Linear regression of the threshold for heavy-ion-induced leakage current on the inverse square of bias voltage. Demonstration of the influence of decreasing bias on LET range capable of inducing SELC.
Introduction
Therefore, SiC devices have an operating range where, of these three failure modes, only leakage current degradation is likely to occur. Because SELC does not inherently cause device or circuit failures, estimating the cumulative amount of leakage current during a mission is essential to ensuring reliability.
Material Properties of Silicon Carbide
Material Properties of SiC
Combined with the reduced temperature sensitivity of the wider bandgap and higher melting point, this makes SiC devices particularly tolerant of high-temperature environments. Additionally, as previously mentioned, SiC devices can in many cases actually be made smaller than equivalent silicon devices, further improving the frequency response.
![Fig. 1. Comparison of the repeating structures of various SiC polytypes, modified from [13], [14]](https://thumb-ap.123doks.com/thumbv2/123dok/10732901.0/14.918.124.793.138.387/fig-comparison-repeating-structures-various-sic-polytypes-modified.webp)
Vertical Power Device Design
Schottky, PiN, and Junction Barrier Schottky Diode Structure
During forward conduction, current flows from the Schottky contacts directly into the n-epitaxial region, since these interfaces have a reduced Schottky forward voltage drop determined by the height of the metal-SiC Schottky barrier. With sufficient reverse bias, the depletion regions connect and effectively remove the Schottky barrier.
Vertical MOSFET Structure
The presence of p+ regions increases the resistance by reducing the Schottky contact area, however in SiC this consequence is a minimal trade-off in most applications compared to the other benefits gained. On the left is a cross-section of a single transistor, while on the right is a top-down view of a MOSFET cover below the metallization showing a parallel line structure.
SiC Vertical Power Device Radiation Tolerance
Common Radiation Effects
- Total Ionizing Dose
- Displacement Damage Dose
- Single-Event Gate Rupture
- Latent Gate Oxide Damage
- Single-Event Burnout
- Single-Event Induced Leakage Current
Heavy-Ion Radiation Environment Modeling
Solar Particles
The solar particle environment is described using the Prediction of Solar particle Yields for Characterizing Integrated Circuits (PSYCHIC) model [51], which is an extension of the Emission of Solar Protons model including solar heavy ions [52]. In addition to flux and LET spectra for the worst recorded day and week, which can be extrapolated to a longer mission length for a conservative overestimate of total fluence, PSYCHIC provides radiation environment spectra for log-normally distributed confidence levels from 1% to 99% . As an example, the 50th confidence level for the 2-year fluence spectrum should approximately correspond to the average 2-year fluence spectrum recorded.
Galactic Cosmic Rays
Response of 1200 V SiC VDMOSFETs and JBS Diodes to Broad-beam Heavy-Ion
- Devices under Test
- Experimental Setup – Cyclotron Facility Information
- Testing Procedures
- General Observations
During the first test trip, a Keithley K2410 Source Measurement Unit (SMU), capable of powering a device while simultaneously measuring voltage and current, was used to both power each device and measure the resulting leakage current. between bias. A few devices were also identified after heavy ion testing to gain information on possible failure mechanisms. The device was replaced if the device was destroyed by SEB or the leakage current exceeded the power compliance of the measuring instrument.
Some additional test runs were completed on one device returning to a lower bias to determine whether or not existing leakage current affected the magnitude of new leakage current increases. Initial experiments determined the relationship between ion LET and reverse bias for the initiation of leakage current degradation. Degradation consisted of discrete increases in leakage current of varying magnitude as previously reported in [9], [10].
At a given bias, the average increase in leakage current with respect to total flow converged to a constant value as total flow per run increased. Example strip chart showing discrete increases in leakage current increases over time for a single device during irradiation.
Analysis of the Threshold for Single-Event Induced Leakage Current
Comparison to Existing Data
14 shows leakage current breakdown thresholds using new data from the test campaigns described in Chapter 6, and data presented in Johnson et al. This figure plots the average between the lowest irradiance bias at which measurable degradation occurs and the highest irradiance bias at which no measurable degradation is observed vs. Due to the mixture of raw data and data extracted from graphs, there are no meaningful error bars that can be applied equally to the entire data set.
Each point represents an individual device as the data is used in linear regression later in the paper to determine the parameters of the critical power model as plotted in the figure. The measured devices differ in breakdown voltages, epitaxial depths and epitaxial doping, but fall together.
Model Development
- Kuboyama et al. Model
- Application of Model
Distribution of degradation onset reverse bias values for various LETs for the data presented here as well as other works [9], [10]. Fit to these data is a linear regression of the function shown in (2) and detailed in Table III, which treats the onset of degradation as the crossing of a critical power threshold. The intercept should be approximately zero for any data set if the model is to be an accurate representation of the mechanisms at play.
The results of the regression analysis of each individual group are sufficiently similar to justify applying the modified model to the collective data. The error is approximately the same, however plotting the magnitude of each error in Figure 14, the critical power for the onset of leakage current degradation appears to be inversely proportional to any change in device parameters present in (1), such as PC *X/k is constant.
