www.nature.com Open in urlscan Pro
151.101.128.95 Public Scan

Back to summary

URL:
https://www.nature.com/articles/am201557
Submission: On July 09 via api (July 9th 2024, 1:14:56 pm UTC) from CZ — Scanned from DE

Form analysis
1 forms found in the DOM

GET /search

<form class="c-header__search-form" action="/search" method="get" role="search" autocomplete="off" data-test="inline-search">
  <label class="c-header__heading" for="keywords">Search articles by subject, keyword or author</label>
  <div class="c-header__search-layout c-header__search-layout--max-width">
    <div>
      <input type="text" required="" class="c-header__input" id="keywords" name="q" value="">
    </div>
    <div class="c-header__search-layout">
      <div>
        <label for="results-from" class="c-header__visually-hidden">Show results from</label>
        <select id="results-from" name="journal" class="c-header__select">
          <option value="" selected="">All journals</option>
          <option value="am">This journal</option>
        </select>
      </div>
      <div>
        <button type="submit" class="c-header__search-button">Search</button>
      </div>
    </div>
  </div>
</form>

Text Content

YOUR PRIVACY, YOUR CHOICE

We use essential cookies to make sure the site can function. We, and our 209
partners, also use optional cookies and similar technologies for advertising,
personalisation of content, usage analysis, and social media.

By accepting optional cookies, you consent to allowing us and our partners to
store and access personal data on your device, such as browsing behaviour and
unique identifiers. Some third parties are outside of the European Economic
Area, with varying standards of data protection. See our privacy policy for more
information on the use of your personal data. Your consent choices apply to
nature.com and applicable subdomains.

You can find further information, and change your preferences via 'Manage
preferences'.
You can also change your preferences or withdraw consent at any time via 'Your
privacy choices', found in the footer of every page.

We use cookies and similar technologies for the following purposes:

STORE AND/OR ACCESS INFORMATION ON A DEVICE

Cookies, device or similar online identifiers (e.g. login-based identifiers,
randomly assigned identifiers, network based identifiers) together with other
information (e.g. browser type and information, language, screen size, supported
technologies etc.) can be stored or read on your device to recognise it each
time it connects to an app or to a website, for one or several of the purposes
presented here.

PERSONALISED ADVERTISING AND CONTENT, ADVERTISING AND CONTENT MEASUREMENT,
AUDIENCE RESEARCH AND SERVICES DEVELOPMENT

Advertising and content can be personalised based on your profile. Your activity
on this service can be used to build or improve a profile about you for
personalised advertising and content. Advertising and content performance can be
measured. Reports can be generated based on your activity and those of others.
Your activity on this service can help develop and improve products and
services.

Accept all cookies Reject optional cookies Manage preferences
Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited
support for CSS. To obtain the best experience, we recommend you use a more up
to date browser (or turn off compatibility mode in Internet Explorer). In the
meantime, to ensure continued support, we are displaying the site without styles
and JavaScript.

* View all journals
* Search

Search articles by subject, keyword or author
Show results from All journals This journal
Search
Advanced search

QUICK LINKS

* Explore articles by subject
* Find a job
* Guide to authors
* Editorial policies

* Log in

* Explore content

EXPLORE CONTENT

* Research articles
* Reviews & Analysis
* News & Comment
* Collections

* Follow us on Twitter
* Sign up for alerts
* RSS feed

* About the journal

ABOUT THE JOURNAL

* Journal Information
* Open Access Fees and Funding
* About the Editors
* Contact
* For Advertisers
* Press Releases

* Publish with us

PUBLISH WITH US

* For Authors & Referees
* Language editing services
* Submit manuscript

* Sign up for alerts
* RSS feed

1. nature
2. npg asia materials
3. original article
4. article

Novel high-κ dielectrics for next-generation electronic devices screened by
automated ab initio calculations
Download PDF
Download PDF
* Original Article
* Open access
* Published: 12 June 2015

NOVEL HIGH-Κ DIELECTRICS FOR NEXT-GENERATION ELECTRONIC DEVICES SCREENED BY
AUTOMATED AB INITIO CALCULATIONS

* Kanghoon Yim1,2,
* Youn Yong1,2,
* Joohee Lee1,2,
* Kyuhyun Lee1,2,
* Ho-Hyun Nahm3,4,
* Jiho Yoo5,
* Chanhee Lee5,
* Cheol Seong Hwang1,2 &
* …
* Seungwu Han1,2

