# Semi-analytical model for schottky-barrier carbon nanotube and graphene nanoribbon transistors.

**ABSTRACT** This paper describes a physics-based semi-analytical model for Schottky-barrier carbon nanotube (CNT) and graphene nanoribbon (GNR) transistors. The model includes the treatment of (i) both tunneling and thermionic currents, (ii) ambipolar conduction, i.e., both electron and hole current components, (iii) ballistic transport, and (iv) multi-band propagation. Further, it reduces the computational complexity in the two critical and time-consuming steps, namely the calculation of the tunneling probability and the self-consistent evaluation of the the surface potential in the channel. When validated against NanoTCAD ViDES, a quantum transport simulation framework based on the non-equilibrium Green's function method, it is several orders of magnitude faster without significant loss in accuracy. Since the model is physics-based, it is parameterizable and can be used to study the effect of common parametric variations in CNT diameter and GNR width, Schottky-barrier height, and insulator thickness.

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**ABSTRACT:**Contact effects of carbon nanotubes to metallic electrodes have a big impact on the electronic transport in CNT-based structures. In general there are two expected types of contacts, Schottky type with semiconducting tubes and ohmic contact with semiconducting and metallic tubes. However not always perfect contacts come, it is rather a tunneling barrier contact because of the weak coupling to the metal electrode or the formation of thin layer of oxides at the interface, for examples. We propose a simple model for non-ideal contacts of metallic single walled nanotube in order to calculate the overall resistance and hence the contact resistance. The model takes into account the findings of both experiments and theories such as effect of work function, electron phonon scattering in low and high bias voltage, image potential and van derWaals distance at the interface. The model can be developed to calculate the contact resistance for strips of several SWNTs or multi walled carbon nanotubes.Transaction on Systems, Signals and Devices. 11/2013; Vol. 8(4):427-439.

Page 1

Semi-analytical Model for Schottky-barrier

Carbon Nanotube and Graphene Nanoribbon Transistors

Xuebei Yang†, Gianluca Fiori‡, Giuseppe Iannaccone‡, and Kartik Mohanram†

†Department of Electrical and Computer Engineering, Rice University, Houston

‡Information Engineering Department, University of Pisa, Pisa

xbyang@rice.edu g.fiori@iet.unipi.it g.iannaccone@iet.unipi.itkmram@rice.edu

Abstract

This paper describes a physics-based semi-analytical model for

Schottky-barrier carbon nanotube (CNT) and graphene nanorib-

bon (GNR)transistors. Themodel includes thetreatment of(i)both

tunneling and thermionic currents, (ii) ambipolar conduction, i.e.,

both electron and hole current components, (iii) ballistic transport,

and (iv) multi-band propagation. Further, it reduces the compu-

tational complexity in the two critical and time-consuming steps,

namely the calculation of the tunneling probability and the self-

consistent evaluation of the the surface potential in the channel.

When validated against NanoTCAD ViDES, a quantum transport

simulation framework based on the non-equilibrium Green’s func-

tion method, it is several orders of magnitude faster without sig-

nificant loss in accuracy. Since the model is physics-based, it is

parameterizable and can be used to study the effect of common

parametric variations in CNT diameter and GNR width, Schottky-

barrier height, and insulator thickness.

Categories and Subject Descriptors: B.7.1 [Integrated circuits]:

