Deep-level transient spectroscopy (DLTS) is a critical characterization technique for identifying and quantifying electrically active defects in semiconductors. These defects, often referred to as traps, significantly influence device performance by altering carrier dynamics. Empirical studies have established clear correlations between DLTS-derived defect parameters—such as trap density, energy level, and capture cross-section—and key device metrics, including leakage current, threshold voltage shifts, and long-term reliability. Understanding these relationships is essential for optimizing semiconductor materials and improving device performance.

Leakage current in semiconductor devices is strongly influenced by the presence of deep-level traps. DLTS measurements reveal that traps with energy levels near mid-gap act as generation-recombination centers, increasing leakage through Shockley-Read-Hall (SRH) processes. For instance, in silicon-based devices, DLTS studies have identified oxygen-vacancy complexes (A-centers) and divacancies as dominant contributors to leakage. Empirical data show that a trap density exceeding 1e12 cm^-3 can lead to a measurable increase in reverse-bias leakage current. In GaN devices, carbon-related traps with energy levels around Ec - 0.9 eV correlate with elevated leakage currents, with densities above 5e13 cm^-3 causing a tenfold increase compared to low-defect samples. The capture cross-section of these traps further modulates leakage, where larger cross-sections (greater than 1e-15 cm^2) exacerbate carrier generation rates.

Threshold voltage shifts in field-effect transistors (FETs) are another critical metric linked to DLTS-detected defects. Traps located near the semiconductor-oxide interface or within the gate dielectric can capture or emit charge carriers, leading to shifts in threshold voltage (Vth). For silicon MOSFETs, DLTS has identified interface states (Dit) and border traps as primary culprits. Empirical studies demonstrate that a Dit value exceeding 1e11 cm^-2 eV^-1 can induce Vth shifts of several millivolts under bias stress. In GaN HEMTs, iron- and carbon-related traps with energy levels near Ec - 0.6 eV have been shown to cause Vth instabilities, with densities above 1e13 cm^-3 leading to shifts exceeding 0.5 V under high-field operation. The time-dependent nature of these shifts aligns with the emission kinetics of the traps, as quantified by DLTS-derived time constants.

Reliability metrics, such as bias temperature instability (BTI) and time-dependent dielectric breakdown (TDDB), also exhibit strong dependencies on DLTS-measured defect parameters. Positive BTI (PBTI) in high-k gate stacks, for example, correlates with oxygen vacancy traps detected via DLTS. Studies on HfO2-based devices reveal that trap densities above 1e12 cm^-3 accelerate Vth degradation under stress, with the degradation rate scaling linearly with trap concentration. Similarly, TDDB lifetime is inversely proportional to the density of bulk traps in the dielectric, as these defects serve as precursors for percolation paths. DLTS data on SiC power devices show that Z1/2 traps (carbon vacancies) at Ec - 0.65 eV reduce time-to-failure by up to 50% when their density exceeds 1e14 cm^-3. The activation energy of these traps, derived from Arrhenius analysis of DLTS spectra, further predicts the temperature dependence of degradation.

The energy level of traps, as determined by DLTS, plays a crucial role in their impact on device metrics. Shallow traps (within a few kT of the band edges) primarily affect carrier mobility and low-field conductivity, while deep traps (near mid-gap) dominate generation-recombination processes. For example, in perovskite solar cells, DLTS has identified iodine vacancy traps at Ev + 0.3 eV as contributors to hysteresis and reduced open-circuit voltage. The empirical relationship between trap density and hysteresis loss follows a logarithmic trend, with densities above 1e15 cm^-3 causing measurable performance degradation. In optoelectronic devices, such as LEDs, deep-level traps act as non-radiative recombination centers, reducing internal quantum efficiency. DLTS studies on InGaN LEDs reveal that trap densities above 1e14 cm^-3 correlate with a 10% drop in efficiency due to increased defect-assisted recombination.

The capture cross-section of traps, another key parameter from DLTS, influences the kinetics of carrier trapping and emission. Larger cross-sections indicate stronger coupling between traps and charge carriers, leading to faster response times but also greater susceptibility to noise and instability. In SiGe heterojunction bipolar transistors (HBTs), DLTS-measured traps with cross-sections greater than 1e-14 cm^2 contribute to excess low-frequency noise, degrading signal-to-noise ratios. The noise power spectral density scales quadratically with trap density in these devices. For power electronics, traps with large cross-sections exacerbate dynamic ON-resistance degradation, as seen in GaN-on-Si devices where iron-related traps (σ > 1e-13 cm^2) cause a 20% increase in resistance after high-voltage switching.

Spatial profiling of defects via DLTS provides additional insights into localized effects on device performance. In vertical power devices, such as PiN diodes, DLTS reveals that trap distributions are often non-uniform, peaking near epitaxial layer interfaces. These localized defect clusters create regions of enhanced leakage and premature breakdown. Empirical data show that a tenfold increase in trap density at the interface correlates with a 30% reduction in breakdown voltage. Similarly, in FinFETs, DLTS profiling identifies corner defects as dominant contributors to variability in threshold voltage across devices. The standard deviation of Vth across a wafer increases linearly with the areal density of these corner traps, as quantified by DLTS.

Temperature-dependent DLTS studies further elucidate the role of defects in device performance under varying operating conditions. In automotive applications, where devices experience wide temperature ranges, the thermal emission rates of traps determine their impact on leakage and stability. For example, in SiC MOSFETs, the EH6/7 traps (Ec - 1.1 eV) exhibit an activation energy of 1.2 eV, causing their contribution to leakage current to double every 50°C temperature increase. This Arrhenius behavior, extracted from DLTS spectra, directly informs derating guidelines for high-temperature operation.

Comparative DLTS analyses across material systems highlight universal trends in defect-device correlations. While specific trap energies and identities vary between silicon, GaN, SiC, and other semiconductors, the general relationship between trap density and device degradation remains consistent. A trap density threshold exists (typically 1e12 - 1e13 cm^-3) beyond which measurable degradation in leakage, threshold voltage, or reliability occurs. This universality suggests that DLTS-derived parameters can serve as reliable predictors of device performance across material platforms.

In summary, empirical DLTS data provide quantitative relationships between defect parameters and critical device metrics. Trap densities above material-specific thresholds degrade performance through increased leakage, threshold voltage shifts, and accelerated reliability failure. The energy level and capture cross-section of defects further modulate these effects, determining their severity under different operating conditions. These correlations enable targeted material optimization and predictive device modeling based on DLTS characterization.