Show authors

NPG Asia Materials volume 7, page e190 (2015)Cite this article

* 28k Accesses

* 4 Altmetric

* Metrics details

ABSTRACT

As the scale of transistors and capacitors in electronics is reduced to less
than a few nanometers, leakage currents pose a serious problem to the device’s
reliability. To overcome this dilemma, high-κ materials that exhibit a larger
permittivity and band gap are introduced as gate dielectrics to enhance both the
capacitance and block leakage simultaneously. Currently, HfO2 is widely used as
a high-κ dielectric; however, a higher-κ material remains desired for further
enhancement. To find new high-κ materials, we conduct a high-throughput ab
initio calculation for band gap and permittivity. The accurate and efficient
calculation is enabled by newly developed automation codes that fully automate a
series of delicate methods in a highly optimized manner. We can, thus, calculate
>1800 structures of binary and ternary oxides from the Inorganic Crystal
Structure Database and obtain a total property map. We confirm that the inverse
correlation relationship between the band gap and permittivity is roughly valid
for most oxides. However, new candidate materials exhibit interesting
properties, such as large permittivity, despite their large band gaps. Analyzing
these materials, we discuss the origin of large κ values and suggest design
rules to find new high-κ materials that have not yet been discovered.

SIMILAR CONTENT BEING VIEWED BY OTHERS

IMPROVING BOTH PERFORMANCE AND STABILITY OF N-TYPE ORGANIC SEMICONDUCTORS BY
VITAMIN C

Article 27 June 2024

ABSOLUTE BAND-EDGE ENERGIES ARE OVER-EMPHASIZED IN THE DESIGN OF
PHOTOELECTROCHEMICAL MATERIALS

Article 26 June 2024

FERROELECTRIC FREESTANDING HAFNIA MEMBRANES WITH METASTABLE RHOMBOHEDRAL
STRUCTURE DOWN TO 1-NM-THICK

Article Open access 25 June 2024

INTRODUCTION

The dielectric insulator is a key component in microelectronic devices such as
the central processing unit (CPU), dynamic random-access memory (DRAM) and flash
memory. The basic function of the dielectric material is to enhance the
capacitive coupling between adjacent metals and semiconductors, although it
should also suppress the leakage current between electrodes, which undermines
the energy consumption (in CPU and DRAM) or long-term reliability (in flash
memory). In past decades, silicon dioxide (SiO2) has been used as an
archetypical dielectric material because it allows for defect-free, high-quality
thin-film growth. As the integration level of microelectronic devices is
currently exponentially increasing, the thickness of SiO2 has decreased to
maintain the device performance. However, if the SiO2 layer becomes thinner than
~1 nm, the leakage current due to the quantum tunneling effect begins to
dominate,1 which causes serious problems in power consumption and device
performance. This technical obstacle has been overcome by replacing SiO2 with
insulators that possess high dielectric constants (high-κ).1, 2 With high-κ
dielectrics, the dielectric thickness can be increased at the same capacitance,
thereby suppressing the leakage current.3 Currently, the favored high-κ
dielectrics are HfO2 (as the gate dielectric in CPU),2 ZrO2 (as the capacitor
dielectric in DRAM)4 and Al2O3 (as the blocking oxide in charge-trap flash
memory).3

In addition to a large dielectric constant, the high-κ dielectric is required to
have a large band gap (Eg) to suppress the charge injection from electrodes into
dielectrics that cause the leakage current. Therefore, the ideal high-κ
dielectrics should possess both large Eg and κ. Notably, when Eg and κ of
well-known oxides are plotted (see Figure 1), the trade-off relation is clearly
noticeable. That is, materials are abundant with large Eg (>~8 eV) or high κ
(>~20); however, no material has been discovered that satisfies both conditions
simultaneously. Because this observation is based on a limited set of materials,
one may question whether a material possessing both large Eg and κ may indeed
exist if the search space is expanded.

Figure 1

The experimental band gap and dielectric constant for well-known oxides. The
property region ideal for dielectrics is also shown.

Full size image

Obtaining material information on Eg and κ, particularly the static dielectric
constant, would be prohibitive if only experimental measurements were utilized.
However, with the recent advances in computational methods and facilities, it is
now feasible to compute ab initio various physical properties of the bulk phase
in a relatively short period. Recently, several attempts have been made to find
an optimal functional material using high-throughput computational screening.5,
6, 7, 8 The machine-learning approach presented by G. Pilania9 is a more
accelerated approach to predict properties of large numbers of polymers;
however, the prediction accuracy for Eg and κ is low for extending the method to
other systems. In this study, we perform ab initio calculations on ~1800 oxides
(except for 3d transition metal oxides) that cover most binary and ternary
oxides identified to date and suggest novel candidate high-κ dielectrics
suitable for each device type. To this end, we set up computational machinery
that automatically fetches structures from the database, prepares input files
and reliably performs the ab initio calculations.

