Engineering topics

Sen, Fatih G.

Publications and source records attributed to Sen, Fatih G..

3 records

DOE OSTI2021

Computational design of passivants for CdTe grain boundaries

CdTe is the second most-widely deployed photovoltaic (PV) material due to its high efficiency and low manufacturing costs. Currently, polycrystalline CdTe (poly-CdTe) has a record efficiency of similar to 22%, which is still well below the theoretical limit (similar to 30%). Polycrystalline CdTe films that have incorporated Cl or Se show higher efficiency, possibly due to the segregation of these ions to the grain boundaries (GBs), where they may passivate the dangling bonds. However, the efficiency enhancement mechanisms of passivants in CdTe GBs, and the feasibility of employing alternative passivants, have not been well explored. Here, we present a systematic computational study of CdTe GBs with potential passivants, namely S, P, As, Se, and Sb on Te sites, and Na, Mg, Al, Sc, Cu, and Zn on Cd sites. Density functional theory (DFT) calculations were performed on GB dislocation core structures derived from scanning transmission electron microscopy (STEM) images of a model GB (bicrystal). We computed the segregation thermodynamics, electronic density of states, and charge variations near doped CdTe GBs. We find that segregation of impurities to GBs is thermodynamically favorable. For a Te-terminated core, Se, S, and P on Te sites effectively reduce midgap states. For both Cd-and Te-terminated dislocation cores, Sc and Al reduce midgap states when substituted for Cd atoms. Overall, the greatest improvement was achieved with co-doping, i.e. simultaneously substituting Te with Se and substituting Cd with Cu or Al. The elimination of midgap states is predicted to increase the photovoltaic efficiency of CdTe by reducing the recombination at grain boundaries.

14 SOLAR ENERGY↗

DOE OSTI2020

Machine-learned impurity level prediction for semiconductors: the example of Cd-based chalcogenides

The ability to predict the likelihood of impurity incorporation and their electronic energy levels in semiconductors is crucial for controlling its conductivity, and thus the semiconductor's performance in solar cells, photodiodes, and optoelectronics. The difficulty and expense of experimental and computational determination of impurity levels makes a data-driven machine learning approach appropriate. In this work, we show that a density functional theory-generated dataset of impurities in Cd-based chalcogenides CdTe, CdSe, and CdS can lead to accurate and generalizable predictive models of defect properties. By converting any semiconductor + impurity system into a set of numerical descriptors, regression models are developed for the impurity formation enthalpy and charge transition levels. These regression models can subsequently predict impurity properties in mixed anion CdX compounds (where X is a combination of Te, Se and S) fairly accurately, proving that although trained only on the end points, they are applicable to intermediate compositions. We make machine-learned predictions of the Fermi-level-dependent formation energies of hundreds of possible impurities in 5 chalcogenide compounds, and we suggest a list of impurities which can shift the equilibrium Fermi level in the semiconductor as determined by the dominant intrinsic defects. Machine learning predictions for the dominating impurities compare well with DFT predictions, revealing the power of machine-learned models in the quick screening of impurities likely to affect the optoelectronic behavior of semiconductors.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Machine learning defect properties in Cd-based chalcogenides

Impurity energy levels in the band gap can have serious consequences for a semiconductor's performance as a photovoltaic absorber. Data-driven approaches can help accelerate the prediction of point defect properties in common semiconductors, and thus lead to the identification of potential deep lying impurity states. In this work, we use density functional theory (DFT) to compute defect formation energies and charge transition levels of hundreds of impurities in CdX chalcogenide compounds, where X = Te, Se or S. We apply machine learning techniques on the DFT data and develop on-demand predictive models for the formation energy and relevant transition levels of any impurity atom in any site. The trained ML models are general and accurate enough to predict the properties of any possible point defects in any Cd-based chalcogenide, as we prove by testing on a few selected defects in mixed chalcogen compounds CdTe 0.5 Se 0.5 and CdSe 0.5 S 0.5 . The ML framework used in this work can be extended to any class of semiconductors.

density functional theory↗