In this study, we develop a time-dependent dielectric breakdown (TDDB) analysis framework. TDDB is an important failure mechanism in transistor devices, which are widely used in electrical equipment, including power applications. In the developed TDDB framework, the percolation path can be predicted according to the thickness of the gate insulator, temperature, and electric-field strength applied to the gate insulating layer by considering three major degradation mechanisms that generate TDDB: anode hole injection, anode hydrogen release, and the thermochemical mechanism. This technique can be used to predict the device lifetime for different stress temperatures, biases, and application times. Thus, the developed framework can be used in industries to derive accelerated-lifetime testing conditions and to analyze the warranty period.

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## I. INTRODUCTION

If a constant stress is applied to an insulator for a long time, the leakage current
in the insulator increases owing to various mechanisms and breakdown of the insulator
occurs. Time-dependent dielectric breakdown (TDDB) is an important factor in predicting
the lifetime of modern metal–oxide semiconductor field-effect transistor devices ^{(1)}. The widely known TDDB model—the percolation model—describes the breakdown phenomenon
of the insulator due to the formation of a percolation path by traps generated in
the insulator by stress. Trap generation in the insulator, which is the cause of TDDB,
has been proposed in previous works and can be explained by various mechanisms, such
as anode hole injection (AHI), (anode) hydrogen release (HR), and the thermochemical
(TC) mechanism. To test the lifetime of a semiconductor device with consideration
of the TDDB phenomenon, a large amount of time is required, as device lifetimes can
reach several years. Therefore, in the semiconductor industry, experiments and statistical
methods for accelerating the voltage and temperature are used. The following limitations
affect the reliability of the TDDB analysis process.

(1) $\textit{Trap-generation predictability}$—Various trap-generation mechanisms have been proposed, but the trap generation is difficult to predict, as it depends on the device structure, process conditions, and operating bias.

(2) $\textit{Physical model and lifetime consolidation analysis tool}$—There is a lack of integrated TDDB lifetime-analysis tools that link the trap-generation physical mechanism model and the lifetime-analysis statistical method.

To overcome these limitations of TDDB analysis, we developed a TDDB analysis framework that can be easily used in industries. The developed framework considers the major trap-generation mechanisms, and appropriate mechanisms operated according to the device structure and operating bias to predict the trap generation seamlessly. Additionally, because the developed solution can automatically extract accelerated testing conditions (stress bias magnitude, time, temperature, etc.) for a specific lifetime or a specific failure rate, it is a useful solution for industries.

## II. EXPERIMENTAL METHODS

### 2.1 Overall Description

To analyze lifetimes affected by the TDDB phenomenon, it is necessary to understand
generated traps through various mechanisms ^{(2)}, generated percolation paths using statistical techniques, and statistically analyze
the lifetime prediction based on them. In this paper, we develop a TDDB simulator
that modulates the traps and percolation paths, as shown in Fig. 1. When the user inputs the geometry parameters of the device, fresh-state electrical
characteristics, and testing conditions, various lifetime prediction results, such
as the bathtub curve, testing condition for initial failure detection, and lifetime
expectancy in the chip operation condition are outputted. This TDDB analysis framework
is implemented using the Python language.

### 2.2 Modeling of Trap Generation

The trap-generation mechanisms in the insulator, which is the cause of TDDB, consist
of AHI, AHR (also known as HR), and TC mechanisms ^{(3)}. According to previous papers describing the trap generation in the insulator by
the different physical mechanisms, the dominant trap-generation mechanism depends
on the conditions in which the insulator operates. In this section, the dominant trap-generation
mechanism is classified according to the experimental results of previous studies,
and the hybrid analysis method having the continuity of each mechanism is implemented
for the first time, to the knowledge of the author, in the proposed framework. The
physical description for each mechanism and the modeling procedures are as follows:

① TC: Polarization occurs when an electric field (E-field) is applied to a dielectric with polarity. This polarization causes the distortion of the lattice, and as a result, the molecules in the dielectric are affected not only by the external E-field ($E_{ox}$) but also by the polarization vector $\textit{P}$ (=$\chi \epsilon _{0}E_{ox}$). Finally, the magnitude of the local E-field ($\textit{E}$$_{loc}$) applied to the insulator molecules is

##### (1)

$\begin{equation} E_{loc}=E_{ox}+L\left(P/\epsilon _{0}\right)=\left(1+L\cdot \chi \right)E_{ox} \end{equation}$
where $\chi $ is the electric susceptibility, and $\textit{L}$ is the Lorentz factor,
which is approximately $\textit{L}$ = 1/3 for symmetrical cubic structures ^{(4)}. Thus, $E_{loc}=$ $\left(\frac{2+k}{3}\right)E_{ox}$. In the SiO$_{2}$ system, when
the dielectric constant $\textit{k}$ is 3.9, the local E-field is estimated to be
approximately $2E_{loc}$. This electrical stress causes the breakage of the natural
weak bond of the insulator (Si-Si bond, i.e., oxygen vacancy). The reduced activation
energy for the breakage of the Si-Si bond induced by $E_{loc}$ in the insulator can
be expressed as

where $Δ H_{0}$ is the field-free activation energy and $p_{\mathrm{eff}}$ is the
effective dipole moment, which is determined by the polar bonding in the molecule
and has a value of approximately 7–14 eÅ ^{(5)}. According to a previous study ^{(6)}, $Δ H_{0}\approx 1.0\,\,\mathrm{eV}$ and $p_{\mathrm{eff}}\approx 7\,\,\mathrm{eÅ
}$ under a weak E-field, and $Δ H_{0}\approx 2.0\,\,\mathrm{eV}$ and $p_{\mathrm{eff}}\approx
13\,\,\mathrm{eÅ }$ under a strong E-field. In this study, we use the latter condition
for insulator breakdown analysis under a strong E-field.

According to the TC model, the field-enhanced thermal bond breakage (\textit{dN/dt}) can be expressed by the first-order reaction rate equation as

where $\textit{N}$($\textit{t}$) is the number of traps per cm$^{3}$ under the given
conditions (time, temperature, and E-field), and $\textit{k}$$_{break}$ is the bond-breakage
rate. The solution of Eq. (3) is $N\left(t\right)=N_{0}\left(1- exp\left(- k_{\textit{break}}t\right)\right)$,
where $\textit{N}$$_{0}$ is the initial number of weak bonds (number of oxygen vacancies).
According to ^{(7)}, $\textit{N}$$_{0}$ is 1/20 for number of SiO$_{2}$ bonding; thus, we use $N_{0}=1.4\times
10^{21}/\mathrm{cm}^{3}$^{(7)}. The bond-breakage rate, $\textit{k}$$_{break}$, can be expressed using the Boltzmann
probability.

Here, $\textit{v}$$_{0}$ is the lattice vibration frequency (~10$^{13}$/s),$Δ H$is
the activation energy due to the local E-field, $\textit{k}$$_{b}$ is the Boltzmann
constant, and $\textit{T}$ is the temperature ^{(8)}. According to Eqs. (1-3), if the probability of trap generation for the unit cell constituting the insulator
follows the Poisson distribution, the parameter $\textit{b}$ of the Poisson distribution
is the product of the number of traps per unit volume ($\textit{N}$($\textit{t}$))
and volume of the unit cell (${a_{0}}^{3}$). Therefore, $b=N\left(t\right)\times {a_{0}}^{3}$.

② AHI: Unlike the TC model, which analyzes the TDDB according to the breakage of weak
bonds in the insulator, the AHI model analyzes the TDDB according to the Fowler–Nordheim
(FN) tunneling phenomenon. In comparison with the TC model, the AHI model is caused
by a stronger E-field. In many previous studies, AHI modeling was performed according
to the carrier fluence ^{(9)}. However, in this study, the cell-based analytical percolation path model is used
to analyze the TDDB.

