# Utilizing the Janus MoSSe surface polarization property in complementary
metal–oxide–semiconductor field effect transistor design

Yun-Pin Chiu  Hsin-Wen Huang  Yuh-Renn Wu<EMAIL_ADDRESS>Graduate Institute
of Photonics and Optoelectronics and Department of Electrical Engineering,
National Taiwan University, Taipei 10617, Taiwan

###### Abstract

Janus transition metal dichalcogenides (JTMDs) have attracted much attention
because of their outstanding electronic and optical properties. The additional
out-of-plane dipole in JTMDs can form n- and p-like Ohmic contacts, and this
may be used in device applications such as pin diodes and photovoltaic cells.
In this study, we exploit this property to design n- and p-type
metal–oxide–semiconductor field effect transistors (MOSFETs). First, we use
density-functional theory calculations to study the inherent dipole field
strength in the trilayer JTMD MoSSe. The intrinsic dipole of MoSSe causes band
bending at both the metal/MoSSe and MoSSe/metal interfaces, resulting in
electron and hole accumulation to form n- and p-type Ohmic contact regions. We
incorporate this property into a 2D finite-element-based
Poisson–drift–diffusion solver to perform simulations, on the basis of which
we design complementary MOSFETs. Our results demonstrate that JTMDs can be
used to make n- and p-MOSFETs in the same layer without the need for any extra
doping.

††preprint: AIP/123-QED

##  I Introduction

In the field of two-dimensional (2D) semiconducting materials, research on
transition metal dichalcogenides (TMDs) has been flourishing in recent years
owing to their remarkable properties, such as relatively high carrier
mobilities,Chhowalla, Jena, and Zhang (2016a); Chen  _et al._ (2022a);
Radisavljevic  _et al._ (2011) suitable bandgap energies,Ye, Zhang, and Yap
(2017); Chaves  _et al._ (2020) and an atomic thickness that is
modifiable.Serna  _et al._ (2016); Zhang  _et al._ (2013); Orofeo  _et al._
(2014) These properties make 2D TMDs suitable for various device
applicationsLiu  _et al._ (2021); Cho, Park, and Kwon (2015); Ying-Chuan_KMC;
Xiong  _et al._ (2021); 2D_Photo, especially for replacing traditional channel
materials and overcoming the barrier of short-channel effects in smaller field
effect transistors (FETs). Liu  _et al._ (2021); Chhowalla, Jena, and Zhang
(2016b); 2D_FET_Pin-Fang These TMD-based channel FETs have been studied for
over a decade, and FETs have been successfully fabricated with TMD materials.
For example, MoS2 and WS2 are two commonly selected materials for n-type
channels, and $\alpha$-MoTe2 and WSe2 are both good candidates for p-type
channels.Radisavljevic  _et al._ (2011); Pezeshki  _et al._ (2016);
Ovchinnikov  _et al._ (2014); Cho, Park, and Kwon (2015); Zhang  _et al._
(2021)

It is well known that the contact characteristics of a FET device will
strongly affect its $I$–$V$ characteristics and performance. For low contact
resistance, Ohmic contacts are usually required at the semiconductor–metal
interface to enhance the electrical performance of FETs while maintaining a
linear $I$–$V$ curve.Sze, Li, and Ng (2021); Allain  _et al._ (2015); Zheng
_et al._ (2021) Unfortunately, Schottky barriers (SBs) are usually formed for
most TMD/metal contacts.Allain  _et al._ (2015); Kang  _et al._ (2014);
Ouyang, Xiong, and Jing (2018); Tang  _et al._ (2019); Zheng  _et al._ (2021)
For an FET with SB contacts, carriers must overcome the energy barrier to
transport from metal into the TMD channel region, impairing the device
function by lowering the injection efficiency.Tung (2014) A typical technique
used to reduce contact resistance in modern semiconductor technology is
substitutional doping.Younas, Zhou, and Hinkle (2023) However, there are
better ways to apply such a doping method to 2D materials, since their
characteristics will be heavily altered after doping. Additionally, the
absence of dangling bonds on the surface of a 2D material makes solid
interface connections with metal challenging, resulting in larger contact
resistance.Allain  _et al._ (2015) Hence, most of the effort required when
engineering TMD/metal contacts is associated with finding a suitable metal to
adjust the work function. For n-FETs, a material with a smaller work function
is needed, whereas a metal with a high work function is often selected for
p-FETs. Several studies have shown that TMD/metal heterostructures such as
MoS2/Cu, WS2/Cu, and WSe2/Ti could provide n-type SB contacts,Kang  _et al._
(2014); Ouyang, Xiong, and Jing (2018) and p-type SB contacts can be formed
with a few combinations such as WSe2/Pd and WSe2/Au.Kang  _et al._ (2014);
Kong  _et al._ (2020) However, only a specific TMD/metal combination is
acceptable for Ohmic contact formation, making TMD-based FET contact design
more challenging.Zhao and Schwingenschlögl (2022)

To combine n- and p-type FETs into a complementary metal–oxide–semiconductor
FET (CMOSFET) device, horizontally combing two different TMD channels to form
a CMOSFET is a common approach.Liu  _et al._ (2014); Das, Dubey, and Roelofs
(2014); Cho, Park, and Kwon (2015); Xiong  _et al._ (2021) With regard to
fabrication, however, this CMOSFET design with two different materials has the
disadvantage that the two materials need to be transferred onto a single
substrate, with the risk of introducing defects or contamination.Ullah  _et
al._ (2021) Furthermore, two contact metals need to be considered separately,
resulting in higher processing complexity and a more time-consuming
fabrication procedure. Recently, ambipolar TMDs such as MoTe2Lin  _et al._
(2014) and ReSe2Lee  _et al._ (2019) have been proposed to compose a single-
channel material CMOSFET, which reduces the difficulty of device fabrication.

The use of a novel type of materials, namely, Janus transition metal
dichalcogenides (JTMDs), MXY, where M $=$ transition metal and X, Y $=$ S, Se,
or Te, with X $\neq$ Y, is another possible way to fabricate a CMOSFET using a
single material. In recent years, several JTMDs have been successfully
synthesized via different techniques. Monolayer MoSSe, for instance, was
synthesized utilizing chemical vapor deposition combined with plasma
strippingLu  _et al._ (2017) or a selective epitaxy atomic replacement
process,Trivedi  _et al._ (2020) while Lin _et al._ Lin  _et al._ (2020)
prepared Janus WSSe by pulsed laser deposition. Previous theoretical studies
of these JTMDs have provided unmistakable proof that their phonon spectra are
stable and devoid of imaginary frequencies, verifying their fabrication
stability.Petrić  _et al._ (2021); Chaurasiya, Gupta, and Dixit (2019)
Compared with TMDs, JTMDs have two different chalcogen-atom layers, which
create an asymmetric sandwich configuration that provides an out-of-plane
electric field. This out-of-plane field has the characteristic of a dipole,
which enables potential applications in piezoelectric, spintronics, catalysts,
and gas sensing devices.Zhang  _et al._ (2022, 2020); Kong  _et al._ (2023)
Also, simulation studies have shown that this intrinsic dipole feature of
JTMDs can enable the formation of both n- and p-type contacts simultaneously
without the need for additional doping when different sides are attached to
graphene,Chen  _et al._ (2022b); Palsgaard  _et al._ (2018); Liu  _et al._
(2020) and this can be incorporated into the design of pin-diodePalsgaard  _et
al._ (2018) or photovoltaicLiu  _et al._ (2020) devices. Moreover, theoretical
studies have shown that the SB at contact could be substantially reduced when
JTMD MoSSe are attached to metals,Zhao and Schwingenschlögl (2022) owing to
the intrinsic dipole. The above-mentioned JTMD/electrode contact systems may
even attain Ohmic contact under certain conditions.Chen  _et al._ (2022b);
Zhao and Schwingenschlögl (2022) However, unlike optoelectronic devices, the
exploitation of the advantages of n- and p-type Ohmic contacts in FET
applications has not yet been explored. Furthermore, there has not been much
work on utilizing this intrinsic dipole property to form FETs or CMOSFETs
without doping.

In this study, we propose a CMOSFET design concept utilizing the intrinsic
dipole of a JTMD. The material MoSSe is selected for investigation, since it
was the first JTMD and is the most commonly synthesized.Lu  _et al._ (2017);
Trivedi  _et al._ (2020) The remainder of this paper is organized as follows.
First, we illustrate the JTMD dipole and contact characteristics for three
heterostructures, namely, bilayer MoSSe (2L-MoSSe)/graphene and MoSSe/Al (111)
face and MoSSe/Cu (111) face structures, via first-principles density
functional theory (DFT). Then, we demonstrate the JTMD CMOSFET device design
concept with simulation results using a 2D finite-element-based
Poisson–drift–diffusion solver (2D-DDCC) developed in our laboratory.Wu

Figure 1: (a) Illustration of calculated MoSSe heterostructure system. (b),
(c), and (d) Top views of 2L-MoSSe/graphene, 1L-MoSSe/Al (111), and
1L-MoSSe/Cu (111) systems, respectively. (e), (f), and (g) Projected band
structures of MoSSe S-side contact with graphene, Al, and Cu, respectively.
(h), (i), and (j) Projected band structures of MoSSe Se-side contact with
graphene, Al, and Cu, respectively. The gray parts are the contributions of
graphene or metals.

