(Jonghwan Lee)
^{1}^{†}
(Daeki Hong)
^{1}

(Department of System Semiconductor Engineering, Sangmyung University, 31 Sangmyungdaegil,
Dongnamgu, Cheonan, Chungnam, 31066, Korea)
Copyright © The Institute of Electronics and Information Engineers(IEIE)
Index Terms
Quantummechanical correction, 1/f gate leakage current noise, 1/f drain current noise, random telegraph noise, nanoscale MOSFETs
I. INTRODUCTION
With the introduction of MOS devices aggressively scaled to deep submicron channel
length, an ultrathin gate oxide and high doping concentration are required to increase
the drive current and to minimize undesirable short channel effects. These design
rules result in strong transverse electric fields at the SiSiO_{2} interface even near the threshold of inversion. This gives rise to significant band
bending of the energy bands, leading to the confinement of electron motion in the
direction perpendicular to the interface. In these conditions, electrons in the inversion
layer form actually a quasi twodimensional electron gas (2DEG) with quantized energy
levels within the channel^{[1]}. These quantum mechanical (QM) effects have significant impact on the performance
of nanoscale MOSFETs and thus the classical models are inadequate for stateoftheart
MOS structures^{[2}^{4]}.
The quantum nature of electrons in the inversion layer is typically studied by solving
Schrödinger’s and Poisson’s equations selfconsistently^{[5]}, and also considering the simplified models used in circuit simulators^{[6]}. The enhancement in the centroid and the finite thickness of the inversion layer
are one of the main QM effects on MOSFET behavior^{[7]}. This reduces the inversion layer charge density and the total gate oxide capacitance^{[8,}^{9]}, compared to classical models without QM effects, and causes the threshold voltage
shift effect ^{[10,}^{11]}.
For a compact circuit simulation model of direct tunneling of electrons form nMOS
inversion layer, a variational approximation is employed to calculate the surface
potential and the energy of the first quantized level in the inversion layer without
the requirement for iterative solution^{[12]}, and an efficient model is demonstrated by using both QM calculations for the substrate
and a modified WKB approximation for the transmission probability^{[13]}. It is shown from the quantum charge sheet model that QM effects on the drain current
are expected to be due to QM effects on gradients in both the inversion charge and
in the surface potential ^{[14]}.
Besides QM effects on the static characteristics, QM effects have been observed in
the trap and noise behavior ^{[4,}^{[15}^{19]}. For heavily doped MOSFETs, extracted interface trap capture cross sections are found
to be an order of magnitude larger than values from the classical theory^{[19]}. In order to investigate QM effects on the capture and emission times of random telegraph
signal (RTS) noise, the interaction between an oxide trap and an electron in the subbands
of the inversion layer is theoretically modeled and analyzed in a SiSiO_{2} interface ^{[15,}^{16,}^{18]}. Regarding the 1/f noise models of drain current, numerical models are employed to
account for QM impact on the extracted value of the oxide trap density^{[17]}. However, there are no analytical 1/f and thermal noise models, which incorporate
QM effects and are applicable to circuit simulations.
In this paper, a comprehensive chargebased compact model is newly formulated to quantify
QM effects on both the gate and drain current noise in nanoscale MOSFETs. The modeling
approach uses calculation of surface potential $\psi_{s}$ to capture the physics of
quantum transport. An emphasis on surfacepotentialbased parameters of noise models
is laid to make the models to accurately predict the noise performance over all operation
regimes of nanoscale MOSFETs, as well as at low and high frequencies. Furthermore,
with the help of an accurate and generally applicable compact noise model, we can
implement the compact noise models into circuit simulator format. Section II describes
the quantization effects on the inversion charge parameters. In Section III, on the
basis of the quantum correction model of the inversion layer charge, a gate leakage
current and 1/f noise model are formulated. Section IV gives the analytical expressions
for the drain current and noise model accounting for quantum and short channel effects.
The comparison between the classical and quantum models is shown in Section V. Finally,
Section VI presents some conclusions.