Fig. 2. Description of the AHI model used in this study, including the electron injection for majority ionization, hole generation/drift, and defect generation.

Fig. 2 depicts the operation of the AHI model. The operation of the AHI model begins with
injecting electrons from the cathode to the anode via FN tunneling. The injected electrons
generate holes through the valence electrons and impact the ionization of the anode.
These high-energy hot holes pass through the insulator through the tunneling and thermionic
emission mechanism and generate defects in the insulator ^{(3)}. The impact ionization is divided into majority ionization and minority ionization
^{(10)}. Majority ionization is the case where the ionized hole has a kinetic energy that
exceeds the$\mathrm{E}_{\mathrm{v}}$ of the insulator, as shown in Fig. 2. This occurs when the energy ($E_{IN}$) of the injected electrons is higher than
the threshold energy ($E_{TH}$) of 6 eV; $E_{TH}=$ $\phi _{H}+E_{G}\left(Si\right)=4.8\,\,\mathrm{eV}+1.1\,\,\mathrm{eV}\approx
6\,\,\mathrm{eV}\,.$The minority ionization mechanism does not have enough energy
to cause the generated hole to exceed the valence-band energy ($\mathrm{E}_{\mathrm{v}})$
of the insulator. Because more tunneling events are needed to pass through the insulator,
the generation rate is significantly lower than that for majority ionization, and
the minority ionization has a smaller effect on the TDDB ^{(11)}. Therefore, the AHI modeling in this study considers only the dominant majority ionization.

Assumptions are needed to perform AHI modeling, as shown in Table 1, in accordance with previous studies and the electrical characteristics of the SiO$_{2}$
system, which is the gate insulator in the semiconductor device considered in this
study. AHI modeling begins with the FN tunneling current equation ^{(12)}:

The impact ionization rate can be expressed as ^{(13)}

where we can take parameter values such as $\text{Y} _{0}=2.15\times 10^{6}\,\mathrm{cm}^{-
1}$ and $\textit{H}$ = 82 MV/cm from a previous experiment ^{(14)}. As the current density of the generated holes is $J_{H}=\text{Y} J_{FN}$^{(11)}, the charge of the generated hole is $J_{H}\times A_{ox}\times t$, where $\textit{A}$$_{ox}$
is the insulator area and $\textit{t}$ is the observation time. This value divided
by $\textit{q}$ is the number of generated holes. As the generated holes drift to
the insulator, they can generate defects. The defect-generation probability ($P_{\textit{defect}}$)
is newly defined as a fitting parameter. This is the probability that a defect will
be generated for each drifted hot hole. This parameter can have the field dependency,
$P_{\textit{defect}}$ increases as $E_{ox}$ increases. Accordingly, the expected number
of defects throughout the oxide is $\frac{J_{H}\times A_{ox}\times t}{q}\times P_{\textit{defect}}$.

If the number of defects generated in the insulator is divided by the insulator volume, the expected number of defects per unit volume in the insulator can be obtained as follows: number of defects generated in insulator ${\div}$ insulator volume = number of defects per unit volume of insulator [ea/cm$^{3}$]. Similar to the TC model, assuming that the defect occurrence of the unit cell follows the Poisson distribution, the Poisson parameter $\textit{b}$ can be calculated as follows: $\textit{b}$ = number of defects per unit volume of insulator$\times $ volume of insulator(${a_{0}}^{3}$).

③ AHR (HR): In semiconductor devices, such as metal–oxide semiconductor (MOS) transistors,
the hydrogen passivation technique is widely used to improve the interface quality
of SiO$_{2}$ insulators grown on a silicon substrate. The Si-H bonds at the Si-SiO$_{2}$
interface are broken, the generated hydrogen atoms/ions are released into the insulator,
and TDDB occurs ^{(15)}. This is mainly observed in hyper-thin insulators of $T_{ox}\leq 50\mathrm{Å }$^{(3)}.