As the epitaxial depth increases, critical power must decrease for (2) to balance against a given LET and bias. Therefore, the data indicate that the length over which a critical power is exceeded is an approximately equal portion of the epitaxy in all the SiC devices shown.
![Fig. 15. Distribution of degradation onset reverse bias values for various LETs for the data presented here as well as other works [9], [10]](https://thumb-ap.123doks.com/thumbv2/123dok/10732901.0/41.918.223.689.255.670/distribution-degradation-onset-reverse-values-various-lets-presented.webp)
Statistical Analysis of SELC
Isolation of SELC in Data
However, the size, quantity and location of the discrete leakage current steps are unclear from the raw data stream. The next two sections are a brief demonstration of the process developed to ensure that the possible sources of error in the cross-sections created are identified. As a qualitative measure of the accuracy of the method, it is possible to recreate the original data using only the current increments associated with leakage current steps and keeping the current constant throughout.
This plot has a threshold applied so that the cumulative sum of the experimental noise values is approximately 0 A. By ensuring that the sum of all noise-related current changes is as close to zero as possible, the net current change between the data of raw and reconstructed data will always be approximately the same with a sufficient number of flow steps (based on observation, greater than ten). The separated leakage steps result in the same mean increase in leakage current as the raw data, indicating that the cross sections or CDFs are derived from these isolated leakage steps.
Analysis of model fit should therefore focus on the accuracy of the reconstruction in the middle of an isolated step curve that is not systematically guaranteed to match the raw data. 16 adjusted to start at 0 A and the leakage current recreated using only the leakage current changes taken from Figs.
Distribution of SELC
- SELC Frequency and Magnitude with Respect to Ion Linear Energy Transfer
- SELC Frequency and Magnitude with Respect to Reverse Bias Voltage
A confidence interval using (3) is used to constrain the estimate of the number of ions hitting the device. The probability of each ion hitting the device is represented by p, which for a beam with uniform distribution is the ratio of the beam area to the device area, or Da/Ba. The uncertainty of the estimate is ~2%, based on the potential variation in the number of ions hitting the device.
There is an additional error associated with the measurement noise threshold, which approaches the smallest step. If the probability of an event is 0.35, then the sensitive area is 35% of the area of the cube. Consequently, the CDF cross sections are inverted so that the cumulative probability decreases with increasing leakage step size, then normalized to the area of the device matrix.
As shown in Fig. 23c, most of the tested reverse bias region where degradation occurred showed a sensitive area equal to the die surface, indicating that every ion hitting the device caused SELC. While the simulated devices are modeled after SiC JBS diodes with a different rating than this experimental work, the saturation of the sensitive region with only high-LET particles is similar.
Modeling Total SELC Development over a Spacecraft Mission Lifecycle
- Risk Avoidance – SELC Threshold Model
- Comparing Experimental Data to Radiation Environments
- Accounting for SELC Step Size Variability
- Accounting for Environmental Variability
- Analysis of Results
- Influence of Reverse Bias
- Influence of Mission Length
- Influence of Shielding Thickness
25 is an example of the process developed in this research of quantizing an integral flux spectrum at specific LET values. The width of each rectangle is the range of LET values generalized from each experimental LET used, and the height of the rectangle is the flux associated with this range. The lowest LET capable of triggering SELC is determined by the simulation bias voltage [60].
Any LET above the highest LET for which there is experimental data must be approximated by a lower LET, introducing a slight underestimation of the cumulative SELC. This number is then mapped to the probability axis of the associated CDF with respect to both the LET and the desired simulation bias, shown in Fig. Note that both the shape of the distributions and the range of values change with the percentile environment used.
Comparison of the integral flux of the 15th confidence level PSIK transported through and 1000 mils of aluminum. With 500 miles of protection, at each bias, at least 40% of the simulations did not result in any cumulative SELC.
Conclusions
Javanainen, “Heavy-ion microbeam studies of single event leakage current mechanism in SiC VD-MOSFETs,” IEEE Trans. Weller, et al., "Heavy ion-induced degradation in SiC schottky diodes: Bias and energy deposition dependence." Wyss, “Gate damage induced in SiC power MOSFETs during heavy-ion irradiation-Part II,” IEEE Trans.
Velardi, “Analysis of Thermal Damage Induced by Heavy Ion Irradiation in SiC Schottky Diodes,” IEEE Trans. Ohyama, “Single-event burning of silicon carbide barrier diodes induced by high-energy protons,” IEEE Trans. Virtanen, “Charge Transport Mechanisms in Heavy Ion Driven Leakage Current in Silicon Carbide Schottky Power Diodes,” IEEE Trans.
Reed, et al., “Heavy ion-induced degradation in sic Schottky diodes: angle of incidence and dependence on energy deposition,”. Concepts for Ion-Induced Leakage Current Degradation in Silicon Carbide Schottky Power Diodes,” IEEE Trans.
Source Code Overview