II. QUANTUM CORRECTION FOR COMPACT MODELING
1. Surface Potential and Threshold Voltage Model
The high channel doping levels and large transverse electric fields result in the
confinement of electron motion in the x direction perpendicular to the interface,
so that the conduction band within the channel is split into discrete subbands. Based
on the effective mass approximation and a parabolic band structure, the envelope wave
function $\zeta_{i j}$ for the ith valley and jth subband satisfies 1D Schrödinger’s
equation^{[1]}. Due to the nonzero value of the quantized charge centroid, the surface potential
$\psi_{s}$ determined by quantum effects is greater than predicted by the classical
model^{[6]}. The enhancement of the surface potential is given by the average centroid position
from the interface, $x_{n}^{Q M}$ and the total electron charge density, $Q_{n}$^{[8,}^{12,}^{14,}^{20}^{22]}.
(1)
with
where $\mathcal{E}_{S i}$ is the silicon dielectric constant, $q$ is the electron
charge, $\hbar$ is the reduced Planck constant, $k$ is the Boltzmann’s constant, $T$
is the temperature, $g_{i}$ is the degeneracy of the energy subband ($g_{1}$=2, $g_{2}$=4),
$m_{di}$ is the densityofstates effective mass ($m_{d1}$=$m_{x2}, m_{di2}= \left(m_{x
1} m_{x 2}\right)^{1 / 2}$), $m_{xi}$ is the effective mass perpendicular to the surface
($m_{x 1}$ = 0.916 $m_{0}$, $m_{x 2}$ = 0.19 $m_{0}$), $E _{ij}$ is the energy state
of the $i$th valley and $j$th subband, $N _{ij}$ is the sheet charge density, $E
_{f}$ is the Fermi energy level, $\phi_{f}=(k T / q) \ln \left(N_{s u b} / n_{i}\right)$
is the Fermi potential, $E _{g}$ is the energy bandgap, and $F _{s}$ is the surface
electric field.
Due to the quantummechanical behavior of inversion layer electrons, more band bending
is required to achieve the same inversion charge as the classical value, resulting
in the increase in the threshold voltage. The threshold voltage $V _{T}$ can be easily
calculated by including an additional quantum shift term as follows^{[11,}^{22]}
with
where $N _{sub}$ is the substrate doping density, $n _{i}$ is the intrinsic carrier
density, and $C _{ox}$ is the oxide capacitance per unit area.
For the polysiliconSiO_{2}pSi MOS structure, the relationship between the gatetosource voltage $V _{gs}$
and the surface potential $\psi_{s}$ is described by the quantized charge density^{[8,}^{12]}
with
where $V_{F B}$ is the flatband voltage, $V_{\text {poly}}$ is the polysilicon voltage
drop, $N_{\text {poly}}$ is the polysilicon doping concentration, $\varepsilon_{ox}$
is the oxide dielectric constant, $t_{ox}$ is the oxide thickness, the trapped charge
in the oxide, $\mathcal{Q}_{i t}$, is included to account for the trappingdetrapping
effects at the SiSiO_{2} interface, and $\mathcal{Q}_{d}$ is the depletion charge density per unit area.
2. Inversion Charge and Capacitance Model
As the channel length and oxide thickness continue to be scaled to nanometer range
in current CMOS devices, the quantization effects should be taken into account for
an accurate inversion charge model. By using Eqs. ((1), (5)), an expression for the inversion layer charge is obtained as
where $N _{sub}$ is the substrate doping density, $n _{i}$ is the intrinsic carrier
density, and $C _{ox}$ is the oxide capacitance per unit area.
For the polysiliconSiO_{2}pSi MOS structure, the relationship between the gatetosource voltage $V _{gs}$
and the surface potential $\psi_{s}$ is described by the quantized charge density^{[8,}^{12]}
with
where the denominator $t_{o x}^{Q M}$ of Eq. (7) is termed the electrical oxide thickness. Including the parasitic source and drain
resistance $R_{s, d}$ and incorporating the draintosource voltage $V_{ds}$, the channel
charge density is expressed by
where $I _{d}$ is the drain current, $V _{y} (y)$ is the channel potential at any
given point y along the channel and $k_{bulk}$ is the bulk charge parameter.
