How Much Physics is in a Current–Voltage Curve? Inferring Defect Properties From Photovoltaic Device Measurements

Abstract

Defect-assisted recombination processes are critical to understand, as they frequently limit photovoltaic (PV) device performance. However, the physical parameters governing these processes can be extremely challenging to measure, requiring specialized techniques and sample preparation. And yet the fact that they limit performance as measured by current-voltage (JV) characterization indicates that they must have some detectable signal in that measurement. In this work, we use numerical device models that explicitly account for these parameters alongside high-throughput JV measurements and Bayesian inference to construct probability distributions over recombination parameters, showing the ability to recover values consistent with previously-reported literature measurements. The Bayesian approach enables easy incorporation of data and models from other sources; we demonstrate this with temperature dependence of carrier capture cross-sections. The ability to extract these fundamental physical parameters from standardized, automated measurements on completed devices is promising for both established industrial PV technologies and newer research-stage ones.

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

“…The method was validated with experimental results, where the parameters obtained by the ML-based and DPSS approaches were shown to agree within an acceptable uncertainty range . ML-based regression methods for solar cell parameters to evaluate the impact of the thickness of different layers on the efficiency, , understand material properties, , and understand current–voltage curve analysis ,− were recently published. Regression tasks have also been performed on luminescence images. , Classification methods have mainly revolved around automated image analysis using deep learning algorithms such as convolutional neural networks (CNNs), where the objective is to classify defects − or identify their position in luminescence images. − …”

Estimation for iron contamination in Si solar cell by ideality factor: Deep neural network approach

“…[1][2][3][4] Typically, metal-related defects are characterised by Fourier-transform infrared spectroscopy, electronparamagnetic resonance, minority carrier lifetime measurements, deep level transient spectroscopy (DLTS), Laplace DLTS, and so forth. [5][6][7] However, these techniques are time-consuming and require special equipment or/and specially prepared samples. At the same time, the rapid standard SC characterization technique widely used in industry today is current-voltage (IV) measurements.…”

“…IV characteristics contain important information about electrically active defects. 5,8 Researchers propose several methods based on IV characteristics to diagnose the defects 5,[8][9][10][11] and consider temperature dependencies of current components 10,11 or IV differential parameters. 8,9 These methods, however, require numerous IV measurements (in the first case) or IV measurements of high accuracy (in the second case).…”

“…Metal contamination control remains an important challenge for silicon processing in microelectronics, logic technologies, and manufacture of solar cells (SCs) 1–4 . Typically, metal‐related defects are characterised by Fourier‐transform infrared spectroscopy, electron‐paramagnetic resonance, minority carrier lifetime measurements, deep level transient spectroscopy (DLTS), Laplace DLTS, and so forth 5–7 . However, these techniques are time‐consuming and require special equipment or/and specially prepared samples.…”

“…At the same time, the rapid standard SC characterization technique widely used in industry today is current‐voltage (IV) measurements. IV characteristics contain important information about electrically active defects 5,8 . Researchers propose several methods based on IV characteristics to diagnose the defects 5,8–11 and consider temperature dependencies of current components 10,11 or IV differential parameters 8,9 .…”