Observation of Temperature-Dependent Capture Cross-Section for Main Deep-Levels in $\beta$-Ga2O3

TL;DR

This study investigates the temperature dependence of capture cross-sections for deep levels in $eta$-Ga2O3, revealing significant overestimations when temperature effects are ignored and proposing a model to accurately estimate these parameters.

Contribution

The paper introduces a method to derive temperature-dependent capture cross-sections in $eta$-Ga2O3, linking experimental data with the Meyer-Neldel rule and improving deep-level identification.

Findings

Temperature dependence significantly affects capture cross-section estimates.

The Meyer-Neldel rule correlates activation energy and capture cross-section.

Ignoring temperature effects leads to overestimations by 1-3 orders of magnitude.

Abstract

Direct observation of capture cross-section is challenging due to the need of extremely short filling pulses in the two-gate Deep-Level Transient Spectroscopy (DLTS). Simple estimation of cross-section can be done from DLTS and Admittance Spectroscopy (AS) data, but it is not feasible to distinguish temperature dependence of pre-exponential and exponential parts of the emission rate equation with sufficient precision conducting a single experiment. This paper presents experimental data of deep-levels in $\beta$-Ga2O3 that has been gathered by our group since 2017. Based on the gathered data we propose a derivation of apparent activation energy ($E_a^m$) and capture cross-section ($\sigma_n^m$) assuming temperature dependent capture via multiphonon emission model, which resulted in strong correlation between $E_a^m$ and $\sigma_n^m$ according to Meyer-Neldel rule, which allowed us to estimate low- and high-temperature capture coefficients $C_0$ and $C_1$ as well as capture barrier $E_b$. It also has been shown that without considering the temperature dependence of capture cross-section, the experimental values of $\sigma_n$ are overestimated by 1-3 orders of magnitude. A careful consideration of the data also allows to be more certain identifying deep-levels by their "fingerprints" ($E_a$ and $\sigma_n$) considering two additional parameters ($E_{MN}$ and $\sigma_{00}$) and to verify the density functional theory (DFT) computation of deep-level recombination properties.