Since the quantum effects lead to an increase in the inversion layer thickness, the
inversion capacitance decreases and thus, the total gate capacitance is reduced. The
inversion charge capacitance is given by ^{[7,}^{22]}
with
where Cn is the classical inversion capacitance, $X^{Q M}$ is the inversion layer
thickness, $E _{eff}$ is the effective electric field in inversion layer in MV/cm,
and $n _{sw}$ is the subthreshold swing parameter. For very high polysilicon doping
levels ($N _{poly}$ > 10^{19} cm^{3}), no quantum behavior has to be considered to determine the surface potential at
the polysiliconSiO_{2} interface, because all the subbands are squeezed out of the potential well^{[8]}. According to the classical theory, the polysilicon capacitance is calculated by
differentiating the polysilicon charge density $Q _{poly}$ as follows^{[8]}
III. QUANTUM EFFECTS ON GATE LEAKAGE CURRENT AND NOISE MODEL
1. Gate Leakage Current Model
In the ultrathin gate oxide regime ( < 3 nm), the classical models without including
quantum effects of the inversion layer overestimate the value of the gate leakage
current and noise, because the peak charge density is situated at a distance (~1 nm)
away from the SiSiO_{2} interface. This leads to an enhancement in the oxide thickness and a reduction in
the oxide voltage. Therefore, the quantum effects need to be taken into account by
using the correction for the oxide thickness and the oxide voltage,
The BSIM4 gate tunneling current model can predict all the significant direct tunneling
current components which are electron tunneling from the conduction band in the substrate
to the gate, electron tunneling from the valence band in the substrate to the gate,
and hole tunneling from the valence band in the gate to the substrate. Taking into
account quantum effects of the oxide thickness and the oxide voltage, the tunneling
current model becomes^{[12,}^{23]}
with
where $h$ is the Planck constant, $m_{ox}$ is the electron effective mass of the conduction
band in the oxide, $\lambda$ is a fitting parameter for three current components,
$\phi_{b o}$ is the SiSiO_{2} barrier height (3.1 eV for electrons and 4.5 eV for holes), and $\phi_{b}$ is the
tunneling barrier height.
2. Gate Current Noise Model
One possible mechanism for 1$/ f^{\gamma}$ noise of the gate leakage current is for
traps to affect locally the barrier height or shape due to thermal noise of the frequencydependent
conductance of the oxide slow traps ^{[24]}. This fluctuation in turn modulates the tunneling transmission, and causes fluctuations
in the tunneling current. The quantum effects for the spectral noise density are accounted
for by the correction of the oxide voltage $V_{ox}$ and the electron subband energy
$E_{ij}$, and are described by ^{[24]}
with
where $m_{\perp}$ is the transversal electron effective mass, $E_{\perp}$ is the transversal
energy component, $A_{g}$ is the gate tunneling area, $G_{p}^{Q M}(f)$ and $C_{p}^{Q
M}(f)$ are the frequencydependent parallel conductance and capacitance of oxide traps,
respectively. Assuming that the oxide trap density is spatially distributed over a
physical distance d into the oxide, the electrical trap distance $d^{Q M}$ becomes
$d+\left(\varepsilon_{o x} / \varepsilon_{s i}\right) x_{n}^{Q M}$ in the quantum
approach, leading to
IV. QUANTUM EFFECTS ON DRAIN CURRENT AND NOISE MODEL
1. Mobility and Drain Current Model
As the gate oxide thickness and the substrate doping level are thinner and higher,
the normal electric field increases significantly and the influence of the quantized
inversion layer become important. A correct and simple model to account for these
effects is required to predict the mobility and $IV$ characteristics of nanoscale
MOSFETs. The quadratic mobility degradation factor is necessary for an effective mobility
model of ultrathin gate oxide nMOSFETs, and an analytical mobility model $\mu_{n,
e f f}^{Q M}$ is given by^{[25]}
where $\mu_{n 0}$ is the lowfield electron mobility, and $\alpha_{1}^{Q M}$ and $\alpha_{2}^{Q
M}$ are the mobility degradation factor. Introducing an effective draintosource
voltage $V_{d s, e f f}^{Q M}$, and considering the channel length shortening effects
and parasitic resistance $R_{s,d}$, the drain current for shortchannel MOSFETs biased
in the linear and saturation region is then obtained as ^{[22,}^{26]}
with
where $W$ is the channel width, $L$ is the channel length, $E_{c}$ is the lateral
critical electric field at which carriers travel with the saturated velocity $v_{sat}$
near the drain section of the length $L_{sat}$, and $\Delta$ is a small quantity to
adjust the smoothness at the transition region near $V_{dsat}$ ^{[26]}.