##  II Computational details

###  II.1 Heterostructure and layered MoSSe DFT simulations

The heterostructure calculations are performed with Quantum ESPRESSO,Giannozzi
_et al._ (2009) an open-source DFT calculation software package employing the
generalized gradient approximation (GGA) of the exchange-correlation
functional and norm-conserving VanderbiltHamann, Schlüter, and Chiang (1979)
pseudopotentials combined with DFT-D3 van der Waals correction.Grimme  _et
al._ (2010) An $8\times 8\times 1$ Monkhorst–Pack $k$ sampling is used. The
geometry is refined until the maximal residual forces are less than 0.015
eV/Å, at which point MoSSe heterostructures are subjected to the dipole
correction to negate the artificial electric field caused by their asymmetric
structures. The vacuum region is set to 20 Å to prevent interactions between
nearby supercells. For MoSSe contact simulation, we attach a
$\sqrt{7}\times\sqrt{7}\times$1 supercell of graphene to a $2\times 2\times 1$
two-layer AA′ stacking supercell of MoSSe, A $2\times 2\times 1$ supercell of
six layers of (111) Al to a $\sqrt{3}\times\sqrt{3}\times$1 supercell of
MoSSe, and a $\sqrt{7}\times\sqrt{7}\times$1 supercell of six layers of (111)
Cu to a $2\times 2\times 1$ supercell of MoSSe. A simulation model is created
for each metal’s energetically advantageous surface,Skriver and Rosengaard
(1992) and four outward metal layers are fixed in the relaxation process. For
layered MoSSe, we construct an AA′A stacking of the MoSSe trilayer structure,
with the above calculation strategy also being carried out here. The only
difference is that the $k$ mesh is set to $36\times 36\times 6$ in the MoSSe
band structure calculation.

###  II.2 Device simulation

The 2D-DDCC solver is used to examine the device performance. This solver was
developed in our laboratory and has been used on many different kinds of
devices. More detailed information can be found on our website.Wu  Both the
Poisson and drift–diffusion equations are solved self-consistently by the
solver. The equations are as follows:

$\displaystyle\nabla_{r}\cdot[\epsilon\nabla_{r}V(r)]=q(N_{A}^{-}-N_{D}^{+}+n-p),$
(1) $\displaystyle J_{n}=-q\mu_{n}n(r)\nabla_{r}V(r)+qD_{n}\nabla_{r}n(r),$
(2) $\displaystyle J_{p}=-q\mu_{p}p(r)\nabla_{r}V(r)-qD_{p}\nabla_{r}p(r),$
(3)

where $\epsilon$ is the dielectric constant, $V$ is the electric potential,
$N^{-}_{A}$ is the acceptor’s doping concentration, $N^{+}_{D}$ is the donor’s
doping concentration, $n$ is the electron concentration, $p$ is the hole
concentration, $J_{n}$ and $J_{p}$ are the current densities of electrons and
holes, respectively, and $D_{n}$ and $D_{p}$ are the electron and hole
diffusion coefficients, respectively. This solver serves as the basis for all
of the device simulations discussed in this article, with the exception of the
MoSSe channel region calculations.

##  III Results and discussion

To study the interactions between MoSSe and contacts, we construct the S and
Se sides of the MoSSe heterostructure with graphene and two common electrode
metals (namely, Al and Cu). Figure 1(a) shows the calculated heterostructure
systems that can form low SB or Ohmic contacts. The top views are shown in
Figs. 1(b) and 1(c), where the lattice mismatches are 1.11%, 0.44%, and 2.97%
for the 2L-MoSSe/graphene, MoSSe/Al, and MoSSe/Cu systems, respectively.
Figures 1(e)–1(j) show the projected band structures, and Figs. 1(e)–1(g)
representing the S-face contact of MoSSe with the electrode material, and
Figs. 1(h)–1(j) representing the Se-face attachment. As can be observed, if
graphene contacts a two-layer MoSSe material on the Se side [Fig. 1(e)], then
the Fermi level of the entire system is 0.1 eV lower than the observable
conduction band of the MoSSe material, suggesting an n-type Schottky barrier.
On the other hand, if graphene contacts a two-layer MoSSe material on the S
side [Fig. 1(h)], then we see a p-type Ohmic contact. These results are
consistent with the results of previous JTMD contact studies.Palsgaard  _et
al._ (2018); Chen  _et al._ (2022b) According to our DFT calculation,
monolayer MoSSe has a strong $2.1\times 10^{6}$ V/cm intrinsic dipole field in
the out-of-plane direction, which may be the reason why this single material
exhibits n- and p-type contact formation when the different sides of the MoSSe
attach to graphene. Also, the Dirac cone of the graphene in the band structure
retains its integrity, indicating the presence of physisorption
characteristics between the two material systems.Gong  _et al._ (2010)

We are also interested in metal electrode cases such as Al and Cu. For Al
(111) face attachment to the S and Se faces of monolayer MoSSe [Figs. 1(f) and
1(i)], the MoSSe band intersects the Fermi level, indicating that Ohmic
contact occurs between the metal and MoSSe states. The intersection can extend
to the MoSSe conduction band when the S face attaches to Al, behaving as an
n-type contact; p-type contact can be observed in the case of Se-face
attachment, since the crossover point is mainly attributable to the valence
band. Similar n- and p-type Ohmic contact formation results for different face
attachments are also seen in the case of Cu (111). In contrast to the
2L-MoSSe/graphene case, these metal contact projected band structures
demonstrate the chemisorption characteristics due to significant hybridization
in the band diagram. This chemisorption-like property imparts an the advantage
with regard to tunneling barrier reduction at the contact region, thus
improving device performance.Gong  _et al._ (2010); Zhao and Schwingenschlögl
(2022)

Figure 2: (a) Projected band structure of AA′A-stacked MoSSe. The yellow,
blue, and red dots indicate the top, middle, and bottom layers of MoSSe,
respectively. (b) AA′A-stacked MoSSe projected band with layer separation in
side view. Each layer offsets the other in the projected band diagrams of
separate MoSSe layers, creating a spontaneous polarization (black arrow)
through three layers.

DFT calculations are also used to obtain the device simulation parameters and
the electronic parameters MoSSe, such as bandgap, affinity, dielectric
constant, and effective mass. Figure 2(a) shows the band diagram of the AA′A
MoSSe stacking with a relaxed lattice constant of 3.25 Å and an interlayer
distance of 3.19 Å. All three layers exhibit an indirect bandgap with a $K$
valley in the conduction bands and a $\Gamma$ valley in the valence bands. The
projected band structure of each MoSSe layer can be observed in Fig. 2(b). The
yellow, blue, and red dots represents the projections of the top, middle, and
bottom MoSSe layers, respectively, and are correlated with the colored arrows
in Fig. 2(a). As the layers’ band components were separated, we observed that
the bands were shifted by 0.43 eV as each MoSSe layer was added. This effect
originates from the intrinsic dipole in the out-of-plane direction of MoSSe.
Similar to the case of gallium nitride, we can represent this dipole effect by
a spontaneous polarization $P_{\mathrm{sp}}$ term in the device simulations,
which is illustrated by the two diagonal black arrows in Fig. 2(b). The
$P_{\mathrm{sp}}$ term for MoSSe can be written as the product of the out-of-
plane dielectric constant $\epsilon_{\bot}$ and the intrinsic electric field
$E$:

$P_{\mathrm{sp}}=\epsilon_{\bot}E.$ (4)

It is easy to obtain $E$ from the thickness of the MoSSe and the value of the
out-of-plane dipole. However, a problem arises when attempting to obtain
$\epsilon_{\bot}$ in DFT calculations, because the vacuum layer along the $z$
axis will also be included. The principle of the equivalent capacitance
methodLaturia, de Put, and Vandenberghe (2018) is adopted here to separate the
dielectric constants of MoSSe from those of the whole system. Carrier mobility
is obtained using the Monte Carlo procedure described by Pai and Wu.Pai and Wu
(2022) We design our device using MoSSe of 5 nm channel thickness to represent
MoSSe multilayers, since this thickness ensures there is sufficient charge
flowing in the channel region. All the parameters used in the 2D-DDCC
simulation are given in Table 1. In our device simulation setting, the metal
work function is set at the mid-gap of the MoSSe to showcase the possibility
of n- and p-type contact formation even in the worst case metal selection
scenario.