MATERIALS AND METHODS

Figure 2 presents a brief scheme of our automation strategy for calculating Eg
and κ. First, all of the structures that contain specific cations are collected
from the Inorganic Crystal Structure Database.10 We exclude 3d transition metal
atoms because the electronic correlation effects are strong and Eg is moderate
(<3 eV). The structures are further screened to avoid duplicative calculations
on the same structure. The structures with partial atomic occupation are
excluded because of the difficulty in computational modeling. In addition to the
structures that are stable at ambient conditions, we consider structures that
are characterized under high-temperature or high-pressure conditions, provided
that they are theoretically stable at zero temperature and pressure conditions,
because the metastable structures can exist at ambient conditions through
doping11 or in nanocrystalline states.12 The atomic positions and lattice
parameters are then relaxed, and the theoretical equilibrium structures are
obtained. For the computational code, the Vienna Ab Initio Simulation Package13
is adopted as the core engine for the ab initio calculations. Regarding the
exchange-correlation functional between electrons, we employ the generalized
gradient approximation (GGA)14 for Eg and local density approximation (LDA)15
for κ. Because GGA and LDA underestimate the band gap, we also perform a
hybrid-functional calculation for the band-edge points identified by GGA. The
detailed procedure for each step is provided in the Methods section. The
reliability of the present automation procedure is confirmed by comparing the
extant experimental data with previous calculations.

Figure 2

Schematic automation strategy to collect computational data on the band gap and
static dielectric constant. The initial structures collected from the ICSD were
filtered for the ensuing ab initio computations.

Full size image

STRUCTURAL RELAXATION

All experimental structures should first be relaxed theoretically. This step is
mandatory because when computing a dielectric constant, the structures are
assumed to be at the local equilibrium. The k-points in the first Brillouin zone
are carefully and automatically sampled such that the total energy and stress
tensor components are converged within 5 meV per atom and 10 kbar, respectively.
The energy cutoff for the plane-wave basis set is selected based on the atomic
species and pseudopotential types. The structural relaxation is performed until
the atomic force and stress tensor are reduced to below 0.02 eV Å−1 and 5 kbar,
respectively. Many structures reported at high-temperature or high-pressure
conditions often have higher symmetry compared with low-temperature phases. When
these structures are relaxed with the symmetry maintained, the final structures
often possess unstable phonon modes. We exclude these structures from
consideration as new high-κ candidates because of their stability issue.

COMPUTATION OF BAND GAP

We first computed energy levels along the lines connecting the high-symmetry
k-points within GGA.16 On the basis of the results, the k-points corresponding
to the valence top and conduction minimum are determined. Because the band gap
underestimation is severe in the semilocal functional, we additionally perform a
hybrid-functional (HSE06) calculation17 (without further structural relaxation)
and calculate the energy levels at the band-edge k-points identified by GGA
(this scheme is called HSE@GGA hereafter). This process assumes that the band
structure rigidly shifts upon application of the hybrid functional.
Supplementary Figure S1 in the Supplementary Information shows that the band
structure from the hybrid functional is approximately a rigid shift of the GGA
result. By adopting the HSE@GGA scheme, we could significantly reduce the
computational cost compared with the full hybrid-functional calculations. For
oxides that include heavy elements such as Tl, Pb and Bi, the spin-orbit
coupling is included in computing Eg.

To ensure the reliability of the present scheme, test calculations on several
oxides are performed and compared with the experiment and previous GGA
calculations (see Figure 3a). The estimated energy gaps are in good agreement
with the experiment, although sizeable errors of up to 1 eV are noticeable for
oxides with Eg larger than ~4 eV. These errors are due to the fixed fraction of
the exact exchange term in the HSE06 functional, which should be increased in
the large-gap materials.18 This finding implies that more sophisticated methods
such as GW calculations can be utilized to obtain more precise values of Eg. We
note that the GW calculations are too expensive to be incorporated into the
high-throughput screening. In addition, it is not yet known which level of GW
approximations can be universally applied to every class of oxides.19
Nevertheless, the accuracy of HSE@GGA is sufficient to screen promising
dielectrics in the present study.