2. RTS (Random Telegraph Signal) Noise Model
If we consider a section of channel with width $W$ and length $y$, fluctuations in
the trapped oxide charge will induce correlated fluctuations in the channel carrier
number and mobility. The resulting fractional change in the local drain current can
be represented as ^{[15,}^{27]}
where $\Delta Q_{n}=Q_{n} W \Delta y$ , $\Delta Q_{T}=Q_{T} W \Delta y$ , and $Q_{n}$
and $Q_{T}$ are total charge of channel carriers and occupied traps per unit area.
The sign of the mobility term is chosen either positive for acceptorlike traps or
negative for donorlike traps. The ratio $R_{0}=\delta \Delta Q_{n} / \delta \Delta
Q_{T}$ in Eq. (21) describes the efficiency of the drain current modulation corresponding to a variation
of the trapped charge. Considering the effects of the quantization and polysilicon
depletion, the coefficient $R_{0}^{Q M}$ depends on the surface potential through
charge capacitance and is given by ^{[27]}
with
where $C_{it}$ is the interface trap capacitance. On the basis of Matthiessen’s rule^{[28]}, we can readily find that
where $S^{Q M}\left(x_{t}^{Q M}, Q_{n}^{Q M}\right)$ is a scattering rate which is
a function of the distance $x_{t}^{Q M}\left(=x_{t}+\left(\varepsilon_{o x} / \varepsilon_{s
i}\right) x_{n}^{Q M}\right)$ of the trapping state^{[28]} and of the carrier charge density $Q_{n}^{QM}$ ^{[29]}, and is strongly affected by channel quantization^{[17]}. In order to properly account for these effects in ultrathin oxide MOSFETs, we employ
an empirical model for the scattering coefficient $S^{Q M}\left(x_{t}^{Q M}, Q_{n}^{Q
M}\right)$ in linear approximation as ^{[28,}^{29]}
where $r_{\max }^{Q M} \approx\left(\varepsilon_{o x}+\varepsilon_{s i}\right) /\left(C_{o
x}^{Q M}+C_{d}^{Q M}+C_{i t}\right)$ is the maximum distance from the scattering charge
for which the effect of image charges can be neglected and $S_{0}$ is the scattering
constant in cm/C^{1/2}Vs. Assuming that the carrier distribution is uniform along the channel, the RTS amplitude
noise becomes ^{[15]}
3. Low and High Frequency Noise Model
According to the number fluctuation theory, the total power spectral density of the
drain current noise at low frequencies is obtained by calculating the fluctuations
in the number of occupied traps and performing the integration over the channel length.