Types and Design Styles—Advanced technologies

General Terms: Design, Performance

Keywords: Carbon nanotubes, graphene nanoribbons, Schottky-

barrier, models

1.Introduction

Carbon-based materials such as carbon nanotubes (CNTs) and,

morerecently, graphenenanoribbons(GNRs), haveattractedstrong

interest as alternative device technologies for future nanoelectron-

ics applications [1–4]. Devices based on these materials offer high

mobility for ballistic transport, low drain-induced barrier lowering,

high mechanical and thermal stability, and high resistance to elec-

tromigration. Although different families of CNTFETs and GNR-

FETs have been fabricated and studied, the most important distinc-

tion is between Schottky-barrier-type (SB-type) and MOSFET-type

FETs [5–7]. SB-type FETs (SBFETs henceforth) are the most eas-

ily fabricated devices, since they use intrinsic CNT/GNR channels

with metallic drain and source contacts. MOSFET-type devices are

characterized by doped CNT/GNR channels and Ohmic contacts,

and pose more engineering challenges. In SBFETs, a SB is formed

between the channel and source/drain contacts and the gate modu-

latesthequantumtunnelingcurrentthroughtheSB[5]. Itisthetun-

neling current, as opposed to the thermionic current in MOSFET-

This research was supported by NSF grant CCF-0916636.

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type devices, that dominates the device transport. SBFETs are am-

bipolar, i.e., they conduct both electrons and holes, showing a su-

perposition of n- and p-type behavior.

Modeling approaches for such novel devices [8] are based on

(i) computationally intensive, quantum-theory-based non-equilib-

rium Green’s function (NEGF) approaches [9, 10] or (ii) simpler

semi-classical approaches [11–18]. NEGF-based approaches are

highly accurate but extremely time consuming. Further, they pro-

vide limited intuition necessary for circuit design and optimization

with multiple devices. In comparison to NEGF-based approaches,

the simpler semi-classical approaches are computationally very ef-

ficient. Further, they are physics-based and parameterizable, pro-

viding good intuition to designers. It has been shown that for

MOSFET-type devices, a semi-classical description is valid for a

channel length down to about 10nm [12–14,19].

However, when GNRFETs and CNTFETs are SBFETs where

switching behavior stems fromthe gate modulation ofthe tunneling

probability through the source/drain-channel contacts, a quantum-

mechanical description is indispensable [5]. State-of-the-art mod-

els for SBFETs, such as [15–18], are based on the semi-classical

approach augmented with quantum theory to handle tunneling in

SBFETs. The earliest model described in [15] was more efficient

than NEGF-based approaches, but still infeasible for routine sim-

ulations required for circuit design due to two computationally in-

tensive steps: (i) the calculation of tunneling probability at the SBs

and (ii) the self-consistent evaluation of the surface potential in the

channel. In [16], the tunneling probability and the surface poten-

tial are obtained using approximate closed-form expressions at the

cost of physical intuition. In [17,18], the models are made com-

putationally efficient by considering SBFETs in only the quantum

capacitance limit to eliminate the need for self-consistent solution.

However, this restricts the use of the model to ultimately scaled

devices free of parasitics.

This paper describes an accurate and computationally efficient

physics-based semi-analytical model for SB-type CNTFETs and

GNRFETs. The model includes the treatment of (i) both tunnel-

ing and thermionic currents, (ii) ambipolar conduction, i.e., both

electron and hole current components, (iii) ballistic transport, and

(iv) multi-band propagation. Further, it reduces the computational

complexity in the two critical and time-consuming steps of the

semi-classical SBFET modeling approach. First, in the calculation

of the tunneling probability through the SB using the Wentzel-Kra-

mers-Brillouin (WKB) approach, we propose linear solutions to

substitute for the computationally intensive integral for tunneling

probability. Second, in the self-consistent loop to evaluate the sur-

face potential in the channel, we simplify the process to calculate

the carrier densities by identifying multiple regions with closed-

form solutions. Since the proposed model is physics-based and

doesnotrelyonfittingparameters, itcan beusedtostudyvariations

in device parameters including CNT diameter and GNR width, in-

233

Page 2

Gate

Insulator

CNT

Insulator

Gate

D

S

L = 20nm

(a)

tins=1nm

d=1nm

Insulator

thickness (tins)

CNT

Insulator

Gate

(b)

Figure 1: Coaxial CNTFET. (a) and (b) are cross sections along

and perpendicular to the channel direction of the CNTFET.

sulator thickness, and SB height. When compared to the original

semi-classical SBFET model and an NEGF-based quantum trans-

port simulation framework NanoTCAD ViDES [10], the proposed

model is faster by one and four orders of magnitude, respectively,

without significant loss in accuracy.