Figure 3

Comparison of theoretical and experimental data for the (a) band gap (Eg) and
(b) static dielectric constant (κ); some reference data from other ab initio
studies (LDA(ref) and GGA(ref)) are also shown as open symbols.20, 33, 34, 35,
36, 37, 38, 39 The solid line indicates perfect agreement with the experiment.
HSE@GGA indicates the band gap obtained using the hybrid-functional calculations
on the band-edge points identified by GGA.

Full size image

COMPUTATION OF DIELECTRIC CONSTANT

To compute the electronic permittivity, the linear-response method based on the
density functional perturbation theory is used to obtain Born effective charges
and phonon modes at the zone center.20 Because the linear-response computation
is sensitive to the k-point sampling, we double the k-point density along each
direction. The static dielectric constant () is then calculated using the
following formula:

where , Ω, ωm and denote the dielectric tensor contributed by electrons, the
unit-cell volume, the frequency of the infrared-active phonon with the mode
number of m and the mode-effective Born effective charges, respectively. The
subscripts α and β in equation (1) indicate the directions. The dielectric
constant (κ) is obtained by averaging the diagonal components of . The
theoretical κ of some oxides determined by GGA and LDA are compared with the
experiment (see Figure 3b). The mean absolute errors for materials with κ<30 are
2.06 and 1.37 for the GGA and LDA results, respectively. The GGA results exhibit
larger errors than those of LDA because the ionic part of κ is sensitive to the
low-frequency phonon modes that are significantly softened when the lattice
parameters are expanded in GGA. Therefore, we employ LDA for computing κ.

COMPUTATIONAL COST

The average computational cost was ~70 CPU hours per structure on the 24-core
cluster. The most time-consuming part was the hybrid-functional calculation in
the HSE@GGA scheme (50%) followed by the density functional perturbation theory
calculations for the dielectric constant (27%). The structure relaxation and
band-edge searching consumed ~15% and 8% of the total computational time,
respectively.

RESULTS

TOTAL PROPERTY MAP

We calculated 1762 oxides in total and obtained a large property database.
Figure 4 presents the property map of Eg versus κ for 1158 binary and ternary
oxides. We excluded the structures that were metallic or unstable under the
ambient condition according to the results. First, the inverse relation between
Eg and κ was roughly valid, and the oxides satisfying the ideal condition were
scarce. To select candidate high-κ oxides from the database, we defined a figure
of merit for reducing the leakage current. The leakage current density by direct
tunneling (JDT) can be expressed using the following semi-empirical formula:21

Figure 4

Eg vs κ plot for computed structures for 1158 oxides. Each point is color coded
according to the figure of merit (Eg·κ). The candidate oxides that have not yet
been tested are indicated by the chemical formula. The rough boundary of
material properties that are adequate for each device type is marked by dashed
lines. CPU, central processing unit.

Full size image

where q and meff are the charge and tunneling effective mass, respectively, of
the electron or hole, Фb is the injection barrier, and tox,eq represents the
equivalent oxide thickness. We define (meff Фb )1/2κ in the exponent as the
figure of merit (fFOM), which indicated that the tunneling current is
exponentially suppressed with fFOM. One can compute meff and Фb theoretically,22
but the process requires demanding calculations that are not compatible with the
high-throughput approach. Here we make a crude but reasonable assumption that
the two parameters are roughly proportional to Eg,23 which approximates fFOM as
simply Eg·κ. Each point in Figure 4 is color coded according to fFOM.

NEW CANDIDATE HIGH-Κ MATERIALS

Among the oxides in Figure 4, c-BeO, the high-pressure phase of BeO in the
rocksalt structure (see Figure 5a), is particularly prominent as it has the
unusual combination of 10.1 eV and 275 for Eg and κ, respectively. Consequently,
its fFOM is considerably beyond those of other materials. The wurtzite BeO
(w-BeO), which is stable at ambient conditions, has already been used in
dielectric applications as it has the largest band gap among all the oxides. The
atomic-layer-deposited w-BeO was fabricated on Si substrates and employed as a
diffusion barrier of oxygen between Si and high-κ dielectrics such as HfO2.24
However, w-BeO has a very small κ of ~7. However, c-BeO has not been applied in
microelectronic devices to date as far as we are aware. The origin of the large
κ of c-BeO is its soft optical phonon mode (~3.5 THz), wherein the Be and O
atoms vibrate in opposite directions (see Figures 5a and b). In c-BeO, the Be–O
bond length is longer than that in w-BeO by 0.11 Å, which softens the optical
phonon mode. The computed total energy indicates that c-BeO is less stable than
w-BeO by 0.483 eV per atom. The large energy difference implies that c-BeO would
be difficult to stabilize under ambient conditions. However, we pay attention to
the experiments in Adelmann et al.25, Kita et al.26 and Tsipas et al.27, which
indicate that the high-temperature phases of HfO2 and ZrO2 can be synthesized as
thin films by external doping or strain. More importantly, the doped phases
possessed increased dielectric constants, as predicted by theory.28 Therefore,
we reasonably expect that c-BeO can be stabilized by doping or strain and will
exhibit physical properties similar to the present calculations.

