Deep-level transient spectroscopy (DLTS) is a critical tool for identifying and characterizing electrically active defects in semiconductors, particularly in wide bandgap materials like silicon carbide (SiC) and gallium nitride (GaN). These defects, often referred to as traps, significantly impact the performance and reliability of power devices by influencing breakdown voltage, switching losses, and long-term stability. DLTS provides direct insights into trap energy levels, capture cross-sections, and concentrations, enabling targeted defect engineering to optimize device performance.

In SiC and GaN power devices, high electric fields and elevated operating temperatures exacerbate the effects of deep-level traps. These traps can originate from intrinsic point defects, impurities, or structural imperfections introduced during crystal growth or processing. DLTS allows researchers to distinguish between different trap species by analyzing their thermal emission signatures. The technique involves filling traps with charge carriers under a bias pulse and monitoring the transient capacitance or current as carriers emit from the traps at varying temperatures. The resulting spectra reveal activation energies and trap densities, which are essential for correlating specific defects with device degradation mechanisms.

One of the most critical parameters in power devices is breakdown voltage, which is highly sensitive to deep-level traps near the conduction or valence band edges. In SiC, for example, Z1/2 and EH6/7 centers are common defects associated with carbon vacancies and silicon vacancies, respectively. DLTS studies have shown that Z1/2 traps, located approximately 0.7 eV below the conduction band edge, act as recombination centers that increase leakage current and reduce breakdown strength. By quantifying these defects using DLTS, manufacturers can adjust growth conditions or implement post-growth annealing to suppress their formation. Similarly, in GaN, carbon-related traps and gallium vacancies have been identified as key contributors to premature breakdown. DLTS measurements have demonstrated that carbon incorporation, often unintentionally introduced during metal-organic chemical vapor deposition (MOCVD), creates acceptor-like traps at 0.9 eV above the valence band edge, leading to increased dynamic on-resistance and reduced blocking capability.

Switching losses in SiC and GaN devices are another major concern influenced by deep-level traps. Fast switching is a hallmark of wide bandgap semiconductors, but traps can delay charge carrier emission, causing hysteresis and increased power dissipation. DLTS has been instrumental in identifying traps responsible for current collapse in GaN high-electron-mobility transistors (HEMTs). For instance, surface states and buffer-related traps have been shown to capture electrons during off-state biasing, which are then slowly emitted during switching transitions. By using DLTS to profile these traps, engineers have developed passivation techniques and optimized buffer layer designs to mitigate their impact. In SiC MOSFETs, near-interface oxide traps (NITs) at the SiO2/SiC boundary have been extensively studied using DLTS. These traps, with energy levels around 0.2-0.4 eV below the conduction band, contribute to threshold voltage instability and increased switching losses. DLTS data has guided the development of improved oxidation processes and nitrogen passivation methods to reduce NIT densities.

The temperature dependence of trap behavior is another area where DLTS provides unique insights. Power devices often operate at elevated temperatures, and some traps that are negligible at room temperature become dominant under high-temperature conditions. DLTS spectra collected over a wide temperature range reveal thermally activated trap emission, allowing researchers to model how defect populations evolve with temperature. In SiC, for example, boron-related deep acceptors exhibit increased activity above 150°C, leading to reduced carrier lifetimes. DLTS has helped quantify these effects, informing the use of alternative dopants or compensation strategies. GaN devices face similar challenges with iron- and magnesium-related traps, whose emission rates accelerate at high temperatures, degrading device reliability. DLTS-based studies have enabled the optimization of doping profiles and barrier layers to minimize these effects.

Defect engineering strategies guided by DLTS often involve a combination of material synthesis adjustments and post-processing treatments. In SiC, DLTS has demonstrated that high-temperature annealing in silicon-rich environments can reduce carbon vacancy concentrations, directly improving breakdown characteristics. For GaN, DLTS data has shown that in-situ doping control and low-damage etching processes are critical for minimizing trap densities in the active regions of devices. The ability to precisely measure trap energy levels also aids in selecting appropriate passivation materials. For instance, DLTS studies have confirmed that hydrogen passivation effectively neutralizes certain deep levels in SiC but may introduce new traps if not carefully controlled. Similarly, dielectric passivation layers on GaN must be optimized to avoid creating additional interface states detectable by DLTS.

Quantitative DLTS analysis has also advanced the understanding of defect interactions and their collective impact on device performance. In both SiC and GaN, multiple trap species often coexist, and their combined effects are not simply additive. DLTS can deconvolute overlapping peaks using rate window analysis or high-resolution techniques like Laplace DLTS, which provides superior energy resolution. This capability has revealed complex interactions between intrinsic defects and impurities, such as the pairing of carbon vacancies with nitrogen donors in SiC or the association of oxygen with threading dislocations in GaN. By mapping these interactions, researchers can prioritize which defects to target for maximum device improvement.

The role of DLTS extends beyond initial material characterization to reliability assessment and failure analysis. Power devices undergo significant stress during operation, and DLTS can detect trap generation or transformation under high-field or high-temperature conditions. For example, DLTS has been used to study the creation of new traps during avalanche breakdown in SiC diodes or under high-voltage bias in GaN HEMTs. These studies have identified critical stress thresholds and informed design rules to enhance device robustness. Additionally, DLTS can monitor trap annealing kinetics, providing data on how defects evolve over time and under different environmental conditions.

Despite its advantages, DLTS has limitations that must be considered when applying it to wide bandgap semiconductors. The technique is most sensitive to majority carrier traps in the depletion region of a junction, requiring careful sample preparation and measurement interpretation. Minority carrier traps can be studied using minority carrier DLTS variants, but with reduced sensitivity. In materials with high dislocation densities like GaN on silicon, dislocation-related traps may dominate the DLTS spectrum, complicating the analysis of point defects. Advanced DLTS methodologies, including optical DLTS and deep-level optical spectroscopy (DLOS), have been developed to address these challenges and expand the range of detectable defects.

The continued development of DLTS techniques aligns with the evolving needs of SiC and GaN power electronics. As device architectures become more complex, with trench designs, superjunction structures, and integrated gate drivers, the localization of defects becomes increasingly important. Scanning DLTS and nanometer-scale probing methods are being developed to map trap distributions with spatial resolution, enabling more precise correlation between defects and device performance. Furthermore, the integration of DLTS data with theoretical calculations and other characterization techniques has strengthened the identification of defect origins and their electronic structures.

In summary, DLTS serves as a cornerstone technique for defect engineering in SiC and GaN power devices by providing quantitative, energy-resolved information about traps that govern critical performance parameters. Its ability to fingerprint specific defects and monitor their behavior under operational conditions has directly contributed to improvements in breakdown voltage, switching speed, and reliability. As power electronics push toward higher voltages, frequencies, and temperatures, DLTS will remain an indispensable tool for unlocking the full potential of wide bandgap semiconductors.