The paper is organized as follows. We introduce semi-classical

SBFET modeling in section 2. In section 3, we describe our ap-

proach to reduce the computational complexity of semi-classical

SBFET modeling. Section IV presents simulation results and sec-

tion V is a conclusion.

2.SBFET modeling

Although we consider a coaxial gate geometry SB-type CNT-

FET with the structure shown in Figure 1 to describe SBFET mod-

eling, the proposed approach can treat other geometries and chan-

nel materials, as shown for a double-gated GNRFET in Sec. 4.2.

The current through the SBFET is calculated using the widely-used

Landauer formula [20] and is given by the following expressions:

Z∞

Ihole =2q

h

−∞

I = Ielectron+ Ihole

Ielectron =2q

h

−∞

Z∞

(f(E − EFS) − f(E − EFD))Te(E)dE

(f(EFD− E) − f(EFS− E))Th(E)dE

where f() is the Fermi function, EFSand EFDare the Fermi levels

at the source and the drain, respectively, and Te (Th) is the trans-

mission coefficient for electrons (holes). When all terminal biases

are added, the only parameters that remain unknown are the trans-

mission coefficients at the contacts.

Calculating the transmission coefficients: At each contact the

transmission coefficients for electron current and hole current need

to be calculated, for a total of four coefficients: TSe, TSh, TDe and

TDh. The final transmission coefficients for electrons Te and holes

Thare obtained by combining TSe, TDeand TSh, TDh.

The current is divided into two components: thermionic current,

which flows above the SBs and tunneling current, which flows

?

??????

?????

??

???????

??????

??

?

??

(a)(b)

Figure 2: Illustration of the classical turning points. Note that

in (b) there is band-to-band tunneling.

????????

??

????

???

???

?? ?

?????

???

??

??

?????? ???????

?????

???????????

Figure 3: Spatial energy band diagram along the transport di-

rection. EFSand EFDare source and drain Fermi levels.

through the SB. Whereas the transmission coefficient is 1 for the

thermionic current, it is given by the tunneling probability for the

tunneling current. The tunneling probability is calculated using the

simple but accurate Wentzel-Kramers-Brillouin (WKB) approach

that has been widely used in literature [21]. Based on the WKB

approach, the tunneling probability T is given by

„

where zinit and zfinal are the classical turning points, illustrated in

Figure 2, and kzis the parallel momentum related to the E-k rela-

tionship of CNTs. Consider electron tunneling for example. kz is

given by the expression:

s

kn =|3n − 4|

T = exp

−2

Zzfinal

zinit

kz(z)dz

«

(1)

kz =

k2

n−

„Eg/2 − (EC(z) − E)

1.5ac-cVppπ

«2

3R

where knis the perpendicular momentum of the nth sub-band, Eg

is the energy band-gap, EC(z) is the bottom of the conduction band

in the z direction, R is the radius of the CNT, ac-c is the carbon-

carbon bond distance [22], and Vppπ is the carbon-carbon bonding

energy [23]. Neglecting phase coherence, the overall transmission

coefficient is given by [20]:

Te(h) =

TSe(Sh)TDe(Dh)

TSe(Sh)+ TDe(Dh)− TSe(Sh)TDe(Dh)

Modeling the energy band diagram: In order to evaluate the

transmission coefficients for the electron and hole tunneling cur-

rent, the energy profile for the bottom of conduction band EC(z)

and the top of valence band EV(z) need to be modeled, respec-

tively, as illustrated in Figure 3. For a long channel, where the SBs

at the source and drain contacts do not influence each other, the

bottom of the conduction band inside the channel Ebot is given by

the expression:

Ebot = ϕSB+ Uscf+ qVfb

where ϕSBis the SB height, Uscfis the surface potential, and Vfbis

the flat band voltage. For the coaxial gate geometry, the conduction

band ECnear the two contacts can be modeled as [17]:

EC,left(z) = ϕSB− (ϕSB− Ebot)(1 − e

EC,right(z) = (ϕSB− qVDS) − (ϕSB− Ebot− qVDS)(1 − e

where tinsis the thickness of insulator and L is the channel length.