These hydrogen atoms/ions drift and diffuse into the insulator and act as traps ^{(11)}. The HR model is similar to the AHI model in that electrons are tunneled from the
cathode. However, it differs in that direct tunneling (DT) is dominant over FN tunneling
in thin insulators with $T_{ox}\leq 50\mathrm{Å }$. Therefore, in the HR modeling,
the DT equation is used. Additionally, the hydrogen desorption mechanism of the HR
model is divided into several categories, with the main ones being electrical excitation
(EE) and vibrational excitation (VE). In EE, hydrogen desorption occurs via field
emission, and in VE, hydrogen desorption occurs via phonons. The results of previous
experiments show that the Si-H bond has a relatively long vibrational lifetime of
$\sim 10^{- 8}$s in the Si-SiO2 system ^{(16)}. Thus, VE mechanism is more important than EE mechanism. VE can be divided into three
mechanisms: single electron coherent excitation, incoherent excitation ^{(17)}, and multi-electron excitation ^{(18)}. Incoherent excitation in a modern small semiconductor device that operate at high
E-field is very rare, and the rate of occurrence of multi-electron excitation is lower
than that of single electron excitation. Therefore, in this study, only single electron
excitation, which has the highest occurrence rate among the three VE mechanisms, is
considered, and the threshold energy required for hydrogen desorption is 2.5–3 eV.
Fig. 3 depicts the operation of the HR model.

Assumptions are needed to perform HR modeling, as shown in Table 2. The HR modeling begins with the DT current equation ^{(19,}^{20)}

##### (7)

$\begin{aligned} J_{DT}=\frac{A{E_{ox}}^{2}}{\left[1- \left(\frac{\phi _{s}+qV_{ox}}{\phi _{s}}\right)^{\frac{1}{2}}\right]^{2}}\times \exp \left[- \frac{B}{E_{ox}}\cdot \frac{{\phi _{s}}^{\frac{3}{2}}- \left(\phi _{s}- qV_{ox}\right)^{\frac{3}{2}}}{{\phi _{s}}^{\frac{3}{2}}}\right] \end{aligned}$

Fig. 3. Description of the HR model used in this study, including the electron injection, hydrogen generation / drift / diffusion, and defect generation.

where $\phi _{s}$ is the barrier height and $V_{ox}$ is the voltage across the insulator. Using Eq. (7), the charge amount of the injected electron can be calculated as $J_{DT}\times A_{ox}\times t$, where $\textit{A}$$_{ox}$ is the insulator area, and $\textit{t}$ is the observation time. This value divided by $\textit{q}$ is the number of injected electrons. The injected electrons can release hydrogen via Si-H bonding at the Si-SiO$_{2}$ interface. The hydrogen-release probability ($P_{\textit{release}}$) is newly defined as a fitting parameter. This is the probability that a hydrogen atom/ion will be released for each injected electron. This parameter can have the field dependency, $P_{\textit{release}}$ increases as $E_{ox}$ increases. The number of released hydrogen atoms/ions is calculated as $\frac{J_{DT}\times A_{ox}\times t}{q}~ \times P_{\textit{release}}$. If we assume that all the injected hydrogen operates as a defect inside the insulator, by dividing the number of hydrogen atoms or ions in the insulator by the volume of the insulator, the expected number of traps per unit volume of the insulator can be determined. Finally, the Poisson distribution parameter can be calculated in the same manner as for the AHI model.

The trap-generation mechanism is appropriately selected automatically in consideration of the semiconductor device insulator structure, and electrical characteristics (dynamic bias, E-field, etc.). By using the modeled trap-generation mechanisms as described above, it is possible to calculate the trap-generation probability (failure probability) in the unit cell in the oxide insulator by calculating the Poisson parameter, assuming the Poisson distribution.