Assuming that the oxide trap density $N_{t} (E)$ is uniformly distributed over the
space into the oxide and the trapping time constant is an exponentially increasing
function of the trap distance $x_{t}$, the total $1/f$ noise power of the drain current
in the linear region ($V_{d} \leq V_{d s a t}$) is given by ^{[27]}
with
where $\gamma_{t u n} \approx 10^{8} \mathrm{cm}^{1}$ is the exponential factor of
tunneling process, $N_{t}(E_{fn})$ is the oxide trap density around the electron quasiFermi
level $E_{fn}$, and $Q_{n 0}^{Q M}$ and $Q_{n L}^{Q M}$ are carrier charge densities
for $R_{s,d} = 0$ at the source and drain ends of the channel, respectively. In the
saturation region, the total $1/f$ noise power is obtained by including the noise
contribution arising from the pinchoff region $L_s{at}$, and is represented as ^{[27]}
with
It has been shown that the high frequency noise originates from thermal fluctuation
in the channel and is expresses as ^{[30,}^{35]}
where $Q_{n}$ is the total inversion layer charge density. For the thermal noise modeling
of short channel devices, the total inversion charge density is derived by considering
the velocity saturation of carriers due to the high lateral electric field, and is
turned out to be ^{[30]}
Substituting Eq. (32) into Eq. (31), the thermal noise spectral densities of drain current in the linear and saturation
region are given by
V. RESULTS AND DISCUSSION
For the purpose of developing a model applicable to circuit simulation, it is very
important to extend classical simulators to include QM effects with computational
efficiency. For the quantized energy levels of inversion layers, a simple and approximate
model can be obtained by considering the dominant contribution of carriers in the
lowest energy subbands $E_{10}$^{[12]}. Fig. 1(a) and Fig. 1(b) show the inversion charge density and capacitance as a function of the gatesource
voltage according to the classical and QM models, respectively. The deviation between
the classical and QM models for the inversion charge density increases significantly
for higher doping concentration, and for the inversion layer capacitance, the results
coincide in weak inversion and deviate in strong inversion. For the feasibility of
QM model, results reported by other researchers^{[12]} are also demonstrated in Fig. 1(a). It is clearly shown that QM model are satisfactorily coincident with previous results.
Fig. 1. Quantum effects on (a) the inversion charge density $Q_{n0}$ versus the gatesource
voltage $V_{gs}$, (b) the inversion layer capacitance $C_{n}$ versus $V_{gs}$ for
$N_{sub}$ = 3×10^{17} cm^{3} and 3×10^{18} cm^{3}. ($t_{ox}$ = 2.5 nm, $V_{FB}$ = 0.9 V, $E_{ij}$ = $E_{10}$, $N_{it}$ = 2×10^{11} cm^{2}, $N_{poly}$ = 2×10^{20} cm^{3}, $T$ = 300 K).
Fig. 2(a) illustrates the gate leakage current noise characteristics as a function of drainsource
voltage for two channel lengths, verifying that the classical model have good agreement
with measurements for $t_{ox}$=2.5 nm ^{[24]}. For thinner oxide MOS structure, however. the oxide thickness shift due to QM effects
has a greater impact on the gate tunneling current than the oxide voltage shift due
to QM effects. By considering the QM effects on the oxide voltage and the oxide thickness,
the gate leakage tunneling current and noise model can be formulated. The oxide voltage
due to QM effects is calculated using the additional surface potential to achieve
the same inversion charge as the classical value. As illustrated in Fig. 2(b), the deviation between the classical and QM model increases with decreasing oxide
thickness. The use of the physical oxide thickness ignoring QM effects leads to erroneous
results in the calculation of the gate tunneling current noise. This observation is
in agreement with the results reported in ^{[31]}, showing similar values of normalized gate current noise for the oxide thickness
~1.5 nm.
Fig. 2. (a) Comparison of the simulation results of classical model and measured data
of the gate current noise as a function of drainsource voltage $V_{ds}$ ($t_{ox}$
= 2.5 nm), (b) quantum effect on the normalized $1/f$ noise of gate leakage current
$S_{Ig}$/$I_{g}$ versus the gatesource voltage $V_{gs}$ for two oxide thickness ($N_{sub}$
= 3×1017 cm^{3}, $V_{FB}$ = 0.9 V, $E_{ij}$ = $E_{10}$, $N_{it}$ = 2×10^{11} cm^{2}, $N_{poly}$ = 2×1020 cm^{3}, $T$ = 300 K).