Similarly, the top of the valence band is shifted down by a value of

Eg, which is the energy gap of the CNT.

−2z

tins )

−2(z−L)

tins

)

234

Page 3

Determining the surface potential: The surface potential Uscfstill

remains to be evaluated in order to model the energy band diagram.

It is solved through a self- consistent loop [12,14,15] according to:

Uscf = UL+ UP

where ULis the Laplace potential due to the applied terminal biases

and UPis the potential due to the change in carrier densities. If the

substrate is sufficiently thick, UL = −qVG. Evaluating UPis com-

putationally demanding, however, since it depends on the change

in carrier densities as explained below. Consider electrons for il-

lustration. When the terminal biases are zero, the electron density

in the channel is:

Z∞

where D(E) is the density of states at the bottom of the conduction

band [20] and f(E − EF) is the Fermi function. Note the integral

is from the bottom of the conduction band to infinity. When the ter-

minal biases are not zero, the device is not at equilibrium and the

states at the bottom of the conduction band are filled by two differ-

ent Fermi levels. States with positive velocity (N+) and negative

velocity (N−) are filled by electrons according to:

Z∞

Heref+andf−aretheFermifunctionsmodulatedbytheSBs[15].

UPis then evaluated as:

N0 =

Ebot

D(E)f(E − EF)dE

N+(−)=1

2

Ebot

D(E − Uscf)f+(−)dE

UP =

q2

CinsΔN =

q2

Cins(N++ N−− N0)

where Cinsis the insulator capacitance. Since this UPis the poten-

tial due to the density change in electrons, the effects of holes must

be added to obtain:

UP =

q2

Cins(ΔNe− ΔNh)

The most computationally intensive steps of SBFET modeling is

thustheself-consistentsolutionforthesurfacepotentialUscf, which

has to be repeated for each combination of terminal biases. Within

the self-consistent loop, the core computational steps are (i) the

evaluation of carrier densities, including electron density and hole

density, and(ii)thetransmissioncoefficientsTSandTDatthesource

and drain contacts for both electrons and holes. The next section

describes the key contributions of this paper to reduce the compu-

tational complexity of these steps in SBFET modeling.

3.Reducing computational complexity

In this section, we describe two key simplifications to greatly

reduce the computational complexity in SBFET modeling. These

simplifications do not rely on fitting parameters, and hence pre-

serve the physics-based properties of the semi-classical approach

necessary for parameterized circuit design and optimization.

3.1The transmission coefficients

While evaluating the transmission coefficient for tunneling cur-

rent, the integral of kzin the WKB approach (Equation 1) has to be

solved numerically. Traditionally, kzis calculated at each grid point

in the channel between the two turning points and is computation-

ally expensive. In this paper, we derive linear solutions to replace

this integral as follows. Without loss of generality, consider kz in

10−6

44

10−5

6

8x 108

??

?

00.1 0.2

1

0.30.4

2

0.50.6

3

0.70.8

4

10−7

0

0

2

? ????

(a)

44

6

8x 108

??

?

012345

0

2

? ????