Figure 5

(a) The unit-cell structure of c-BeO and the lowest phonon mode indicated by
arrows. The frequency of this mode (ω) is also noted. (b) The phonon dispersion
curve of c-BeO. The lowest phonon mode (threefold degenerate) is marked by a
circle. The unit-cell structure of (c) NbOCl3 and (d) Na2SO4. The lowest phonon
modes that are responsible for most of κ are shown in each structure with the
vibrational directions indicated by arrows.

Full size image

Except for c-BeO, we could not find any outstanding high-κ dielectrics with
either Eg or κ larger than those of the HfO2 thin films currently used in CPU or
DRAM (Eg~6.0 eV and κ~20–25; see t-HfO2 in Figure 4). Nevertheless, we
identified several candidates that are noteworthy and list them in Table 1.
These materials could be important in the future as the main material shifts
from Si to Ge and GaAs and a more diverse selection of gate dielectrics is
highly demanded to meet chemical conditions that are different from Si. Here we
exclude oxides that have been studied previously. In the last column of Table 1,
we mark the appropriate device type according to the material properties; for
CPU and DRAM devices, the international technology roadmap of semiconductors
states that further device scaling requires higher-κ dielectrics with κ>30.29 We
note the candidate high-κ materials that satisfy this condition and have larger
fFOM than that of t-HfO2 (fFOM~210). We also limited Eg>4 eV for CPU and Eg>3 eV
for DRAM, considering that the ideal Φb should be at least >1.5 eV. For the
blocking oxides used in flash memory, large values of Eg are more crucial than
large κ to satisfy the more stringent leakage current specification
(<~10−9 Acm−2) compared with DRAM (<~10−7 Acm−2) and CPU (<~10−1 Acm−2). We,
therefore, impose the condition of Eg>6 eV and fFOM>80, which is larger than the
values for Al2O3, the currently favored high-κ blocking oxide in the charge-trap
flash memory. The total candidate lists including those for DRAM and Flash are
provided in Tables 2 and 3, respectively. For the gate dielectric for CPU, we
further consider that the stability of high-κ/Si interfaces is important to
prevent the unintentional oxidation of the Si substrate. One metric of oxide
stability is the formation energy of oxygen vacancies (Evac) because it reflects
the strength of metal–oxygen bonding. The computed Evacs for the candidate
materials are listed in Table 1. Because the Evac of SiO2 is 5.6 eV, oxides with
Evac values larger than this value may form a stable interface with Si. For
example, it is known that ZrO2 (Evac=5.1 eV) exhibits a stability issue on Si
substrates,30 whereas HfO2 (Evac=6.9 eV) is stable. This behavior is reflected
in selecting candidate oxides for CPU in Table 1. ΔE per atom in Table 1 denotes
the total energy per atom relative to the most stable phase identified from the
present computation and indicates the relative thermal stability of the given
phase.

Table 1 New candidate materials suitable for high-κ dielectrics selected from
Figure 3
Full size table
Table 2 All high-κ candidate materials for DRAM and CPU from the present
property database, which satisfy fFOM>210, and Eg>3 eV (for DRAM) or Eg>4 eV
(for CPU)
Full size table
Table 3 All high-κ candidate materials for flash memory application from the
present property database, which satisfy fFOM>80 and Eg>6 eV
Full size table

