Novel high-κ dielectrics for next-generation electronic devices screened by automated ab initio calculations

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, HfO 2 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 41800 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.

Searching for materials with high refractive index and wide band gap: A first-principles high-throughput study

“…Our aim is to provide a statistical, "data driven", analysis based on a large set of 4040 semiconductors. Calculated data confirm the global inverse trend between those two properties, as recently discussed in similar works [15][16][17]. However, there is also a wide spread of the data around this general tendency, pointing out some outliers with both relatively high refractive index and wide band gap among which well-known materials (TiO 2 , LiNbO 3 , ...), already widely used for optical applications, and other materials, not yet considered for such applications (Ti 3 PbO 7 , LiSi 2 N 3 , BeS, ...).…”

Descriptors for dielectric constants of perovskite-type oxides by materials informatics with first-principles density functional theory

“…In fact, some differences are observed between the theoretical and experimental results in this study. It is wellknown that DFT with GGA-type functionals calculates larger dielectric constants because GGA-type functionals overestimate the lattice constants of crystal structures, which affects low-frequency phonon modes [54,55]. Table 1 lists some of the highest calculated values of the dielectric constants in our DFPT simulation.…”

“…Figure 3 shows the resulting VIP scores, which reflect the importance of each explanatory variable in fitting the explanatory and objective variables. In our PLS regression analysis, the following descriptors, which are denoted by a-f in Figure 3 PLS regression was also carried out by adding band gap values extracted from DOS spectra as explanatory variables, since the previous paper revealed clear correlations between band gap and dielectric properties [55]. However, no significant change in fitting quality was observed.…”

Property database for single-element doping in ZnO obtained by automated first-principles calculations

“…(See Supplementary Fig. S6) For the first-row alkali atoms, in particular Li and Na, it is known that GGA +  U fails to describe the hole polaron in the Zn-substitutional site31. Thus, we carried out the full relaxation on Li Zn and Na Zn .…”

“…To compare various dopant configurations for so many dopants, thousands of supercell structures should be calculated, which would be formidable if one tries to carry out computations in a manual way. In a previous work, we demonstrated high-throughput screening of high-k oxides by overcoming difficulties in massive calculations through the reliable automation procedure31. Similarly, in order to establish extensive property databases of doped-ZnO in a consistent and systematic way, we develop in this work a first-principles-based automation code that explores various dopant configurations and yields formation-energy profiles for any given dopant.…”