(b)

Figure 4: kz(z) when (a) EC(zinit) − E < Eg/2 and (b)

EC(zinit) − E > Eg/2

the lowest sub-band for the electron tunneling current.

s

1.5ac-cVppπ

s„

s„ac-cVppπ

s„Eg

Note that EC(z)−E monotonically decreases from zinitto zfinaland

EC(zfinal) = E. Considering the following two cases:

kz(z) =k2

n−

„Eg/2 − (EC(z) − E)

«2

«2

=

1

3R

−

„Eg/2 − (EC(z) − E)

«2

«2

1.5ac-cVppπ

«2

=

1

1.5ac-cVppπ

2R

−

„Eg

2

− (EC(z) − E)

«2

=

1

1.5ac-cVppπ

2

−

„Eg

2

− (EC(z) − E)

«2

(i) EC(zinit) − E < Eg/2: As shown in Figure 4(a), kz(z) de-

creases monotonically. The integral in Equation 1 is the area be-

tween the x-axis, y-axis, and kz(z), which is roughly the area of

a triangle. Hence, the integral can be simplified to the product of

kz(zinit+ zfinal

2

) and zfinal− zinit.

(ii)EC(zinit)−E > Eg/2: AsshowninFigure4(b), kz(z)increases

first, then decreases monotonically. The integral in Equation 1, or

the area between the x-axis, y-axis, and kz(z), can be regarded as

the area of a triangle and a trapezoid. The triangle and the trapezoid

are separated at zpeak, where zpeaksatisfies EC(zpeak) − E = Eg/2

and kz(zpeak) reaches its maximum. From the model for the energy

band diagram,

zpeak =tins

2

ln

ϕSB− Ebot

E + Eg/2 − Ebot

Hence, the integral can be simplified to

kz(zpeak)

2

Therefore, instead of computing kz at each grid point in the chan-

nel between the two turning points, the transmission coefficients

can be evaluated using only one or two expressions for kz. This

reduces the computation time significantly, with negligible impact

on accuracy, as presented in Sec. 4.

(zfinal− zpeak) +(kz(zinit) + kz(zpeak))

2

(zpeak− zinit)

3.2Electron and hole densities

In the self-consistent loop, the calculation of UPis computation-

ally demanding since the change in both electron density and hole

density must be evaluated numerically at each grid point. In our

work, we propose a transformation that simplifies the calculation

of carrier densities as follows. Without loss of generality, consider

electron density. The density of electrons in positive velocity states

N+and in negative velocity states N−are:

235

Page 4

00.050.10.15 0.20.250.30.35 0.4 0.45 0.5

0

0.5

1

1.5

2

2.5

3

3.5x 10−4

E (eV)

(a)

P(E)

00.1 0.20.3 0.40.50.60.70.8

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

E (eV)

(b)

P(E)

00.10.20.30.40.50.60.70.80.91

0

0.1

0.2

0.3

0.4

0.5

0.6

E (eV)

(c)

P(E)

Figure 5: (a) Ebot > Eexp, (b) Ebot < Eexpand ϕSB− Ebot < VDS, (c) Ebot < Eexp, ϕSB− Ebot > VDSand ϕSB− VDS< Eexp

N+=1

2

Z∞

Z∞

Ebot

D(E − Uscf)f+dE

=1

2

Z∞

Ebot

D(E − Uscf)TSfS+ TDfD− TSTDfD

TS+ TD− TSTD

D0(E − Ebot+ Eg/2)(TSfS+ TDfD− TSTDfD)

2p(E − Ebot+ Eg/2)2− (Eg/2)2(TS+ TD− TSTD)

dE

=

Ebot

dE

Similarly,

N−=

Z∞

Ebot

D0(E − Ebot+ Eg/2)(TSfS+ TDfD− TSTDfS)

2p(E − Ebot+ Eg/2)2− (Eg/2)2(TS+ TD− TSTD)

Here, fS and fD are Fermi function of the source and the drain

respectively. The total electron density is equal to N++N−. Since

both fS and fD that include exponential terms appear in both N+

and N−, we rearrange terms to simplify the integrals as follows.

dE

N1=D0

2

Z∞

Z∞

Ebot

E − Ebot+ Eg/2

p(E − Ebot+ Eg/2)2− (Eg/2)2

E − Ebot+ Eg/2

p(E − Ebot+ Eg/2)2− (Eg/2)2

Note that since N++ N−is equal to N1+ N2, rearranging terms

does not change the final result. By making the substitution

p

and setting

p

N1and N2can be written as:

Z∞

Z∞

The integrals are then simplified as follows, and we illustrate this

by considering N1:

Z∞

=D0

2

0

=D0

2

0

=D0

2

0

2TSfS− TSTDfS

TS+ TD− TSTD

2TDfD− TSTDfD

TS+ TD− TSTD

dE

N2=D0

2

Ebot

dE

E?=

(E − Ebot+ Eg/2)2− (Eg/2)2

E =

(E?)2+ (Eg/2)2− Eg/2 + Ebot,

N1 =D0

2

0

2TS(E) − TS(E)TD(E)

TS(E) + TD(E) − TS(E)TD(E)fS(E)dE?

N2 =D0

2

0

2TD(E) − TS(E)TD(E)

TS(E) + TD(E) − TS(E)TD(E)fD(E)dE?

N1 =D0

2

0

2TS(E) − TS(E)TD(E)

TS(E) + TD(E) − TS(E)TD(E)fS(E)dE?

T(E)fS(E)dE?

Z∞

Z∞

Z∞

P

“p

(E?)2+ (Eg/2)2− Eg/2 + Ebot

”

dE?

P(E?)dE?

(2)

In order to simplify the integral, it is necessary to investigate the

shape of P(E?). P(E?) is the product of two terms: the Fermi

function and the transmission coefficient (T-term). At low ener-

gies, the Fermi function is always close to 1 and the T-term dom-

inates the shape of P(E?). When the energy increases, the Fermi

function has an exponential behavior, and changes in the value of

the T-term are slow in comparison to the exponential fall-off in

the value of the Fermi function. Hence, the shape of P(E?) is

dominated by the Fermi function in this regime. We assume that

Fermi function begins to exhibit exponential behavior at the energy

E?= Eexp where fS(pE2

lowing regimes, as illustrated in Figure 5:

exp+ (Eg/2)2− Eg/2 + Ebot) ≈ 0.90.

With this boundary condition, we divide the problem into the fol-

1. Ebot > Eexp

P(E?) is monotonically decreasing in this situation. In order

to simplify the integral, we use the bisection method to find

the point E?= Emwhere P(Em) = 1/2P(0), and approxi-

mate the integral in Equation 2 to 2P(Em)·Em.

2. Ebot < Eexpand ϕSB− Ebot < VDS

Under these conditions, there are two regions separated by

Eexp. In the first region, the Fermi function is almost con-

stant and the T-term dominates the shape of the curve. Upon

closer examination, it is clear that the T-term is equal to

TS(E) because TD(E) = 1. We use a linear function to sim-

plify the integral by calculating TS(Eexp/2), and simplify the

integral in this region to TS(Eexp/2)·Eexp. In the second re-

gion, the Fermi function dominates, and the conditions are

similar to Ebot > Eexpabove. The total value of the integral

in Equation 2 is given by the sum of the two regions.

3. Ebot < Eexpand ϕSB− Ebot > VDS

Under these conditions, there may be two or three regions,

depending on whether ϕSB − VDS is above Eexp or not. If

ϕSB−VDS ≥ Eexp, it is similar to condition (2) above. There

are two regions, with the T-term dominating the first. Al-

though the T-term is not equal to TS(E), it is still possible

to use a linear function to simplify the integral. In the sec-

ond region, the Fermi function dominates and it is equivalent

to case (1) above. If ϕSB− VDS < Eexp, there are three re-

gions. The first interval is from from 0 to the point where

p(E?)2+ (Eg/2)2− Eg/2 + Ebot = ϕSB− VDS. If the en-

the source has SBs. In both regions, we use a linear func-

tion to simplify the integral. Finally, the third region uses the

same conditions as (1) above.

ergy increases to Eexp, it is in the second region where only

Although the self-consistent loop is still needed to evaluate Uscf,

simplifying the calculation of the transmission coefficients and the

carrier densities reduces the computational burden in the two core

236

Page 5

loops significantly. Hence, the overall complexity of the semi-

classical SBFET model is reduced without loss in accuracy.