Table 1: . Input parameters used in the JTMD FET simulations. The parameters in the upper part of the table are MoSSe-related constants, and those in the lower part are parameters of the oxides. Parameter | Numerical value | Description111MoSSe parameters are all obtained by DFT. SiO2 and Al2O3 parameters are given inherently in 2D-DDCC.Wu
---|---|---
$E_{g}$ | 1.212 eV | Average bandgap
$E_{ea}$ | 3.97 eV | Electron affinity
$\mu_{e}$ | 667 cm2/(V$\cdot$s) | Electron mobility
$\mu_{h}$ | 624 cm2/(V$\cdot$s) | Hole mobility
$m^{*}_{e,K}$ | $0.37m_{0}$ | Electron $K$-valley effective mass
$m^{*}_{e,Q}$ | $0.47m_{0}$ | Electron $Q$-valley effective mass
$m^{*}_{h,\Gamma}$ | $0.75m_{0}$ | Hole $\Gamma$-valley effective mass
$m^{*}_{h,K}$ | $0.45m_{0}$ | Hole $K$-valley effective mass
$\epsilon_{||}$ | $15.93\epsilon_{0}$ | In-plane dielectric constant
$\epsilon_{\bot}$ | $6.31\epsilon_{0}$ | Out-of-plane dielectric constant
$P_{\mathrm{sp}}$ |  $4.76\times 10^{13}$ cm-2 | Spontaneous polarization
$\epsilon_{\mathrm{SiO_{2}}}$ | $3.9\epsilon_{0}$ | SiO2 dielectric constant
$\epsilon_{\mathrm{Al_{2}O_{3}}}$ | $9.3\epsilon_{0}$ | Al2O3 dielectric constant
$E_{g,\mathrm{HfO}_{2}}$ | 5.8 eV | HfO2 bandgapWu, Alivov, and Negi (2008)
$\epsilon_{\mathrm{HfO_{2}}}$ | $25\epsilon_{0}$ | HfO2 dielectric constantKumar, Kaushik, and Negi (2014)

Figure 3: (a) Schematic of 2D MoSSe JTMD CMOSFET device. (b) and (c) Energy
band and carrier concentration sectional view diagrams of p-MOSFET and
n-MOSFET, respectively, from source metal to gate oxide. (d) n-MOSFET
sectional view conduction band diagram and electron density with different
$P_{\mathrm{sp}}$ values for comparison. Figure 4: (a) and (b) Schematics of
single- and double-gate MoSSe MOSFET designs, respectively, with
$L_{g}=1~{}\mu$m, showing the structure and critical device dimensions. (c)
Schematic of double-gate MoSSe MOSFET design with $L_{g}=50$ nm. (d)
$I_{ds}$–$V_{g}$ plots for the designs shown in (a)–(c) with a
$t_{\mathrm{ox}}=1$ nm HfO2 gate oxide for both polarities of the MOSFET.
Parameters are given in Table 1.

Figure 3(a) is a schematic of double-gate CMOSFET device design exploiting the
strong $P_{\mathrm{sp}}$ properties of MoSSe. As illustrated to the left of
the black dashed line, to create a p-MOSFET, two metal electrodes are attached
to the Se face to produce p-type source and drain regions, while the remaining
MoSSe region between them acts like a channel area after the oxide and gate
metal have been placed in the vertical direction. An n-MOSFET can be created
similarly, as illustrated to the right of the dashed line, with the only
difference being that in this case it is the S face, rather than the Se face,
to which metals are attached to form n-type regions at source and drain. This
horizontal CMOSFET design has two advantages: (1) the Janus MoSSe reduces the
complexity of material selection, since only one channel material is used to
form n- and p-MOSFETs; (2) this structure enables layer-by-layer build-up
during the fabrication process, which makes the process more reliable.

To investigate the band and carrier characteristics of the designed MoSSe
MOSFETs, the 2D-DDCC solver is used out to simulate device characteristics.
Cross-sectional views of the band diagram are shown in Figs. 3(b) and 3(c) for
the p- and n-MOSFETs, respectively. The strong $P_{\mathrm{sp}}$ in the MoSSe
vertical direction acts like partial positive and negative charges attracting
electrons and holes, causing the valence band to be pulled upward [Fig. 3(b)]
and the conduction band to be bent downward [Fig. 3(c)]. Thus, the majority
carrier concentration (dashed curve) surges near the interface with the metal
for contacts of both polarities. Both electron and hole carrier concentrations
can reach to near the order of $10^{20}$ cm-3 when layered MoSSe serves as the
channel layer. As with doping, the high carrier concentration of the
metal–SeMoS contacts means that these can be regarded as p-type Ohmic
contacts. The metal–SMoSe contacts can be regarded as n-type Ohmic contacts,
which is in good agreement with the aforementioned DFT calculations. The
difference in physical behavior between the different sides of MoSSe
attachment corresponds simply to the difference in sign of $P_{\mathrm{sp}}$
here. It is also worth considering how the value of $P_{\mathrm{sp}}$ affects
this phenomenon. Figure 3(d), taking an n-type MOSFET as an example, shows the
effects of different values of $P_{\mathrm{sp}}$ on the band structure and
carrier density at the interface. This can provide a reference for future
studies of materials with different JTMD dipole strengths. As the dipole field
of the material becomes stronger, a larger $P_{\mathrm{sp}}$ is generated,
leading to more pronounced JTMD-induced band bending. Consequently, the
accumulation of carriers becomes more prominent. Conversely, smaller
$P_{\mathrm{sp}}$ values result in fewer carriers gathering at the interface.
If the goal in device design is to increase carrier concentration at the
contact, then selecting materials with higher $P_{\mathrm{sp}}$ values will be
appropriate.

The $I$–$V$ characteristics of several MoSSe MOSFET designs are now
investigated. Figure 4(a) shows a single-gate design with gate length
$L_{g}=1~{}\mu$m, which is a common dimension adopted in experimental studies
of FETs based on 2D materialsXiong  _et al._ (2021); 2D_FET_Bench. Device
modifications such as the use of double gates [Fig. 4(b)] and smaller
dimension [Fig. 4(c)] are also simulated. The simulations for all devices
assume an HfO2 gate oxide of thickness $t_{\mathrm{ox}}=1$ nm. Figure 4(d)
shows the transfer characteristics ($I_{ds}$–$V_{g}$) of the n- and p-MOSFETs
from Figs. 4(a)–4(c). The curves with squares, circles, and diamonds
correspond to the n-MOSFETs in (a), (b), and (c), respectively, and those with
triangles, stars, and pentagons to the p-MOSFETs in (a), (b), and (c),
respectively. The key distinction lies in the opposite orientation of
$P_{\mathrm{sp}}$ in the JTMD layer. The typical $I_{ds}$–$V_{g}$ curves for
the n- and p-MOSFETs indicate that all three devices exhibit satisfactory
enhancement-mode MOSFET behavior. Among the three devices, the single-gate
design [Figure 4(a)] has the most significant sub-threshold swing (SS) and the
lowest on-current $I_{\mathrm{on}}$. When a bottom gate is added [Figure
4(b)], the SS drops to around 60.5 mV/dec, while $I_{\mathrm{on}}$ increases
by an order of magnitude, indicating an improvement in gate control. Having
shown that both designs are feasible at a larger scale, we reduce the
horizontal scale of the double-gate design to $L_{g}=50$ nm [Figure 4(c)] to
examine the device performance at a smaller scale. The consequent increases in
SS from 60.45 to 60.53 for the n-MOSFET and from 60.55 to 60.71 for the
p-MOSFET demonstrate that reducing $L_{g}$ weakens the control of the gate. In
addition, $I_{\mathrm{on}}$ is increased by an order of magnitude compared
with that of the larger-scale device, while the off-state current
$I_{\mathrm{off}}$ is decreased by about an order, indicating that the loss of
SS is compensated by an improvement in $I_{\mathrm{on}}/I_{\mathrm{off}}$. All
of these results show that the SS and on/off-current characteristics of the
designs considered are those of a typical MOSFET device. The lower SS and
higher $I_{\mathrm{on}}$ consistently observed for all the n-MOSFETs compared
with the corresponding p-MOSFETs may originate from the slightly higher
mobility of electrons compared with holes.

Figure 5: (a) Schematic of double-gate MoSSe MOSFET with $L_{g}=50$ nm and a
variety of oxides with different values of EOT. (b) and (c) $I_{ds}$–$V_{g}$
plots for n- and p-MOSFETs, respectively, with different values of EOT.
$t_{\mathrm{ox}}=1$ nm for all oxides, and, in addition, results for designs
with $t_{\mathrm{ox}}=2$ and 10 nm are also shown for HfO2. The parameters are
given in Table 1. Figure 6: Simulated majority carrier concentration plots for
double-gate HfO2 p- and n-MOSFET devices under $V_{g}=-0.5$ V and 0.5 V bias
operation. The $V_{ds}$ bias is set to zero for observational convenience.

