An Artificial-Intelligence-Assisted Investigation on the Potential of Black Silicon Nanotextures for Silicon Solar Cells
Shaozhou Wang, Tong Xie, Ran Liang, Yu Zhang, Fa‐Jun Ma, D.N. Payne, Giuseppe Scardera, Bram Hoex
Abstract
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.