### 2.3 Modeling of Percolation Path Estimator

We use the cell-based analytical percolation model to analyze the lifetime of dielectric
breakdown caused by traps in the insulator, as described by the TC, AHI, and HR physical
mechanisms. As shown in Fig. 4, in the percolation model, traps inside the insulator between two electrodes are
randomly generated by stress and form a conductive path between the electrodes, causing
electrical breakdown ^{(21,}^{22)}. The multiple percolation path model is a model that considers not only the shortest
distance between the electrodes ^{(23)} but also the path of the nearest cells. As shown in Fig. 4, the simulation was conducted with a model in which the nine nearest cells could
form a path.

The two most widely used types of percolation models are the Monte Carlo (MC) model
and the cell-based analytical model. The MC model generates traps randomly in the
insulator to determine the failure probability of the device over time and has the
disadvantage of a long simulation time. In comparison, the cell-based analytical model
is advantageous because it requires less simulation time (<10% compared with the MC
model) and can predict the lifetime efficiently even under various stress conditions
^{(24)}. Therefore, in this study, a cell-based analytical model is used to perform lifetime
analysis.

First, the insulating layer is divided into unit cells. If the Poisson distribution
parameter obtained in the previous step is $\textit{b}$, the probability of generating
$\textit{k}$-traps in the unit cell can be obtained by using the PMF$P\left(X=k\right)=(b^{k}e^{-
b})/k!\left(k=0,1,2,3,\cdots \right)$of the Poisson distribution ^{(25)}. If there is at least one trap in the unit cell, the cell is determined as defective,
and $\textit{P}$(defective cell) = 1 - $\textit{P}$(no trap in the unit cell) = $1-
e^{- b}$. The failure rate of a cell is denoted as$F_{cell}=\lambda \,.$ The probability
that a column becomes the percolation path is $F_{perc}=9^{n- 1}\lambda ^{n}$, and
the probability that the percolation path is not generated in all $\textit{N}$ columns
is$1- F_{perc}$ $=\left(1- \frac{1}{9}\left(9\lambda \right)^{n}\right)^{N}.$Then,
the Weibit ($\textit{W}$$_{BD}$) can be calculated as$W_{BD}=\ln \left[- \ln \left(1-
F_{BD}\right)\right]=\ln \left[- N\ln \left(1- \frac{1}{9}\left(9\lambda \right)^{n}\right]\right.\,,$
and because $\lambda \leq 1$,$W_{BD}\approx \ln \left(N\right)+\ln \left(\frac{1}{9}\right)+n\ln
\left(9\lambda \right)$^{(23,}^{26)}.

### 2.4 Modeling of Lifetime Analyzer

In this study, we use the Weibull distribution and temperature–nonthermal (T-NT) relationship
for the lifetime distribution and lifetime/stress relationship, respectively, and
the T-NT Weibull model is assumed as an accelerated-lifetime test model by combining
the lifetime distribution and the lifetime/stress relationship. Note that the Weibull
distribution is applicable to all cases, regardless of whether the probability of
failure increases, decreases, or remains constant over time. This is why we use the
Weibull distribution in this study. The suitability of the data for the Weibull distribution
can be evaluated by using the Weibull plot with the Weibit ($\textit{W}$$_{BD}$, which
was obtained in Sections 2.3) and \textit{ln}(\textit{time}) as the y and x axes,
respectively. Fig. 5 shows an example of a Weibull plot in which the temperature and bias voltage in an
MOS transistor device are varied ^{(27)}.

Fig. 5. Example of a Weibull plot in which the temperature and bias voltage in an MOS transistor device are varied.

In the T-NT Weibull relation, the temperature and the bias voltage are considered
separately as stress sources by considering the Arrhenius relationship and the inverse
power law relationship simultaneously. The corres-ponding equation is ^{(28)}