For successful operation of a CMOSFET, an enhancement-mode MOSFET device is
essential. Since the gate oxide thickness $t_{\mathrm{ox}}$ plays a crucial
role in determining the operation mode of a MOSFET, a variety of oxides and
different values of $t_{\mathrm{ox}}$, are tested to determine the optimal
design of the device in Fig. 5(a) in terms of the equivalent oxide thickness
(EOT), and the results are shown in Figs. 5(b) and 5(c) for the n- and
p-MOSFETs, respectively. Here, we define on/off state switching when
$I_{\mathrm{on}}/I_{\mathrm{off}}>10^{3}$. First, a SiO2 oxide gate of
thickness 1 nm is tested, and the $I_{d}$–$V_{g}$ result is shown by the curve
with red circles. The device is clearly in the on-state ($I_{d}\sim 100$ A/cm)
when no gate bias is applied, indicating that the MOSFET is a depletion-mode
device. To search for the threshold EOT at which the device switches to the
enhancement mode, we try decreasing the EOT by selecting an Al2O3 gate with
$t_{\mathrm{ox}}1$ nm (EOT $=$ 0.42 nm, blue triangles). However, the current
remains of the order of $10^{1}$ A/cm at $V_{g}=0$ V, and both polarities
remain in the depletion mode. Therefore, a high-$\kappa$ material, namely,
HfO2, is selected to help decrease the EOT of the MOSFETs. Using HfO2 with
$t_{\mathrm{ox}}=1$ nm (EOT $=$ 0.16 nm, green stars) gives a superior
enhancement-mode device for both n- and p-MOSFETs. Finally, we optimize the
$t_{\mathrm{ox}}$ of the HfO2 to 2 nm (EOT $=$ 0.31 nm, purple diamonds) to
prevent current leakage through the thin gate oxide. As can be seen from Figs.
5(b) and 5(c), both of the $I_{d}$–$V_{g}$ curves for this optimized value of
$t_{\mathrm{ox}}$ intersect the off-state brown dashed line at $V_{g}=0$ V,
indicating preservation of the enhancement mode even though the EOT has
increased. These results for HfO2 with $t_{\mathrm{ox}}=2$ nm suggest that the
EOT threshold between the depletion and enhancement modes in this MOSFET
design is 0.312 nm. For this optimal MOSFET design based on HfO2 with
$t_{\mathrm{ox}}=2$ nm, the SS as extracted from Figs. 5(b) and 5(c) is about
64 mV/dec. Since such low-$t_{\mathrm{ox}}$ devices may encounter several
processing difficulties, simulation results for a more easily fabricated thick
HfO2 gate with $t_{\mathrm{ox}}=10$ nm (EOT $=$ 1.6 nm, black squares) are
also given in Figs. 5(b) and 5(c). Owing to the increased EOT, both the n- and
p-type MOSFETs exhibit the depletion mode in terms of transfer
characteristics, where both the $I_{\mathrm{on}}$ drops and the device turns
off at $-$1.2 V and 1.4 V for n- and p-MOSFETs, respectively. All in all, the
$I$–$V$ plots demonstrate the possibility of creating a workable MOSFET even
with larger values of $t_{\mathrm{ox}}$ and provide a reference for future
experimental studies.

To delve further into the on/off characteristics of the MOSFET devices, we
plot the majority carrier concentration of the optimal p- and n-MOSFET design
(HfO2 gate with $t_{\mathrm{ox}}=2$ nm) in Fig. 6. The source and drain areas
are defined as those where the MoSSe is attached to the metal, which gives a
relatively high carrier concentration of about $10^{14}$ cm-3 (green color).
The carrier concentration gradually decays as the position moves away from the
contact region. Owing to the 10 nm gate metal isolation design, two bump-like
regions of high carrier concentration appear next to the source and drain
areas. The thicker oxide eliminates the dipole effect of MoSSe, and thus
carrier gas remains. Taking the p-MOSFET (top two plots) as an example, we can
see that in with the application of a negative $-$0.5 V gate bias, holes are
attracted into the channel region, forming a strong inversion channel layer
and causing the device to turn on. By contrast, when a positive 0.5 V gate
bias is applied, holes are depleted, and the inversion layer disappears,
indicating an off-state device. A similar result is also obtained for the
n-MOSFET, with the only difference being that a strong inversion layer appears
when $V_{g}=0.5$ V and the inversion layer vanishes when a $V_{g}=-0.5$ V bias
is applied.

##  IV Conclusions

We have introduced a novel CMOSFET design based on the intrinsic dipole
properties of JTMD, with a specific focus on the material MoSSe. Our
investigation has encompassed diverse heterostructures, including bilayer
MoSSe/graphene and MoSSe/Al (111) face and MoSSe/Cu (111) face structures,
which we have analyzed using first-principles DFT. We have also examined the
interactions between MoSSe, graphene, and Al and Cu electrodes, identifying
characteristics such as Schottky barriers and Ohmic contacts. Our design
leverages the strong dipole field of MoSSe to create n- and p-type contact
regions, thereby forming a CMOSFET with simplified material selection. The
proposed horizontal CMOSFET architecture offers advantages in fabrication
reliability and integration. Device simulations have provided insights into
the spontaneous polarization caused by band bending and the related carrier
distribution, with an optimized EOT $=$ 0.312 nm giving a favorable threshold
between the depletion and enhancement modes of the MOSFET device. Majority
carrier plots show the capability of the device to control carriers in the
MoSSe channel region, demonstrating its potential for low-voltage logic
elements in integrated circuits. On the basis of the surface polarization
strategy adopted here, it might be possible to form a MOSFET from any JTMD,
provided that the 2D material possesses a strong intrinsic out-of-plane
dipole. Regardless, this work has introduced a promising approach to CMOSFET
design, offering valuable insights into materials, interfaces, and device
characteristics for future electronic applications.

###### Acknowledgements.

This work was supported by the National Science and Technology Council under
Grant Nos. NSTC 112-2221-E-002-215-MY3, NSTC 112-2923-E-002-002, and
12-2221-E-002-214-MY3.

## AUTHOR DECLARATIONS

### Conflict of Interest

The authors have no conflicts to disclose

### Author Contributions

Yun-Pin Chiu: Conceptualization (equal); Data curation (lead);Formal analysis
(equal); Methodology (equal); Validation (lead); Visualization(lead); Writing
– original draft (lead). Hsin-Wen Huang: Conceptualization (equal); Data
curation (support); Formal analysis (equal); Methodology (equal); Validation
(support). Yuh-Renn Wu: Conceptualization (equal); Formal analysis (equal);
Funding acquisition (lead); Project administration (lead); Software(lead);
Supervision (lead); Writing – review and editing (lead).

## Data Availability

The data that support the findings of this study are available from the
corresponding author upon reasonable request.