On the basis of the inversion charge model including QM effects, the drain current
and noise model is evaluated for ultrathin oxide MOS device ($t_{ox}$ = 1.5~2.5 nm).
Prior to the evaluation of $1/f$ noise model, it is necessary to investigate QM effects
on the RTS fluctuations which are the origin of $1/f$ noise. The QM and polysilicon
depletion effects on the number fluctuation factor R0 versus the gatesource voltage
are plotted in Fig. 3(a). Due to the inversion layer offset, the measured oxide capacitance is considerably
smaller than the one based on the physical oxde thickness 2.5 nm^{[32]}. As a consequence, the QM and polysilicon depletion effects are appreciably enhanced
in the strong inversion. Fig. 3(b) shows QM effects on the scattering coefficient S as a function of the gatesource
voltage. For higher substrate doping concentration, QM effects become more noticeable.
The higher doping concentration yields lower inversion charge density. This explains
why the scattering coefficient increases for higher coping concentration. The circle
symbols in Fig. 3(b) represent the extracted scattering parameter reported by [4]^{[4]}, which includes both remote Coulomb scattering and surface roughness. Based on number
and mobility fluctuations, the RTS amplitude versus the surface potential is evaluated
using the QM correction charge model, as shown in Fig. 4. The RTS noise increases rapidly as the surface potential decreases. This same trend
is also observed for the devices with highly scaled channel length ~0.1mm^{[33]}. Note that in the case of number fluctuations, the classical and QM models exactly
coincide in the strong inversion and somewhat deviate in
the weak inversion.
Fig. 3. Quantum effects on (a) the number fluctuation factor $R_{0}$ versus the gatesource
voltage $V_{gs}$ with the polysilicon depletion effects, (b) the scattering coefficient
$S$ versus the gatesource voltage $V_{gs}$ for two substrate doping densities ($t_{ox}
= 2.5 nm, $V_{FB}$ = 0.9 V, $E_{ij}$ = $E_{10}$ , $T$ = 300 K).
Fig. 4. Quantum effects on the fractional RTS amplitude $\Delta I_{d} / I_{d}$ versus
the surface potential s y ($t_{ox}$ = 2.5 nm, $N_{sub}$ =3×10^{17} cm^{3}, $V_{FB}$ = 0.9 V, $E_{ij}$ = $E_{10}$ , $S_{0}$ = 8×10^{9} cm/C^{1/2}Vs, $k_{bulk}$ = 0.625, $T$ = 300 K).
Fig. 5 shows the comparison of measured data for $1/f$ drain current noise and simulation
results carried out with the classical noise model, accurately predicting the noise
behavior^{[27]}. However, for higher doping concentration and shorter channel length, the deviations
are pronounced, as illustrated in Fig. 6(a). The classical and QM models of $1/f$ drain current noise behave in a qualitatively
similar fashion to the RTS noise in the weak and strong inversion, showing the deviation
in the saturation region. It can be seen for highly scaled devices ($L=0.1 \mu \mathrm{m}$)
that QM model reproduces measurements in [34]^{[34]} relatively good for all bias conditions, whereas classical model has specific insufficiencies,
especially for higer darinsource voltage. Fig. 6(b) illustrates the thermal noise spectral power density of drain current as a function
of the drainsource voltage for different gatesource voltage. The simulation is verified
with data extracted from noise measurements^{[35]}. The inclusion of QM effects can improve the accuracy of the thermal noise model
in nanoscale channel length.
Fig. 5. Comparison of the simulation results of classical model and measured data
of the drain current noise as a function of drainsource voltage for two channel lengths
($t_{ox}$ = 2.5 nm, $N_{sub}$ = 2×10^{16} cm^{3}, $N_{t}$ =2×10^{17} cm^{3}eV^{1}).