4. Results and discussions

We begin by comparing the results of our proposed semi-ana-

lytical model to the original semi-classical model as well as to

the rigorous quantum atomistic simulator NanoTCAD ViDES [10].

We consider a CNTFET with coaxial gate geometry and a 20nm

long (13,0) CNT channel. The gate insulator is SiO2 with tins =

1nm. The SB height is assumed to be half of the band-gap. In

Figure 6, we compare the I-V curves obtained from our semi-

analytical model, the original semi-classical model, and NanoT-

CAD ViDES, respectively, for several bias points. Ambipolar char-

acteristics due to both electron and hole conduction are clearly

shown, and the drain voltage exponentially increases the minimum

leakage current. The point of minimum current is at VGS = VDS/2.

As drain voltage increases, SBFETs show linear behavior in the

overall range of gate bias. For example, the drain current and

the channel charge for VDS = 0.8V are linearly proportional to

VGS, whereas those for VDS = 0.4V show exponential behavior in

the sub-threshold region. Note that the difference between the re-

sults of the proposed semi-analytical model and the original semi-

classical model is very small, which demonstrates that our sim-

plification does not result in significant loss of accuracy. In com-

parison, the difference between the results of the proposed model

and NanoTCAD ViDES is slightly higher. This can be attributed

to the difference in the calculation of the transmission coefficients

between the semi-classical and quantum approaches. NanoTCAD

ViDES considers phase coherence of scattering at the two SBs and

the energy profile is more rigorous, which explains the oscillating

nature of the computed transmission coefficient. However, since

this treatment is at the atomic level and extremely computation-

ally intensive, it is not feasible in semi-classical approaches. Ta-

ble 1 presents the average computational time for 80 bias points

for NanoTCAD ViDES, the original semi-classical model, and the

proposed model. Our semi-analytical model is one order of mag-

nitude faster than the original semi-classical model and four orders

of magnitude faster than NanoTCAD ViDES.

Table 1: Average computational time for NanoTCAD ViDES,

original semi-classical SBFET model, and the proposed model.

ModelTime

NanoTCAD ViDES

Semi-classical SBFET model

Proposed semi-analytical model

8-10 hours

20-30 seconds

2-4 seconds

4.1Parameter variations

Parameter variations play an important role in CNTFET elec-

tronics because they significantly influence both the “on” and “off”

current. Typical parameters considered in CNTFET simulation and

design include CNT chirality, insulator thickness, and SB height.

Our model is able to simulate the effect of parameter variations. Al-

though prior work [9,15] has investigated these problems in detail

using rigorous simulation approaches, our semi-analytical model is

able to provide results that are consistent with these more rigorous

approaches in a fraction of the time. In this section, we will present

the simulation results of variations in CNT chirality (and hence,

diameter), insulator thickness, and SB height.

CNT chirality: The diameter of a CNT is determined by the chi-

rality, while the energy band-gap is inversely proportional to the

diameter. We simulated (11,0), (13,0), and (17,0) CNTFETs, and

10−6

10−5

10−4

00.10.20.3 0.4 0.50.60.70.8

10−9

10−8

10−7

??????????????

????????????????????

?????

Figure

0.4V, 0.6V, and 0.8V.

6:CNTFETmodel validationfor

VDS

=

10−6

10−5

10−4

???

???

??????

???????

??????????

????????????

0 0.10.20.3 0.4 0.50.60.70.8

10−9

10−8

10−7

??????????????

????????????????????

??????

? ?

??????

Figure 7: The effect of CNT chirality on the drain current.

VDS= 0.6V , tinsis 1.5nm, and channel length is 20nm.

the results are shown in Figure 7. As the diameter increases, the

band-gap decreases. This allows more electrons and holes in the

conduction and valence bands, respectively, thereby increasing the

current. However, a small band-gap also increases the “off” cur-

rent, so the Ion/Ioffratio is lower.

