Premium
Defect Parameters Contour Mapping: A Powerful Tool for Lifetime Spectroscopy Data Analysis
Author(s) -
Bernardini Simone,
Naerland Tine U.,
Coletti Gianluca,
Bertoni Mariana I.
Publication year - 2018
Publication title -
physica status solidi (b)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.51
H-Index - 109
eISSN - 1521-3951
pISSN - 0370-1972
DOI - 10.1002/pssb.201800082
Subject(s) - a priori and a posteriori , characterization (materials science) , computer science , spectroscopy , visualization , limiting , silicon , photovoltaic system , coupling (piping) , biological system , materials science , data mining , physics , mechanical engineering , nanotechnology , engineering , optoelectronics , philosophy , epistemology , quantum mechanics , biology , metallurgy , electrical engineering
Temperature‐ and injection‐dependent lifetime spectroscopy (TIDLS) is extensively used for the characterization of defects in silicon material for photovoltaic applications. By coupling TIDLS measurements with Shockley–Read–Hall recombination models, the most important defects’ parameters can be assessed including the defect energy level E t and the capture cross section ratio k . However, while proving extremely helpful in a variety of studies aiming at the characterization of contaminated silicon, a generalized approach for the analysis of industrially‐relevant material has not yet emerged. In this contribution, we examine in detail the recently introduced defect parameters contour mapping (DPCM) methodology for TIDLS data analysis as a tool for direct visualization of possible lifetime limiting defects. Herein, we showcase the DPCM method's potential by applying it to two representative case studies selected from literature and we demonstrate that, even when data are scarce, invaluable information is obtained in an easy and intuitive way without any a priori assumption needed. We then apply the DPCM method to simulated TIDLS data to evaluate the general characteristics of its response and the optimal conditions for its application. This analysis proves that the temperature dependence of lifetime is the most critical information required toward a really univocal identification of metal impurities.