## References

## References

  * Chhowalla, Jena, and Zhang (2016a) M. Chhowalla, D. Jena, and H. Zhang, “Two-dimensional semiconductors for transistors,” Nat. Rev. Mater. 1, 16052 (2016a).
  * Chen  _et al._ (2022a) F.-X. R. Chen, N. Kawakami, C.-T. Lee, P.-Y. Shih, Z.-C. Wu, Y.-C. Yang, H.-W. Tu, W.-B. Jian, C. Hu, and C.-L. Lin, “Visualizing correlation between carrier mobility and defect density in MoS2 FET,” Appl. Phys. Lett. 121, 151601 (2022a).
  * Radisavljevic  _et al._ (2011) B. Radisavljevic, A. Radenovic, J. Brivio, V. Giacometti, and A. Kis, “Single-layer MoS2 transistors,” Nat. Nanotechnol. 6, 147–150 (2011).
  * Ye, Zhang, and Yap (2017) M. Ye, D. Zhang, and Y. K. Yap, “Recent advances in electronic and optoelectronic devices based on two-dimensional transition metal dichalcogenides,” Electronics 6, 43 (2017).
  * Chaves  _et al._ (2020) A. Chaves, J. G. Azadani, H. Alsalman, D. R. da Costa, R. Frisenda, A. J. Chaves, S. H. Song, Y. D. Kim, D. He, J. Zhou, A. Castellanos-Gomez, F. M. Peeters, Z. Liu, C. L. Hinkle, S.-H. Oh, P. D. Ye, S. J. Koester, Y. H. Lee, P. Avouris, X. Wang, and T. Low, “Bandgap engineering of two-dimensional semiconductor materials,” npj 2D Mater. Appl. 4, 29 (2020).
  * Serna  _et al._ (2016) M. I. Serna, S. H. Yoo, S. Moreno, Y. Xi, J. P. Oviedo, H. Choi, H. N. Alshareef, M. J. Kim, M. Minary-Jolandan, and M. A. Quevedo-Lopez, “Large-area deposition of MoS2 by pulsed laser deposition with _in situ_ thickness control,” ACS Nano 10, 6054–6061 (2016).
  * Zhang  _et al._ (2013) Y. Zhang, Y. Zhang, Q. Ji, J. Ju, H. Yuan, J. Shi, T. Gao, D. Ma, M. Liu, Y. Chen, _et al._ , “Controlled growth of high-quality monolayer WS2 layers on sapphire and imaging its grain boundary,” ACS Nano 7, 8963–8971 (2013).
  * Orofeo  _et al._ (2014) C. M. Orofeo, S. Suzuki, Y. Sekine, and H. Hibino, “Scalable synthesis of layer-controlled WS2 and MoS2 sheets by sulfurization of thin metal films,” Appl. Phys. Lett. 105, 083112 (2014).
  * Liu  _et al._ (2021) Y. Liu, X. Duan, H.-J. Shin, S. Park, Y. Huang, and X. Duan, “Promises and prospects of two-dimensional transistors,” Nature 591, 43–53 (2021).
  * Chhowalla, Jena, and Zhang (2016b) M. Chhowalla, D. Jena, and H. Zhang, “Two-dimensional semiconductors for transistors,” Nat. Rev. Mater. 1, 16052 (2016b).
  * Pezeshki  _et al._ (2016) A. Pezeshki, H. Shokouh, S. Hossein, P. J. Jeon, I. Shackery, J. S. Kim, I.-K. Oh, S. C. Jun, H. Kim, and S. Im, “Static and dynamic performance of complementary inverters based on nanosheet $\alpha$-MoTe2 p-channel and MoS2 n-channel transistors,” ACS Nano 10, 1118–1125 (2016).
  * Ovchinnikov  _et al._ (2014) D. Ovchinnikov, A. Allain, Y.-S. Huang, D. Dumcenco, and A. Kis, “Electrical transport properties of single-layer WS2,” ACS Nano 10, 8174–8181 (2014).
  * Cho, Park, and Kwon (2015) A.-J. Cho, K. C. Park, and J.-Y. Kwon, “A high-performance complementary inverter based on transition metal dichalcogenide field-effect transistors,” Nanoscale Res. Lett. 10, 115 (2015).
  * Zhang  _et al._ (2021) L. Zhang, Y. Zhang, X. Sun, K. Jia, Q. Zhang, Z. Wu, and H. Yin, “High-performance multilayer WSe2 p-type field effect transistors with Pd contacts for circuit applications,” J. Mater. Sci.: Mater. Electron. 32, 17427–17435 (2021).
  * Sze, Li, and Ng (2021) S. M. Sze, Y. Li, and K. K. Ng,  _Physics of Semiconductor Devices_ (Wiley, 2021).
  * Allain  _et al._ (2015) A. Allain, J. Kang, K. Banerjee, and A. Kis, “Electrical contacts to two-dimensional semiconductors,” Nat. Mater. 14, 1195–1205 (2015).
  * Zheng  _et al._ (2021) Y. Zheng, J. Gao, C. Han, and W. Chen, “Ohmic contact engineering for two-dimensional materials,” Cell Rep. Phys. Sci. 2, 100298 (2021).
  * Kang  _et al._ (2014) J. Kang, W. Liu, D. Sarkar, D. Jena, and K. Banerjee, “Computational study of metal contacts to monolayer transition-metal dichalcogenide semiconductors,” Phys. Rev. X 4, 031005 (2014).
  * Ouyang, Xiong, and Jing (2018) B. Ouyang, S. Xiong, and Y. Jing, “Tunable phase stability and contact resistance of monolayer transition metal dichalcogenides contacts with metal,” npj 2D Mater. Appl. 2, 13 (2018).
  * Tang  _et al._ (2019) H. Tang, B. Shi, Y. Pan, J. Li, X. Zhang, J. Yan, S. Liu, J. Yang, L. Xu, J. Yang, M. Wu, and J. Lu, “Schottky contact in monolayer WS2 field-effect transistors,” Adv. Theory Simul. 2, 1900001 (2019).
  * Tung (2014) R. T. Tung, “The physics and chemistry of the Schottky barrier height,” Appl. Phys. Rev. 1 (2014).
  * Younas, Zhou, and Hinkle (2023) R. Younas, G. Zhou, and C. L. Hinkle, “A perspective on the doping of transition metal dichalcogenides for ultra-scaled transistors: Challenges and opportunities,” Appl. Phys. Lett. 122 (2023).
  * Kong  _et al._ (2020) L. Kong, X. Zhang, Q. Tao, M. Zhang, W. Dang, Z. Li, L. Feng, L. Liao, X. Duan, and Y. Liu, “Doping-free complementary WSe2 circuit via van der Waals metal integration,” Nat. Commun. 11, 1866 (2020).
  * Zhao and Schwingenschlögl (2022) N. Zhao and U. Schwingenschlögl, “Dipole-induced Ohmic contacts between monolayer Janus MoSSe and bulk metals,” npj 2D Mater. Appl. 131, 72 (2022).
  * Liu  _et al._ (2014) H. Liu, A. T. Neal, Z. Zhu, Z. Luo, X. Xu, D. Tománek, and P. D. Ye, “Phosphorene: An unexplored 2D semiconductor with a high hole mobility,” ACS Nano 8, 4033–4041 (2014).
  * Das, Dubey, and Roelofs (2014) S. Das, M. Dubey, and A. Roelofs, “High gain, low noise, fully complementary logic inverter based on bi-layer WSe2 field effect transistors,” Appl. Phys. Lett. 105 (2014).
  * Xiong  _et al._ (2021) X. Xiong, A. Tong, X. Wang, S. Liu, X. Li, R. Huang, and Y. Wu, “Demonstration of vertically-stacked CVD monolayer channels: MoS2 nanosheets GAA-FET with $I_{\mathrm{on}}>700$ µA/µm and MoS2/WSe2 CFET,” in  _2021 IEEE International Electron Devices Meeting (IEDM)_ (2021) pp. 7.5.1–7.5.4.
  * Ullah  _et al._ (2021) S. Ullah, X. Yang, H. Q. Ta, M. Hasan, A. Bachmatiuk, K. Tokarska, B. Trzebicka, L. Fu, and M. H. Rummeli, “Graphene transfer methods: A review,” Nano Res. 14, 3756–3772 (2021).
  * Lin  _et al._ (2014) Y.-F. Lin, Y. Xu, S.-T. Wang, S.-L. Li, M. Yamamoto, A. Aparecido-Ferreira, W. Li, H. Sun, S. Nakaharai, W.-B. Jian, K. Ueno, and K. Tsukagoshi, “Ambipolar MoTe2 transistors and their applications in logic circuits,” Adv. Mater. 26, 3263–3269 (2014).
  * Lee  _et al._ (2019) K.-C. Lee, S.-H. Yang, Y.-S. Sung, Y.-M. Chang, C.-Y. Lin, F.-S. Yang, M. Li, K. Watanabe, T. Taniguchi, C.-H. Ho, C.-H. Lien, and Y.-F. Lin, “Analog circuit applications based on all-2D ambipolar ReSe2 field-effect transistors,” Adv. Funct. Mater. 29, 1809011 (2019).
  * Lu  _et al._ (2017) A.-Y. Lu, H. Zhu, J. Xiao, C.-P. Chuu, Y. Han, M.-H. Chiu, C.-C. Cheng, C.-W. Yang, K.-H. Wei, Y. Yang, Y. Wang, D. Sokaras, D. Nordlund, P. Yang, D. A. Muller, M.-Y. Chou, X. Zhang, and L.-J. Li, “Janus monolayers of transition metal dichalcogenides,” Nat. Nanotechnol. 12, 744–749 (2017).
  * Trivedi  _et al._ (2020) D. B. Trivedi, G. Turgut, Y. Qin, M. Y. Sayyad, D. Hajra, M. Howell, L. Liu, S. Yang, N. H. Patoary, H. Li, M. M. Petrić, M. Meyer, M. Kremser, M. Barbone, G. Soavi, A. V. Stier, K. Müller, S. Yang, I. S. Esqueda, H. Zhuang, J. J. Finley, and S. Tongay, “Room-temperature synthesis of 2D Janus crystals and their heterostructures,” Adv. Mater. 32, 2006320 (2020).
  * Lin  _et al._ (2020) Y.-C. Lin, C. Liu, Y. Yu, E. Zarkadoula, M. Yoon, A. A. Puretzky, L. Liang, X. Kong, Y. Gu, A. Strasser, H. M. I. Meyer, M. Lorenz, M. F. Chisholm, I. N. Ivanov, C. M. Rouleau, G. Duscher, K. Xiao, and D. B. Geohegan, “Low energy implantation into transition-metal dichalcogenide monolayers to form Janus structures,” ACS Nano 14, 3896–3906 (2020).
  * Petrić  _et al._ (2021) M. Petrić, M. Kremser, M. Barbone, Y. Qin, Y. Sayyad, Y. Shen, S. Tongay, J. Finley, A. Botello-Méndez, and K. Müller, “Raman spectrum of Janus transition metal dichalcogenide monolayers WSSe and MoSSe,” Phys. Rev. B 103, 035414 (2021).
  * Chaurasiya, Gupta, and Dixit (2019) R. Chaurasiya, G. Gupta, and A. Dixit, “Ultrathin Janus WSSe buffer layer for W(S/Se)2 absorber based solar cells: A hybrid, DFT and macroscopic, simulation studies,” Sol. Energy Mater. Sol. Cells 201, 110076 (2019).
  * Zhang  _et al._ (2022) L. Zhang, Y. Xia, X. Li, L. Li, X. Fu, J. Cheng, and R. Pan, “Janus two-dimensional transition metal dichalcogenides,” J. Appl. Phys. 131, 230902 (2022).
  * Zhang  _et al._ (2020) L. Zhang, Z. Yang, T. Gong, R. Pan, H. Wang, Z. Guo, H. Zhang, and X. Fu, “Recent advances in emerging Janus two-dimensional materials: From fundamental physics to device applications,” J. Mater. Chem. A 8, 8813–8830 (2020).
  * Kong  _et al._ (2023) D. Kong, F. Tian, Y. Xu, S. Zhu, Z. Yu, L. Xiong, P. Li, H. Wei, X. Zheng, and M. Peng, “Polarity reversal and strain modulation of Janus MoSSe/GaN polar semiconductor heterostructures,” Phys. Chem. Chem. Phys. 25, 30361–30372 (2023).
  * Chen  _et al._ (2022b) Y.-Q. Chen, H.-H. Zhang, B. Wen, X.-B. Li, X.-L. Wei, W.-J. Yin, L.-M. Liu, and G. Teobaldi, “The role of permanent and induced electrostatic dipole moments for Schottky barriers in Janus MXY/graphene heterostructures: A first-principles study,” Dalton Trans. 51, 9905–9914 (2022b).
  * Palsgaard  _et al._ (2018) M. Palsgaard, T. Gunst, T. Markussen, K. S. Thygesen, and M. Brandbyge, “Stacked Janus device concepts: Abrupt pn-junctions and cross-plane channels,” Nano Lett. 18, 7275–7281 (2018).
  * Liu  _et al._ (2020) X. Liu, P. Gao, W. Hu, and J. Yang, “Photogenerated-carrier separation and transfer in two-dimensional Janus transition metal dichalcogenides and graphene van der Waals sandwich heterojunction photovoltaic cells,” J. Phys. Chem. Lett. 11, 4070–4079 (2020).
  * (42) Y. R. Wu, “Optoelectronic Device Simulation Laboratory,” http://yrwu-wk.ee.ntu.edu.tw/, see the DDCC 2D section for more information.
  * Giannozzi  _et al._ (2009) P. Giannozzi, S. Baroni, N. Bonini, M. Calandra, R. Car, C. Cavazzoni, D. Ceresoli, G. L. Chiarotti, M. Cococcioni, I. Dabo, A. D. Corso, S. de Gironcoli, S. Fabris, G. Fratesi, R. Gebauer, U. Gerstmann, C. Gougoussis, A. Kokalj, M. Lazzeri, L. Martin-Samos, N. Marzari, F. Mauri, R. Mazzarello, S. Paolini, A. Pasquarello, L. Paulatto, C. Sbraccia, S. Scandolo, G. Sclauzero, A. P. Seitsonen, A. Smogunov, P. Umari, and R. M. Wentzcovitch, “QUANTUM ESPRESSO: A modular and open-source software project for quantum simulations of materials,” J. Phys.: Condens. Matter 21, 395502 (2009).
  * Hamann, Schlüter, and Chiang (1979) D. R. Hamann, M. Schlüter, and C. Chiang, “Norm-conserving pseudopotentials,” Phys. Rev. Lett. 43, 1494–1497 (1979).
  * Grimme  _et al._ (2010) S. Grimme, J. Antony, S. Ehrlich, and H. Krieg, “A consistent and accurate _ab initio_ parametrization of density functional dispersion correction (DFT-D) for the 94 elements H–Pu,” J. Chem. Phys. 132, 154104 (2010).
  * Skriver and Rosengaard (1992) H. L. Skriver and N. M. Rosengaard, “Surface energy and work function of elemental metals,” Phys. Rev. B 46, 7157 (1992).
  * Gong  _et al._ (2010) C. Gong, G. Lee, B. Shan, E. M. Vogel, R. M. Wallace, and K. Cho, “First-principles study of metal–graphene interfaces,” J. Appl. Phys. 108, 123711 (2010).
  * Laturia, de Put, and Vandenberghe (2018) A. Laturia, M. L. V. de Put, and W. G. Vandenberghe, “Dielectric properties of hexagonal boron nitride and transition metal dichalcogenides: From monolayer to bulk,” npj 2D Mater. Appl. 2, 6 (2018).
  * Pai and Wu (2022) H.-C. Pai and Y.-R. Wu, “Investigating the high field transport properties of Janus WSSe and MoSSe by DFT analysis and Monte Carlo simulations,” J. Appl. Phys. 131, 144303 (2022).
  * Wu, Alivov, and Negi (2008) M. Wu, Y. I. Alivov, and H. Negi, Morkoç, “High-$\kappa$ dielectrics and advanced channel concepts for Si MOSFET,” J. Mater. Sci.: Mater. Electron. 19, 915–951 (2008).
  * Kumar, Kaushik, and Negi (2014) B. Kumar, B. K. Kaushik, and Y. S. Negi, “Perspectives and challenges for organic thin film transistors: Materials, devices, processes and applications,” J. Mater. Sci.: Mater. Electron. 25, 1–30 (2014).