Insulator thickness: The insulator thickness tinsinfluences the SB

thickness and the gate capacitance. As tins increases, the SBs at

the two channel ends also become thicker and the gate capacitance

decreases. A low gate capacitance means the gate has less control

over the drain current, and a thick SB lowers the tunneling prob-

ability. Therefore, when tins increases, the current decreases. We

simulated a CNTFET with gate insulator thickness at 1nm, 2nm,

and 3nm, and the results are shown in Figure 8.

SB height: The most commonly studied devices are mid-gap SB-

FETs, i.e., ϕSB = Eg/2, and all the simulations so far have only

considered mid-gap transistors. Under this condition, the minimum

current occurs at VGS = VDS/2. However, depending on the contact

work functions, the SB height can differ from Eg/2 [24]. As the SB

height increases, the electron current is lowered and hole current is

larger, so that the right branch of the curve is shifted down, the

left branch shifted up, and the minimum current point moves to the

right. When the SB height decreases, the right branch shifts up, the

left branch shifts down, and the minimum current point shifts to the

left, as shown in Figure 9. Our results for ϕSB = 0, Eg/2, and Eg

are shown in Figure 9.

4.2 GNRFET simulation

So far, this paper has only considered CNTFETs. However, our

model can also be used to simulate GNRFETs. Since GNRs and

237

Page 6

10−6

10−5

???

???

????????

????????

??????????

00.1 0.2 0.30.40.5 0.60.70.8

10−7

??????

??????????????

????????????????????

????????

Figure 8: The effect of insulator thickness on CNTFET drain

current. VDS = 0.6V , default gate insulator dielectric constant

is 3.9, and channel length is 20nm.

10−6

10−5

10−4

???

???

???????

??????????

??????

??????????

????????????

0 0.10.2 0.30.4 0.50.60.70.8

10−9

10−8

10−7

??????

??????????????

????????????????????

Figure 9: The effect of SB height on CNTFET drain current.

VDS= 0.4V , tinsis 1.5nm, and channel length is 20nm.

CNTs share many properties, only a few changes are needed. The

major changes include: (i) the energy band-gap of a GNR is deter-

mined by its width instead of the chiralities, (ii) GNRFETs usually

do not have coaxial structures, so the expressions for gate capaci-

tance and energy band diagram need to be changed depending on

the structure (double-gate in our case), and (iii) kz [18] for GNRs

has a linear relationship with energy that makes the integral of kz

in the WKB even simpler. By making these changes, our model is

compatible with GNRFETs, and the major ideas and simplification

approaches mostly remain valid. Figure 10 presents results com-

paring our model and NanoTCAD ViDES for a double-gated GN-

RFET with an N=12 armchair-edge GNR channel and tins = 2nm.

Further, theproposedmodelcanalsoincorporatetheeffectsofedge

bond relaxation and third nearest neighbor interactions that have

been shown to play an important role in GNRs.

5.Conclusions

Current models for Schottky-barrier carbon nanotube and gra-

phene nanoribbon FETs are either computationally expensive or

unable to provide physical intuition for device parameter optimiza-

tion. This paper described a semi-analytical model for such FETs

based on physics-based methods to reduce the computational com-

plexity by simplifying the calculation of the transmission coeffi-

cient and the carrier densities. When compared to the quantum-

theory-based simulator NanoTCAD ViDES and the original semi-

classical model, the proposed model is four orders and one order of

magnitudefaster, respectively, without significantlossofaccuracy.

00.10.20.30.40.5 0.60.7 0.8

10−8

10−7

10−6

10−5

VGS(V)

IDS(A)

Proposed model

ViDES

VDS= 0.6V

VDS= 0.5V

VDS= 0.4V

N=12 AGNR channel

tins= 2nm

Figure 10: Results for a GNRFET with N=12 armchair-edge

GNR channel and tins= 2nm.

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