##### (8)

$\begin{equation} f\left(t,V,T\right)=\frac{\beta V^{n}e^{- \frac{B}{T}}}{C}\cdot \left(\frac{t\cdot V^{n}e^{- \frac{B}{T}}}{C}\right)^{\beta - 1}\cdot e^{- {\left(\frac{t\cdot V^{n}e^{- \frac{B}{T}}}{C}\right)^{\beta }}} \end{equation}$where $\textit{t}$ is time, $\textit{V}$ is the voltage, and $\textit{T}$ is the temperature. If we have three parameters, such as $\textit{B}$, $\textit{C}$, and $\textit{n}$, the lifetime and stress relationship can be predicted. ${\beta}$ can be obtained from the slope of the $\textit{W}$$_{BD}$$\textit{-ln(t)}$ curve, which is used to check the validity of the data. The three parameters ($\textit{B}$, $\textit{C}$, and $\textit{n}$) are extracted from the lifetime and stress experimental data by using the linearization technique of the T-NT Weibull relationship. Once the three parameters are obtained, relationship between the lifetime and stress (such as the voltage and temperature) can be obtained according to the T-NT relationship, which is expressed by the following equation.

##### (9)

$\begin{equation} \textit{Lifetime}\left(stress\,\,V,T\right)=C/\left(V^{n}e^{- \frac{B}{T}}\right) \end{equation} $The model parameters are extracted automatically via regression analysis using Python code, and various lifetime and stress analyses (e.g., for a specific lifetime or predicting the lifetime at a specific failure rate) can be easily performed in the framework.

## III. RESULTS AND DISCUSSION

The developed TDDB analysis framework has been applied to the planar MOS transistor with a gate insulator, SiO$_{2}$.

Fig. 6 shows measured data and simulation results for thick gate oxide ^{(29)}. The simulation conditions are $T_{ox}$ = 9.9 nm, $A_{ox}$= 0.6 mm$^{2}$, and the
temperature is set to the room temperature. The simulation $E_{ox}$ value ranges from
5 MV/cm to 12 MV/cm. As $E_{ox}$ increases, trap generation in the oxide is actively
occurring, and therefore lifetime decreases. Also, as $E_{ox}$ increases, the fitting
parameter$P_{\textit{defect}}$of the AHI model increases. This field dependency is
applied by when $E_{ox}$ increases by 1 MV/cm, the parameter value increases several
times. During the simulation, the TC model is applied under a relatively weak $E_{ox}$
region, and the AHI model are applied as the $E_{ox}$ becomes stronger. In the case
of thick gate oxide (case 1 in Fig. 8), we can see the boundary of TC/AHI according to $E_{ox}$. For the thick oxide, assume
that the direct tunneling probability is extremely small and that the HR model is
not working. The boundary where the models operate are $E_{ox}$= 6~MV/cm for TC/AHI.
A stronger local $E_{ox}$ applied to the bonds in the TC model yields a higher bond-breakage
rate and a higher breakdown probability. Additionally, in the case of the AHI model,
as the number of tunneling electrons increases, the impaction ionization rate increases.
Consequently, more hot holes are generated, forming additional traps, which increases
the probability of breakdown.

Fig. 7 shows simulation results for thin oxide ^{(30)}. The simulation conditions are $T_{ox}$ = 2.7 nm, $A_{ox}$= 0.001~mm$^{2}$. The simulated
temperature is set to room temperature. The $E_{ox}$ range was simulated from 10~MV/cm
to 12~MV/cm, and similar to Fig. 6, as $E_{ox}$ increases, trap generation in the oxide is actively occurring, and therefore
lifetime is decreasing. Also, as $E_{ox}$ increases, the fitting parameter$P_{\textit{release}}$of
the HR model increases. This field dependency is applied by when $E_{ox}$ increases
by 1 MV/cm, the parameter value increases several times. Unlike case of thick oxide,
as for thin oxide, the TC model is applied under a relatively weak $E_{ox}$ , and
the HR models are applied as the $E_{ox}$ becomes stronger. In the case of thin oxide,
direct tunneling acts as the main mechanism of oxide degradation, eventually activating
the HR model in the high field region. In case 2 of Fig. 8, we can see the boundary of TC/HR according to $E_{ox}$. The boundary where the models
operate are $E_{ox}$= 11~MV/cm for TC/HR. It is assumed that the AHI model does not
work in this simulation because $E_{ox}$ that satisfies the threshold energy($E_{TH}=6eV$)
of the AHI region in the thin oxide is a very high field region(almost 22 MV/cm).
In the case of the HR model, the number of electrons tunneling from the cathode is
increased, and as hydrogen is released at the Si/SiO$_{2}$ interface to generate traps,
the probability of breakdown increases.