# Using the Open Meta Kaggle Dataset to Evaluate Tripartite Recommendations in
Data Markets

Dominik Kowald  Know-Center GmbHGraz, Austria<EMAIL_ADDRESS>,
Matthias Traub  Know-Center GmbHGraz, Austria<EMAIL_ADDRESS>,  Dieter
Theiler  Know-Center GmbHGraz, Austria<EMAIL_ADDRESS>,  Heimo Gursch
Know-Center GmbHGraz, Austria<EMAIL_ADDRESS>,  Emanuel Lacic  Know-
Center GmbHGraz, Austria<EMAIL_ADDRESS>,  Stefanie Lindstaedt  Know-
Center GmbHGraz, Austria<EMAIL_ADDRESS>,  Roman Kern  Graz University
of TechnologyGraz, Austria<EMAIL_ADDRESS>and  Elisabeth Lex  Graz University
of TechnologyGraz, Austria<EMAIL_ADDRESS>
(2019)

###### Abstract.

This work addresses the problem of providing and evaluating recommendations in
data markets. Since most of the research in recommender systems is focused on
the bipartite relationship between users and items (e. g., movies), we extend
this view to the tripartite relationship between users, datasets and services,
which is present in data markets. Between these entities, we identify four use
cases for recommendations: (i) recommendation of datasets for users, (ii)
recommendation of services for users, (iii) recommendation of services for
datasets, and (iv) recommendation of datasets for services. Using the open
Meta Kaggle dataset, we evaluate the recommendation accuracy of a popularity-
based as well as a collaborative filtering-based algorithm for these four use
cases and find that the recommendation accuracy strongly depends on the given
use case. The presented work contributes to the tripartite recommendation
problem in general and to the under-researched portfolio of evaluating
recommender systems for data markets in particular.

Tripartite Recommendations; Data Markets; Recommender Systems; Collaborative
Filtering; Offline Evaluation; DMA

††journalyear: 2019††copyright: acmcopyright††conference: REVEAL@RecSys’2019;
September 16-20, 2019; Copenhagen, Denmark††price: 15.00††doi:
xx.xxx/xxxxxx.xxxxxx††isbn: xxx-x-xxxx-xxxx-x/xx/xx

##  1\. Introduction

Data-driven services are becoming an increasingly important aspect of the
modern economy, with data markets playing a key role as broker between the
stakeholders of the data-driven ecosystem. Various initiatives have been
started to research the requirements and dynamics of data markets. To name two
examples, the “Data Market Austria” (DMA)111https://datamarket.at/en/ (Traub
et al., 2017) is a national project in Austria, while “A European AI On Demand
Platform and Ecosystem” (AI4EU)222https://www.ai4eu.eu/ aims at creating a
market platform for data and artificial intelligence solutions on the European
level.

For successful collaborations in data markets, the different entities need to
collaborate with each other in order to create new solutions and to be able to
provide innovative data products (Cavanillas et al., 2016; Curry, 2016).

Problem and objective of this work. Recommender services thereby play a
crucial role in data markets, since their suggestions allow to discover
potential new combinations between users, datasets and services (Damiani et
al., 2015). This results in a more complex tripartite relationship comprising
users, datasets and services, as well as an increased number of use cases, in
comparison with a traditional recommender setting. The tripartite structure
and use cases are depicted in Figure 1.

However, most of the research in recommender systems is focused on settings
consisting only of users and items, like recommending new movies to viewers.
Hence, these settings can be categorized as bipartite relationships. The work
of (Godoy and Corbellini, 2016) points out the research need for
recommendations in tripartite relationship scenarios such as the data markets
scenario investigated in the work at hand. Another issue is the lack of an
open dataset for the evaluation of tripartite recommendations in data markets.
Therefore, we propose the use of the open Meta Kaggle dataset of the well-
known data science portal Kaggle.

Figure 1. The tripartite relationship in a data market is spun between users,
datasets and services. We identify four use cases for recommendations between
these three identities, namely recommendation of datasets for users (UC1),
recommendation of services for users (UC2), recommendation of datasets for
services (UC3) and recommendation of services for datasets (UC4).

Contributions and findings. The contributions of our work are two-fold:

  * •

We propose four use cases as well as a system architecture for recommendations
in data markets (see Section 2).

  * •

We provide evaluation results for a popularity-based as well as collaborative
filtering-based algorithm for these four use cases using the open Meta Kaggle
dataset (see Section 3).

Figure 2. The system architecture of our recommender system for data markets
is build upon ScaR as well as the open-source frameworks Apache Solr and
ZooKeeper. The communication between the modules is handled via REST-based Web
services.

Our results show that the recommendation accuracy strongly depends on the
given use case. For example, in settings in which we have a limited set of
candidate entities to recommend, already the simple popularity-based approach
(recommending the most popular (MP) entities) provides good results. However,
in more complex settings, where it is required to link services and datasets,
a personalized approach such as collaborative filtering (CF) should be
favored.

Taken together, our work contributes to the under-researched portfolio of
recommender systems for data markets and thus, should be of interest for both
researchers and practitioners in this area.

