The Two Approaches of Surface-Texture Optimization in Thin-Film Silicon Solar Cells

Textured interfaces in thin-film silicon solar cells can increase the photocurrent of the devices. However, the challenge is how to design textures (periodic or random) that could outperform the state-of-the-art random ones. Moreover, the textures should enable defect-less semiconductor layer growth, which has often been neglected in the past, resulting in the low open-circuit voltage and fill factor of the devices. In this paper, we present two approaches for the systematic optimization of surface-textures: bottom-up and top-down. Fully 3-D optical simulations, including calibrated nonconformal layer growth model were employed to predict gains in short-circuit current densities in a micromorph solar cell (bottom-up approach) and a single-junction amorphous silicon solar cell (top-down approach). In the bottom-up approach, we start with a simple sinusoidal component and change its shape systematically in the direction of broader valleys, also resulting in better conditions for the layer growth. In the top-down approach, we start from a random texture (morphology fingerprint taken from Asahi U type substrate) and modify it in a spatial frequency domain. We show the role of the presence/absence of different frequency regions as well as the important role of the phase spectrum on the optical characteristics of the device. In both approaches, improved textures have the potential to outperform state-of-the-art random ones, not only from optical point of view but in terms of conversion efficiency as well.