Abstract:
For the efficient absorption of light in a broad wavelength band, Si photovoltaic devices require a high concentration of metal atoms at a shallow depth up to a few 10s of nm in the Si substrates. Low energy (< 50 keV) implantation of Ag ions in Si is one of the most suitable synthesis steps to facilitate the formation of these metal nanoclusters at the shallow depths in Si. However, during the low energy implantation of the heavy ions, one of the unintended consequences is the sputtering of target atoms particularly if the target is made of lower Z materials such as Si. In this study, we have investigated the re-distribution of atoms in the target layers due to the surface sputtering effects from 50 keV Ag ion implantation in Si substrates. Initially, the implant profile was estimated with the widely used static simulation code, theStopping and Range of Ions in Matter (SRIM). However, it’s simulation routine lacks any consideration of the fluence dependent evolution of the target material. Therefore, we have explored the use of another ion-solid interaction code T-DYN, which considers the dynamic changes in the thickness and/or composition of the target material during the implantation process. For 50 keV Ag ion implantation in Si, the T-DYN simulation predicts the Ag ion depth profile reaches a maximum or saturation in the concentration at a critical ion fluence of ~7×1016 atoms/cm2, whereas for a more heavier element like Au, similar saturation in the concentration is predicted at a relatively lower fluence of ~4×1016 atoms/cm2. The depth profiles of the implanted Ag atoms extracted from experiments utilizing the Rutherford Backscattering Spectrometry and X-ray Photoelectron Spectroscopy characterization techniques show asymmetric distributions with the position of peak concentration depth gradually moving towards the Si surface with increasing implant ion fluence. Once the implantation ion fluence reached a critical value, the peak value of the elemental concentration is seen saturated similar to the predictions from T-DYN simulations.