##  2\. Recommendations in Data Markets

This section gives an a detailed overview of the four central data market use
cases followed by the architecture of the proposed recommender system and all
its components.

###  2.1. Use Cases

As depicted in Figure 1, data markets create a tripartite relationship between
their entities users, datasets and services, thus leading to more complex
recommendation problems. We identify four use cases for recommendations in the
setting of data markets, investigated in more detail in the remainder of this
subsection.

UC1: Recommendation of datasets for users. In the first use case, we recommend
datasets to users. Thus, this one reflects a rather classic item2user
recommendation problem, in which we analyze past user interactions between the
target user and datasets (e. g., clicks or purchases) in order to recommend
other datasets that could be interesting for the user (e. g., by using CF).

UC2: Recommendation of services for users. The second use case also reflects a
classic item2user recommendation problem but this time we aim to recommend
services for users of the data market. Since typically there are more services
than datasets available in a data market (see Section 3.1), the set of
potential candidate services is also larger, which makes this recommendation
problem potentially harder than the one of UC1.

UC3: Recommendation of datasets for services. UC3 reflects a more complex
recommendation problem, in which we aim to recommend datasets for services. As
both entities are now item types, we do no longer have classic user
interactions for CF as we have in UC1 and UC2. To overcome this, we could
establish an indirect connection between a dataset and a service when a user
has interacted with both, the dataset and the service (see Section 3.1).

UC4: Recommendation of services for datasets. In the fourth and final use
case, we recommend services for datasets. As mentioned in UC2, we typically
have more services than datasets available in a data market, which makes this
use cases more complex than UC3, where the set of candidate entities to
recommend is smaller. Furthermore, in UC4, we want to link services and
datasets, where we do not have direct user interactions available. Thus, we
believe that this use case is the most complex one and therefore, we also
expect the lowest recommendation accuracy for this one (see Section 3.3).

###  2.2. System Architecture

The design of the system architecture of our recommender system for data
markets is centred upon the scalable recommendation framework
ScaR333http://scar.know-center.tugraz.at/ (Lacic et al., 2015; Lacic et al.,
2014). In Figure 2, we illustrate our main modules and how they interact with
each other as well as with users and administrators of a data market. Apache
ZooKeeper444https://zookeeper.apache.org/ is used for handling the
communication between the modules and for load balancing (e.g., deploying
multiple instances of a module).

Service Provider (SP). The SP acts as a proxy for data markets to interact
with the recommender system. It provides REST-based Web services to enable
users to query recommendations of datasets and services, and to add new data
(e. g., user interactions, datasets or services) to the system.

Data Modification Layer (DML) & Apache Solr. The DML encapsulates all
database-related CRUD operations (i. e., create, retrieve, update, delete) in
one module and thus, enables easy access to the underlying data backend. As
shown in Figure 2, we utilize the high-performance search engine Apache
Solr555http://lucene.apache.org/solr/. This data backend solution not only
guarantees scalability and (near) real-time recommendations but also the
support of multiple entities like the users, datasets and services we
encounter here.

Recommender Engine (RE). The RE is the main module of our recommender system
for data markets as it is responsible for calculating recommendations. Here,
we make use of Apache Solr’s build-in data structures for efficient similarity
calculations. Currently, we focus on popularity and CF-based recommendation
algorithms, but the RE module could be easily extended with further algorithms
as well (e. g., content-based filtering).

Recommender Customizer (RC). The RC is used to change the parameters (e. g.,
the number of recommended entities $k$) of the recommendation approaches on
the fly. Thus, it holds a so-called recommendation profile for each approach,
accessible and changeable by the data market administrator. These changes are
then broadcast to the RE to be aware of how a specific approach should be
executed.

Recommender Evaluator (REV). The REV is responsible for evaluating the
recommendation algorithms implemented in the RE. Hence, it can be executed to
perform an offline evaluation with training/test set splits (see Section 3.2).
In the future, it will also be possible to conduct online evaluations in data
markets via A/B-tests.

##  3\. Evaluation

In this section, we present our evaluation study, in which we compare
popularity-based with CF-based recommendations for all four use cases defined
in Section 2.1.

###  3.1. Data

For our evaluation, we use the open Meta Kaggle
dataset666https://www.kaggle.com/kaggle/meta-kaggle (2017-11-15) of the well-
known Kaggle data science portal in order to simulate a real-world data
market. Here, we have 6,108 users and 45 datasets that are connected via 2,926
user/dataset interactions, where an interaction is given by a user writing
about a dataset in a discussion thread. Furthermore, we have 3,334 services
that are connected to the 6,108 users via 18,593 user interactions. These
interactions are created by users voting for a service.

Finally, we establish a collaboration network between datasets and services
(see e.g., (Hasani-Mavriqi et al., 2018)). Thus, we create a link between a
dataset and a service when a user has interacted with both, the dataset and
the service, which leads to 95,249 interactions. The full statistics of our
dataset are summarized in Table 1.

Feature |  #
---|---
Number of users |  6,108
Number of datasets |  45
Number of services |  3,334
Number of user/dataset interactions |  2,962
Number of user/service interactions |  18,593
Number of dataset/service interactions |  95,249
Table 1. Statistics of our dataset, which was collected from the Meta Kaggle
platform in order to simulate the entities and interactions in a real-world
data market. As the number of datasets is much smaller than the number of
services, we also expect better recommendation accuracy results for UC1 and
UC3 than for UC2 and UC4.

###  3.2. Evaluation Method

In this section, we describe the evaluation protocol, recommendation
algorithms and evaluation metrics used for our study.

Evaluation protocol. For measuring the recommendation quality in the settings
of the four use cases UC1 - UC4, we follow common practice in the area of
recommender systems and split our Meta Kaggle dataset into training and test
splits as suggested by (Herlocker et al., 2004).

Specifically, we extract all entities with at least eleven interactions from
whom we withhold ten interactions for the test set and use the rest for
training777We only evaluate users with a minimum of eleven interactions to
ensure that we have at least one interactions for training when using ten
interactions for testing. (Parra and Brusilovsky, 2009). Thus, for UC1, this
results into 17 users for whom we recommend datasets; for UC2, this results
into 184 users for whom we recommend services; for UC3, this results into
2,338 services for whom we recommend datasets; for UC4, this results into 44
datasets for whom we recommend services.

Recommendation algorithms. We evaluate our four uses cases for recommendations
in data markets with two algorithms, namely most popular (MP) and (ii)
collaborative filtering (CF). The recommendations are calculated and evaluated
using the recommender system presented in Section 2.2.

MP is a non-personalized algorithm and is especially useful for new entities
in a data market without any interactions so far, commonly referred as cold-
start entities (Schein et al., 2002). This approach recommends datasets or
services, which are weighted and ranked by the number of interactions. As
mentioned, the MP approach is non-personalized and thus, each entity will
receive the same recommendations.

CF algorithms (Ricci et al., 2011) analyze the interactions between users and
entities, e. g., datasets and services alike. In CF methods two users are
treated as similar if they have interacted with similar entities in the past.
Hence, entities a similar user has interacted with in the past are candidates
to recommend to a target user, who has not interacted with those entities yet.
In the case of data markets, we do not only have interactions between users
and entities but also between entities themselves when we consider UC3 and
UC4. Here, we realize the CF approach in a similar way but instead of
calculating user similarities, we calculate similarities between datasets and
services, respectively.

Evaluation metrics. For measuring the accuracy of the recommendations in data
markets, we use a rich set of metrics, namely Precision (P@$k$), F1-score
(F1@$k$), Recall (R@$k$), Mean Reciprocal Rank (MRR@$k$), Mean Average
Precision (MAP@$k$) and normalized Discounted Cumulative Gain (nDCG@$k$)
(Järvelin and Kekäläinen, 2002).

We report these metrics for different numbers of recommended entities (= $k$),
i. e., P@1 for $k$ = 1, F1@5 for $k$ = 5888For 10 recommended entities,
Precision typically reaches its highest value for $k$ = 1 and F1 for $k$ = 5.,
R@10 for $k$ = 10, MRR@10 for $k$ = 10, MAP@10 for $k$ = 10 and nDCG@10 for
$k$ = 10. Please note that we set the maximum number of $k$ to 10, which is a
common value for the evaluation of recommender systems (Said and Bellogín,
2014).

###  3.3. Results

In this section, we present the results of our evaluation with respect to our
four use cases. Table 2 holds the resulting numbers achieved in our
experiments.

Approach | P@1 | F1@5 | R@10 | MRR@10 | MAP@10 | nDCG@10
---|---|---|---|---|---|---
UC1: MP | 0.823 | 0.470 | 0.717 | 0.217 | 0.597 | 0.729
UC1: CF | 0.705 | 0.431 | 0.611 | 0.192 | 0.484 | 0.635
UC2: MP | 0.103 | 0.050 | 0.066 | 0.023 | 0.026 | 0.072
UC2: CF | 0.137 | 0.086 | 0.114 | 0.037 | 0.054 | 0.121
UC3: MP | 1.000 | 0.411 | 0.707 | 0.232 | 0.580 | 0.750
UC3: CF | 1.000 | 0.636 | 0.934 | 0.281 | 0.925 | 0.948
UC4: MP | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000
UC4: CF | 0.022 | 0.006 | 0.006 | 0.003 | 0.004 | 0.009
Table 2. Evaluation results of our four use cases for recommendations in data
markets. While we get the best recommendation accuracy results for the
unpersonalized MP algorithm in UC1, the personalized CF approach provides the
best results for the more complex UC2, UC3 and UC4. The poor results for UC4
indicate that we need more sophisticated algorithms than MP and CF in this
setting. Please note that bold numbers indicate the best results for a use
case.