DISCUSSION

In Figures 5c and d, the structures of two candidate oxides, AlO(OH) and Na2SO4,
are displayed together with the lowest phonon mode, indicated by arrows. These
structures exhibit the two representative features of high-κ ternary oxides. In
the lowest phonon modes of AlO(OH) (Figure 5c), cations vibrate within the
octahedral cage formed by anions. In contrast to face-sharing octahedra in
Al2O3, H+ ions in AlO(OH) lead to corner sharing, which softens the vibrational
mode of Al3+. NbOCl3 in Table 1 also has a similar feature of cationic vibration
within the corner-sharing octahedra. However, all of the other ternary oxides in
Table 1 have another common feature, which can be represented by Na2SO4 in
Figure 5d: they usually contain non-metal oxygen units such as (SO4)2−, (PO4)3−
and (IO3)−. These units form a rigid bond in the compounds, whereas the other
type of cation is loosely bound to oxygen and yields soft phonon modes (see Na
atoms in Figure 5d). Furthermore, we find that the unblocked cation channel is
critical for a large dielectric constant. For example, two polymorphs of Na2SO4
have common orthorhombic phases but slightly different atomic arrangements with
a crystal symmetry of Cmcm and Pbnm, respectively (see Supplementary Figure S2
in Supplementary Information). The coordination number and oxidation state of
each atom are similar, and Eg is thus almost the same. However, the two κ values
significantly differ (Cmcm: 20.7 vs Pbnm: 5.9).

For the Cmcm structure, as shown in Figure 5d, all of the cations in the soft
mode vibrate coherently along certain passages without being blocked by other
species of atoms. However, such an unblocked channel does not exist in the Pbnm
phase. Consequently, the Na atoms oscillate in rather random directions, and the
contributions to dielectric polarization cancel each other out, resulting in
smaller κ. Note that the ionic conduction of Na+ can cause device instability31.
However, considering the lower ionic conductivity in the Cmcm structure compared
with that for other phases32, we cautiously believe that the high-κ phase of
Na2SO4 could be employed in microelectronic devices.

On the basis of these observations, high-κ ternary oxides that simultaneously
exhibit large Eg and κ might be identified through two models: (1) cationic
vibration within the corner-sharing octahedral cage of anions and (2) channeled
structures formed by a combination of metal ions and various non-metal oxide
units. Considering the numerous possible combinations to form ternary oxides, we
expect that several ideal dielectric materials that have these features could be
identified in the future.

In summary, we screened ~1800 binary and ternary oxides using high-throughput ab
initio calculations of the band gap and static dielectric constant with the aim
to find new candidate high-κ dielectrics that can be used in various
microelectronic devices such as CPU, DRAM and flash memory. From the obtained
property database, we generated a materials map of the band gap versus static
dielectric constant and identified new candidate materials that have not been
considered in previous studies. From the detailed analysis on the atomic
structure and phonon mode, we identified key factors that correlate with the
large dielectric constant. By suggesting new candidate high-κ materials and
developing a large material property database covering most binary and ternary
oxides, the present work will contribute greatly to selecting functional oxides
that are optimal for specific applications. An automated high-throughput study
on oxygen vacancy defects in oxides is also in progress.

REFERENCES

1. Kingon, A. I., Maria, J.-P. & Streiffer, S. K. Alternative dielectrics to
silicon dioxide for memory and logic devices. Nature 406, 1032–1038 (2000).

Article CAS Google Scholar

2. Robertson, J. High dielectric constant gate oxides for metal oxide Si
transistors. Rep. Prog. Phys. 69, 327–396 (2006).

Article CAS Google Scholar

3. Lee, C.-H., Hur, S.-H., Shin, Y.-C., Choi, J.-H., Park, D.-G. & Kim, K.
Charge-trapping device structure of SiO2 /SiN/high-k dielectric Al2O3 for
high-density flash memory. Appl. Phys. Lett. 86, 152908 (2005).

Article Google Scholar

4. Kim, S. K., Lee, S. W., Han, J. H., Lee, B., Han, S. & Hwang, C. S.
Capacitors with an equivalent oxide thickness of &lt;0.5 nm for nanoscale
electronic semiconductor memory. Adv. Funct. Mater. 20, 2989–3003 (2010).

Article CAS Google Scholar

5. Hautier, G., Miglio, A., Ceder, G., Rignanese, G.-M. & Gonze, X.
Identification and design principles of low hole effective mass p-type
transparent conducting oxides. Nat. Commun. 4, 2292 (2013).

Article Google Scholar

6. Fujimura, K., Seko, A., Koyama, Y., Kuwabara, A., Kishida, I., Shitara, K.,
Fisher, C. A. J., Moriwake, H. & Tanaka, I. Accelerated materials design of
lithium superionic conductors based on first-principles calculations and
machine learning algorithms. Adv. Energy Mater 3: 8 980–985 (2013).

Article CAS Google Scholar

7. Bennett, J. W. & Rabe, K. M. Integration of first-principles methods and
crystallographic database searches for new ferroelectrics: Strategies and
explorations. J. Solid State Chem. 195, 21–31 (2012).

