Defect Parameters Contour Mapping: A Powerful Tool for Lifetime Spectroscopy Data Analysis

Abstract

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 Et 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.

Very thin crystalline silicon cells: A way to improve the photovoltaic performance at elevated temperatures

“…In contrast, T dependences of bulk and surface SRH recombination is more complex, because it depends on the type of the relevant recombination center (carrier trap state) with multiple parameters (density, energy level, capture cross sections, etc.). [43][44][45][46] T dependence of the τ eff in SHJ cells, which are the main focus of this work, has been reported by several groups. 7,14,47 Besides, we also observe an increase of τ eff with the increase in T, as shown later in Section 4.5.…”

Review of injection dependent charge carrier lifetime spectroscopy

“…A different method of visualization for the defect parameter solution surface has been proposed by Bernardini et al [103][104][105]. In this method (named as defect parameter contour map (DPCM)), a discrete space of (E t , k) combinations is created.…”

Deep Learning Extraction of the Temperature-Dependent Parameters of Bulk Defects

“…The true defect parameter lies at the intersection of all these curves. Due to the presence of noise in TIDLS measurements, at least two potential solutions are commonly identified (usually one in the upper half bandgap and one in the lower half bandgap). ,, The DPSS method, referred to as the “traditional” approach, has been refined over the years: linearization of the SRH equation under specific conditions has been proposed to facilitate the fitting procedure; , faster convergence techniques, such as the Newton–Raphson method, have been adapted; and defect parameter contour mapping was developed to visualize the potential solutions . Recently, a machine learning (ML) based framework was introduced to analyze TIDLS measurements. , Multiple ML algorithms were trained to successfully predict the defect parameters (regression) and the half-bandgap location (classification).…”