Fig. 6. Quantum effects on (a) the $1/f$ noise power of drain current versus the drainsource
voltage $V_{ds}$, (b) the thermal noise power of drain current versus the drainsource
voltage $V_{ds}$ for two gatesource voltages ($N_{sub}$ = 3×10^{17} cm^{3}, $V_{FB}$ = 0.9 V, $E_{ij}$ = $E_{10}$ , $v_{sat}$ = 8×10^{6} cm/s, $R_{s,d}$ =10 Ω, $k_{bulk}$ = 0.625, $T$ = 300 K).
The channel doping impacts are important to both the $1/f$ noise and the thermal noise
modeling. Obvious increases in the $1/f$ noise are observed at higher doping concentration
owing to channel nonuniformity, especially in subthreshold region^{[36,}^{37]}. On the contrary, the thermal noise decreases for higher doping concentration, due
to increased mobile charge^{[38]}. As shown in Fig. 7, it is confirmed that for higher doping concentration and thinner oxides, QM effects
in the inversion layer should be considered for accurate noise modeling of nanoscale
MOSFETs.
Fig. 7. Quantum effects on the total noise power of drain current versus the frequency
$f$ for two substrate doping concentrations ($V_{FB}$ = 0.9 V, $E_{ij}$ = $E_{10}$,
$v_{sat}$ = 8×10^{6} cm/s, $R_{s,d}$ = 10 Ω, $k_{bulk}$ = 0.625, $T$ = 300 K).
VI. CONCLUSION
The current and noise models accounting for QM effects in nanoscale MOSFETs were presented.
Simple and analytical models were formulated by considering the QM effects due to
the lowest energy level in the inversion layer. The inversion charge parameters were
studied and treated quantummechanically, because good modeling of current and noise
depends strongly on the accurate estimate of the inversion charge due to QM effects.
The gate leakage current noise models were evaluated by using the quantum correction
model for the oxide voltage and oxide thickness. By accounting for the QM effects
through the inversion charge density model, the quantum models of the drain current
and noise were obtained and compared with the classical models. The simulation results
showed that 1) the quantum correction of the oxide thickness is essential to the accurate
prediction of the gate leakage current and noise model, 2) QM effects on the 1/f noise
and the thermal noise of drain current is significant in the saturation region, similar
to the drain current characteristics, and 3) QM effects on the noise model become
more pronounced for higher doing concentration and thinner oxide thickness. Therefore,
as the MOS devices are scaled down to nanometer regime, existing current and noise
models should incorporate more accurate, physicsbased descriptions which account
for quantization effects observable in the inversion layer.
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Author
was born in Seoul, Korea, on February2, 1969.
He received the M.S. degrees from the Inha University, Incheon, Korea, and the Ph.D.
degree in electrical & computer engineering from the University of Florida, Gainesville,
in 1993 and 2003, respectively.
In 2017, he joined the Department of System Semiconductor Engineering, Sangmyung University,
Korea, where he is currently a Professor.
From 2003 to 2016, he was with Samsung Display Inc., Korea, as a Principal Research
Engineer, where he worked on semiconductor and display devices design and development.
His current research interests include semiconductor device modeling and simulation,
physicsbased artificial neural network, noise modeling in nanoscale MOSFETs, display
device design and processarchitecture, and thermoelectric transport modeling.
He has published more than 30 refereed journal and conference papers on these topics,
and holds more than 20 patents in the field of display and semiconconductor devices.
was born in Seoul, Korea, in 1972.
He received the B.S. degree in Computer Engineering from Kwangwoon University, Seoul,
Korea, in 1997, and the M.S. and Ph.D. degrees at Yonsei University, Seoul, Korea,
in 1999 and 2003, respectively.
From 2002 to 2006, he was a Senior Researcher at the wireless PAN technology project
office, Korea Electronics Technology Institute (KETI), KyungGiDo, Korea.
In 2006, he joined the Department of System Semiconductor Engineering, Sangmyung University,
Korea, where he is currently a Professor.
From 2016 to 2017, he served as dean of the Sangmyung University School of Engineering.
He is the leader of the PRIME, WeUp, and CK projects funded by national research foundation
of Korea from 2014 to the present.
His research interests are in the areas of system on chip.