Article CAS Google Scholar

8. Curtarolo, S., Hart, Gus, L. W., Nardelli, M. B., Mingo, N., Sanvito, S. &
Levy, O. The high-throughput highway to computational materials design.
Nat. Mater. 12, 191–201 (2013).

Article CAS Google Scholar

9. Pilania, G., Wang, C., Jiang, X., Rajasekaran, S. & Ramprasad, R.
Accelerating materials property predictions using machine learning. Sci.
Rep. 3, 2810 (2013).

Article Google Scholar

10. Karlsruhe, F. I. Z . Inorganic Crytal Structure Database, Available at
http://icsd.fiz-karlsruhe.de.

11. Lee, C.-K., Cho, E., Lee, H.-S., Hwang, C. S. & Han, S. First-principles
study on doping and phase stability of HfO2 . Phys. Rev. B 78, 012102
(2008).

Article Google Scholar

12. McHale, J. M., Auroux, A., Perrotta, A. J. & Navrotsky, A. Surface energies
and thermodynamic phase stability in nanocrystalline aluminas. Science 277,
788 (1997).

Article CAS Google Scholar

13. Kresse, G. & Hafner, J. Ab initio molecular dynamics for liquid metals.
Phys. Rev. B 47, 558 (1993).

Article CAS Google Scholar

14. Perdew, J. P., Burke, K. & Ernzerhof, M. Generalized gradient approximation
made simple. Phys. Rev. Lett. 78, 1396 (1997).

Article CAS Google Scholar

15. Ceperley, D. M. & Alder, B. J. Ground state of the electron gas by a
stochastic method. Phys. Rev. Lett. 45, 566 (1980).

Article CAS Google Scholar

16. Setyawan, W. & Curtarolo, S. High-throughput electronic band structure
calculations: Challenges and tools. Comput. Mater. Sci. 49, 299 (2010).

Article Google Scholar

17. Heyd, J., Scuseria, G. E. & Ernzerhof, M. J. Hybrid functionals based on a
screened Coulomb potential. Chem. Phys. 118, 8207–8215 (2003).

CAS Google Scholar

18. Marques, M. A. L., Vidal, J., Oliveira, M. J. T., Reining, L. & Botti, S.
Density-based mixing parameter for hybrid functionals. Phys. Rev. B 83,
035119 (2011).

Article Google Scholar

19. Kang, Y., Kang, G., Nahm, H.-H., Cho, S.-H., Park, Y. S. & Han, S. GW
calculations on post-transition-metal oxides. Phys. Rev. B 89, 165130
(2014).

Article Google Scholar

20. Lee, C.-K., Cho, E., Lee, H., Seol, K. & Han, S. Comparative study of
electronic structures and dielectric properties of alumina polymorphs by
first-principles methods. Phys. Rev. B 76, 245110 (2007).

Article Google Scholar

21. Yeo, Y.-C. MOSFET gate leakage modeling and selection guide for alternative
gate dielectrics based on leakage considerations. IEEE Trans. Electron Dev.
50: 4 1027 (2003).

Article CAS Google Scholar

22. Robertson, J. Band offsets of wide-band-gap oxides and implications for
future electronic devices. J. Vac. Sci. Technol. B 18, 1785 (2000).

Article CAS Google Scholar

23. Hinkle, C.L., Fulton, C., Nemanich, R.J. & Lucovsky, G. A novel approach
for determining the effective tunneling mass of electrons in HfO2 and other
high-K alternative gate dielectrics for advanced CMOS devices.
Microelectron. Eng. 72, 257–262 (2004).

Article CAS Google Scholar

24. Yum, J. H., Bersuker, G., Akyol, T., Ferrer, D.A., Lei, M., Park, K. W.,
Hudnall, T. W., Downer, M. C., Bielawski, C. W., Yu, E. T., Price, J., Lee,
J. C. & Banerjee, S. K. Epitaxial ALD BeO: efficient oxygen diffusion
barrier for EOT scaling and reliability improvement. IEEE Trans. Electron
Dev. 58, 4384–4392 (2011).

Article CAS Google Scholar

25. Adelmann, C., Tielens, H., Dewulf, D., Hardy, A., Pierreux, D., Swerts, J.,
Rosseel, E. X., Shi, E., Van Bael, M. K., Kittl, J.A. & Van Elshochta, S.
Atomic Layer Deposition of Gd-Doped HfO2 Thin Films. J. Electrochem. Soc
157, 4 (2010).

Article Google Scholar

26. Kita, K., Kyuno, K. & Toriumi Akira. Permittivity increase of yttrium-doped
HfO2 through structural phase transformation. Appl. Phys. Lett. 86, 102906
(2005).