Fig. 8 shows the boundaries between each model under $E_{ox}$. For the HR model, the threshold
energy ($E_{TH}$) has a value of 3 eV and 6 eV for the AHI model ^{(11)}. In cases where $T_{ox}$ is less than 5 nm, DT tunneling has an important effect
on TDDB than FN tunneling, and as $E_{ox}$ increases, it operates in the order of
TC,HR,AHI model. If $T_{ox}$ is gris gris greater than 5 nm, the tunneling current
in the low field is very small (direct tunneling component), so the HR model does
not operate ^{(19)}. Thus, in the low field, the TC model and the AHI model operates in the high field.
Case 1 is a simulation region for thick oxide, resulting from 5 MV/cm to 12 MV/cm,
and the boundary of TC/AHI exists at approximately 6~MV/cm. Case 2 is the simulation
region for thin oxide, the result of 10 MV/cm to 12 MV/cm, and the boundary for TC/HR
can be found at approximately 11 MV/cm.

## IV. CONCLUSIONS

The breakdown of the insulating layer due to the TDDB phenomenon has long been a serious problem in semiconductor manufacturing and various mechanisms for explaining it have been presented. However, it is difficult to analyze the TDDB, owing to (1) the variation of the main trap-generation mechanism with changes in the process, device structure, and applied bias and (2) the absence of an analysis tool that allows lifetime analysis of the TDDB physical mechanism in connection with the statistical method. Thus, in this study, (1) three different mechanisms, depending on the architecture, were made to operate within a single framework seamlessly and (2) the analysis of “trap generation${\rightarrow}$percolation path${\rightarrow}$ lifetime” was unified to provide an integrated analysis environment. Additionally, a methodology for calibrating the simulator according to the measured lifetime was provided. This is useful for determining accelerated testing conditions under a given condition, assuming a specified lifetime.

### ACKNOWLEDGMENTS

This research was supported by the MOTIE(Ministry of Trade, Industry & Energy (10085645) and KSRC(Korea Semiconductor Research Consortium) support program for the development of the future semiconductor device, and in part by Korea Electric Power Corporation (Grant number: R18XA06-78).

### REFERENCES

## Author

Kiron Park received the B.S. degree from the electrical and electronics engineering from Konkuk University, Seoul, Korea, in 2019.

He is currently working toward the M.S. degree of the electrical and electronics engineering from Konkuk University.

His research area is reliability characterization and TCAD analysis of the next generation semiconductor devices.

Sujin Im is currently working at device research laboratory as a researcher from Konkuk University, Seoul, Korea.

His research area is reliability characterization of the nanoscale semiconductor device.

Keonho Park is currently working at device research laboratory as a researcher from Konkuk University, Seoul, Korea.

His research area is reliability characterization of the nanoscale semiconductor device.

Kwonjoo Son is currently working at device research laboratory as a researcher from Konkuk University, Seoul, Korea.

His research area is reliability characterization of the nanoscale semiconductor device.

Seungeui Hong is currently working at device research laboratory as a researcher from Konkuk University, Seoul, Korea.

His research area is reliability characterization of the nanoscale semiconductor device.

Jongwook Jeon received the Ph.D. degree from the Department of Electrical Engineering, Seoul National University, Seoul, South Korea, in 2009.

From 2009 to 2017, he was a senior and a pricipal engineer in semiconductor R&D center of Samsung Electronics, Hwasung, Korea. In 2017, he became an Assistant Professor with the Department of Electronics Engineering, Konkuk University, Seoul.

His current research interests include reliability characterization and path-finding next generation technology of semiconductor device.