UC1: Recommendation of datasets for users. This use case reflects the least
complex one as we recommend from a quite limited set of candidate entities
(i.e., 45 datasets) with a small number of connections to the target entities
(i. e., 2,962 user interactions). This is also reflected in the recommendation
accuracy results presented in Table 2 as the unpersonalized MP approach
provides better results than the personalized CF one. This behavior of MP
outperforming CF can only be observed in this use case, which shows that
personalized approaches are not always necessary.

UC2: Recommendation of services for users. When recommending services for
users, we face a more complex problem since we have a much larger set of
candidate entities (i.e., 3,334 services). Thus, the accuracy results in UC2
are much lower than the ones in UC1. Furthermore, in this case, the CF
approach, which analyzes the 18,593 interactions between users and services in
a personalized manner, provides better results than MP.

UC3: Recommendation of datasets for services. Similar to UC1, in UC3, we also
recommend datasets but this time for services instead of users. For this use
case, we also have a large set of 95,249 interactions between datasets and
services available, leading to the overall best results for CF across all four
use cases. Interestingly, both MP and CF provide a perfect score for P@1 of
1.000, which indicates that both algorithms rank a highly-connected dataset on
the first position that is relevant for all 2,338 evaluated services.

UC4: Recommendation of services for datasets. UC4 reflects the most complex of
our use cases since we have a large set of 3,334 candidates services
available, which are linked via 95,249 interactions to a small set of 44
datasets being the evaluated entities. This is reflected in the results shown
in Table 2 as both algorithms, MP and CF, provide the worst results across all
use cases. Here, the unpersonalized MP approach even reaches a recommendation
accuracy of 0.000 for all metrics, thus not recommending a single relevant
service.

###  3.4. Discussion

Our evaluation results show that there is no one-size-fits-all solution for
recommendations in data markets. One particular finding of us is, that in
cases having a limited set of candidate entities available like in UC1,
popularity-based methods such as MP provide good results. Another finding is
that personalized methods such as CF should be favored when the use cases get
more complex, for example if we have a larger set of candidate entities as it
is the case in UC2. The same holds for the recommendations of entities to
other entities, like datasets to services in UC3.

However, our results also show that both MP and CF provide poor results for
UC4 being the most complex use case. For such a setting, we need more
sophisticated methods that incorporate also other data sources, e. g.,
content-based filtering (CBF) approaches (Lops et al., 2011). For overcoming
sparsity problems, these approaches could also be combined with word
embeddings (Kenter and De Rijke, 2015; Mikolov et al., 2013).

##  4\. Conclusion and Future Work

In this paper, we presented our initial steps for providing and evaluating
recommendations in data markets. Therefore, we first provided four potential
use cases, which included recommendation of datasets for users (UC1),
recommendation of services for users (UC2), recommendation of datasets for
services (UC3), and recommendation of services for datasets (UC4). Then, we
proposed a system architecture for a recommender system for data markets based
on the scalable recommendation framework ScaR.

Finally, we provided an evaluation of these four uses using the Meta Kaggle
dataset and our proposed recommender system. Here, we find that the
unpersonalized most popular approach (MP) provides the best results for UC1
and the personalized collaborative filtering approach (CF) provides the best
results for the more complex use cases UC2, UC3 and UC4.

Limitations and future Work. One limitation of our evaluation is that we have
simulated a real-world data market using the Meta Kaggle dataset. Although,
this dataset provides all relevant entities of data markets (i. e., users,
datasets and services), we plan to also conduct evaluation studies in real-
world data markets such as the ones created in the DMA and AI4EU initiatives.

Furthermore, so far, we have only evaluated the two algorithms MP and CF.
Thus, we also plan to extend our study with more recommendation approaches
such as content-based filtering (see Section 3.4).

Acknowledgments. This work was supported by the Know-Center GmbH, the FFG
flagship project Data Market Austria (DMA) and the H2020 project AI4EU (GA:
825619). The Know-Center GmbH is funded within the Austrian COMET Program -
Competence Centers for Excellent Technologies - under the auspices of the
Austrian Ministry of Transport, Innovation and Technology, the Austrian
Ministry of Economics and Labor and by the State of Styria. COMET is managed
by the Austrian Research Promotion Agency (FFG).

## References

  * (1)
  * Cavanillas et al. (2016) José María Cavanillas, Edward Curry, and Wolfgang Wahlster (Eds.). 2016.  New Horizons for a Data-Driven Economy.  Springer, Cham, Switzerland.  DOI:http://dx.doi.org/10.1007/978-3-319-21569-3
  * Curry (2016) Edward Curry. 2016\.  The big data value chain: definitions, concepts, and theoretical approaches.  In New horizons for a data-driven economy. Springer, Cham, 29–37.
  * Damiani et al. (2015) Ernesto Damiani, Paolo Ceravolo, Fulvio Frati, Valerio Bellandi, Ronald Maier, Isabella Seeber, and Gabriela Waldhart. 2015.  Applying recommender systems in collaboration environments.  Computers in Human Behavior 51 (2015), 1124–1133.
  * Godoy and Corbellini (2016) Daniela Godoy and Alejandro Corbellini. 2016.  Folksonomy-Based Recommender Systems: A State-of-the-Art Review.  International Journal of Intelligent Systems 31, 4 (2016), 314–346.  DOI:http://dx.doi.org/10.1002/int.21753 arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1002/int.21753
  * Hasani-Mavriqi et al. (2018) Ilire Hasani-Mavriqi, Dominik Kowald, Denis Helic, and Elisabeth Lex. 2018.  Consensus dynamics in online collaboration systems.  Computational Social Networks 5, 1 (2018), 2\.
  * Herlocker et al. (2004) Jonathan L Herlocker, Joseph A Konstan, Loren G Terveen, and John T Riedl. 2004.  Evaluating collaborative filtering recommender systems.  ACM Transactions on Information Systems (TOIS) 22, 1 (2004), 5–53.
  * Järvelin and Kekäläinen (2002) Kalervo Järvelin and Jaana Kekäläinen. 2002.  Cumulated gain-based evaluation of IR techniques.  ACM Transactions on Information Systems (TOIS) 20, 4 (2002), 422–446.
  * Kenter and De Rijke (2015) Tom Kenter and Maarten De Rijke. 2015.  Short text similarity with word embeddings. In Proc. of CIKM’15. ACM, 1411–1420.
  * Lacic et al. (2014) Emanuel Lacic, Dominik Kowald, Denis Parra, Martin Kahr, and Christoph Trattner. 2014.  Towards a scalable social recommender engine for online marketplaces: The case of apache solr. In Proceedings of the 23rd International Conference on World Wide Web. ACM, 817–822.
  * Lacic et al. (2015) Emanuel Lacic, Matthias Traub, Dominik Kowald, and Elisabeth Lex. 2015.  ScaR: Towards a Real-Time Recommender Framework Following the Microservices Architecture. In Proceedings of LSRS2015 Workshop at RecSys 2015. .
  * Lops et al. (2011) Pasquale Lops, Marco De Gemmis, and Giovanni Semeraro. 2011\.  Content-based recommender systems: State of the art and trends.  In Recommender systems handbook. Springer, 73–105.
  * Mikolov et al. (2013) Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013.  Efficient Estimation of Word Representations in Vector Space.  arXiv e-prints (Jan 2013), arXiv:1301.3781.  arXiv:cs.CL/1301.3781
  * Parra and Brusilovsky (2009) Denis Parra and Peter Brusilovsky. 2009.  Collaborative filtering for social tagging systems: an experiment with CiteULike. In Proceedings of the third ACM conference on Recommender systems. ACM, 237–240.
  * Ricci et al. (2011) Francesco Ricci, Lior Rokach, and Bracha Shapira. 2011\.  Introduction to recommender systems handbook.  Springer.
  * Said and Bellogín (2014) Alan Said and Alejandro Bellogín. 2014.  Comparative recommender system evaluation: benchmarking recommendation frameworks. In Proceedings of the 8th ACM Conference on Recommender systems. ACM, 129–136.
  * Schein et al. (2002) Andrew I Schein, Alexandrin Popescul, Lyle H Ungar, and David M Pennock. 2002.  Methods and metrics for cold-start recommendations. In Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 253–260.
  * Traub et al. (2017) Matthias Traub, Heimo Gursch, Elisabeth Lex, and Roman Kern. 2017.  Data Market Austria (Institute of Systems Sciences, Innovation and Sustainability Reports), Romana Rauter, Martina Zimek, Aisma Linda Kiesnere, and Rupert J. Baumgartner (Eds.). Institute of Systems Sciences, Innovation and Sustainability, University of Graz, 353–363.