Silicon carbide (SiC) devices have gained prominence in high-power and high-temperature applications due to their superior material properties, including wide bandgap, high thermal conductivity, and high electric field breakdown strength. However, reliability challenges remain critical barriers to widespread adoption. Key issues include gate oxide degradation, bipolar degradation, and threshold voltage instability. Understanding these failure mechanisms, along with accelerated testing methods and lifetime prediction models, is essential for improving device performance and longevity.

Gate oxide degradation is a major reliability concern in SiC metal-oxide-semiconductor field-effect transistors (MOSFETs). The interface between SiC and silicon dioxide (SiO₂) exhibits higher defect densities compared to silicon-based devices, leading to charge trapping and threshold voltage shifts. Fowler-Nordheim tunneling and hot carrier injection contribute to oxide wear-out, particularly under high electric fields. Negative bias temperature instability (NBTI) is another critical phenomenon, where negative gate bias at elevated temperatures induces interface trap generation, increasing threshold voltage over time. High-temperature reverse bias (HTRB) testing is commonly employed to evaluate gate oxide reliability, with failure criteria based on threshold voltage drift or gate leakage current increase. Empirical models, such as the power-law model for time-dependent dielectric breakdown (TDDB), are used to predict oxide lifetime under operating conditions.

Bipolar degradation occurs in bipolar SiC devices, such as PiN diodes and insulated gate bipolar transistors (IGBTs), due to the expansion of stacking faults in the epitaxial layer. These faults nucleate from basal plane dislocations (BPDs) under minority carrier injection, leading to increased forward voltage drop and reduced device performance. The degradation mechanism is accelerated at higher current densities and temperatures. To mitigate bipolar degradation, epitaxial growth techniques have been developed to reduce BPD density, and device designs incorporating recombination-enhancing layers have been implemented. Accelerated testing involves forward current stress at elevated temperatures, with degradation monitored through forward voltage measurements. Lifetime prediction models correlate stacking fault growth rate with stress conditions, enabling extrapolation to real-world operating scenarios.

Threshold voltage instability is another critical reliability issue in SiC MOSFETs, manifesting as shifts in threshold voltage during device operation. This instability arises from charge trapping at the SiO₂/SiC interface or within the oxide bulk. Positive and negative bias temperature instability (PBTI and NBTI) are observed under respective gate bias polarities, with charge trapping dynamics influenced by temperature and electric field. Fast and slow traps contribute to transient and permanent threshold voltage shifts, complicating device characterization. Bias-temperature stress (BTS) testing is used to assess threshold voltage instability, with stress conditions tailored to replicate operational environments. Compact models incorporating trap energy distributions and capture/emission time constants are employed to predict long-term threshold voltage drift.

Accelerated testing methods are essential for evaluating SiC device reliability within practical timeframes. These methods subject devices to elevated stress conditions—such as higher voltage, current, or temperature—to induce failure mechanisms observed under normal operation. Step-stress and constant-stress testing protocols are commonly used, with failure data analyzed using statistical methods like Weibull distribution. The Arrhenius model is applied to temperature-accelerated tests, while the inverse power law models voltage or current acceleration. Combined stress testing, such as high-temperature gate bias (HTGB) and high-temperature reverse bias (HTRB), provides insights into synergistic effects of multiple stressors.

Lifetime prediction models for SiC devices rely on extrapolating accelerated test data to normal operating conditions. For gate oxide reliability, the E-model and 1/E-model are used to describe electric field dependence, while the Arrhenius equation accounts for temperature effects. Bipolar degradation models incorporate recombination-enhanced defect motion theory, with stacking fault growth rates modeled as a function of current density and temperature. Threshold voltage instability models employ stretched exponential or logarithmic time dependence to capture charge trapping dynamics. These models enable estimation of mean time to failure (MTTF) and failure-in-time (FIT) rates, supporting reliability qualification and design optimization.

Despite progress in understanding SiC device reliability challenges, gaps remain in fully characterizing defect interactions and long-term degradation under complex stress conditions. Advanced characterization techniques, such as deep-level transient spectroscopy (DLTS) and scanning probe microscopy, are being employed to probe defect energetics and spatial distributions. Additionally, machine learning approaches are being explored to improve lifetime prediction accuracy by identifying hidden correlations in large reliability datasets.

In summary, reliability challenges in SiC devices—gate oxide degradation, bipolar degradation, and threshold voltage instability—require rigorous characterization and modeling to ensure robust performance in demanding applications. Accelerated testing methods and physics-based lifetime prediction models play a crucial role in addressing these challenges, enabling the development of more reliable SiC power electronics. Continued research into defect engineering, interface passivation, and advanced modeling techniques will further enhance the reliability of SiC devices, supporting their adoption in next-generation energy systems.