Article Google Scholar

27. Tsipas, P., Volkis, S. N., Sotiropoulos, A., Galata, S. F., Mavrou, G.,
Tsoutsou, D., Panayiotatos, Y., Dimoulas, A., Marchiori, C. & Fompeyrine,
J. Germanium-induced stabilization of a very high-k zirconia phase in
ZrO2/GeO2 gate stacks. Appl. Phys. Lett. 93, 082904 (2008).

Article Google Scholar

28. Zhao, X. & Vanderbilt, D. First-principles study of structural,
vibrational, and lattice dielectric properties of hafnium oxide. Phys. Rev.
B 65, 233106 (2002).

Article Google Scholar

29. International Technology Roadmap for Semiconductors (2013)
http://www.itrs.net/, accessed on May 2014.

30. Fulton, C. C., Cook, T. E. Jr., Lucovsky, G. & Nemanich, R. J. Interface
instabilities and electronic properties of ZrO2 on silicon (100). J. Appl.
Phys. 96, 2665 (2004).

Article CAS Google Scholar

31. Snow, E. H., Grove, A. S., Deal, B. E. & Sah, C. T. Ion transport phenomena
in insulating films. J. Appl. Phys. 36, 1664 (1965).

Article CAS Google Scholar

32. Choi, B.-K. & Lockwood, D. J. Ionic conductivity and the phase transitions
in Na2SO4 . Phys. Rev. B 40, 4683 (1989).

Article CAS Google Scholar

33. Moakafi, M., Khenata, R., Bouhemadou, A., Khachai, H., Amrani, B., Rached,
D. & R’erat, M. Electronic and optical properties under pressure effect of
alkali metal oxides. Eur. Phys. J. B 64, 35–42 (2008).

Article CAS Google Scholar

34. Labat, F., Baranek, P., Domain, C., Minot, C. & Adamo, C. Density
functional theory analysis of the structural and electronic properties of
TiO2 rutile and anatase polytypes: Performances of different
exchange-correlation functionals. J. Chem. Phys. 126, 154703 (2007).

Article Google Scholar

35. Jaffe, J. E., Bachorz, R. A. & Gutowski, M. Low-temperature polymorphs of
ZrO2 and HfO2: a density-functional theory study. Phys. Rev. B 72, 144107
(2005).

Article Google Scholar

36. Xiong, K., Robertson, J. & Clark, S. J. Behavior of hydrogen in wide band
gap oxides. J. App. Phys. 102, 083710 (2007).

Article Google Scholar

37. Rignanese, G.-M. Dielectric properties of crystalline and amorphous
transition metal oxides and silicates as potential high-κ candidates: the
contribution of density-functional theory. J. Phys. Condens. Matter 17,
R357–R379 (2005).

Article CAS Google Scholar

38. Lukačević, I. High-pressure lattice dynamics and thermodynamics in BaO.
Phys. Status Solidi B 248, 1405–1411 (2011).

Article Google Scholar

39. Lee, B., Lee, C.-K., Hwang, C.S. & Han, S. Influence of
exchange-correlation functionals on dielectric properties of rutile TiO2 .
Curr. Appl. Phys. 11, S293–S296 (2011).

Article Google Scholar

Download references

ACKNOWLEDGEMENTS

This research was supported by the EDISON program (NRF-2012M3C1A6035307). The
computations were performed at the KISTI supercomputing center
(KSC-2014-C3-012).

AUTHOR INFORMATION

AUTHORS AND AFFILIATIONS

1. Department of Materials Science and Engineering, Seoul National University,
Seoul, South Korea

Kanghoon Yim, Youn Yong, Joohee Lee, Kyuhyun Lee, Cheol Seong
Hwang & Seungwu Han

2. Research Institute of Advanced Materials, Seoul National University, Seoul,
South Korea

Kanghoon Yim, Youn Yong, Joohee Lee, Kyuhyun Lee, Cheol Seong
Hwang & Seungwu Han

3. Center for Correlated Electron Systems, Institute for Basic Science (IBS),
Seoul, South Korea

Ho-Hyun Nahm

4. Department of Physics and Astronomy, Seoul National University, Seoul, South
Korea

Ho-Hyun Nahm

5. Platform Technology Lab., SAIT, Samsung Materials Research Complex, Yeong
Tong-gu, Gyeonggi-do, South Korea

Jiho Yoo & Chanhee Lee

Authors
1. Kanghoon Yim
View author publications