSi and provides surface 3D models with better reliability and consistency.