Device performance limit of monolayer SnSe_(2) MOSFET

Two-dimensional(2D)semiconductors are attractive channels to shrink the scale of field-effect transistors(FETs),and among which the anisotropic one is more advantageous for a higher on-state current(I_(on)).Monolayer(ML)SnSe_(2),as an abundant,economic,nontoxic,and stable two-dimensional material,possesses an anisotropic electronic nature.Herein,we study the device performances of the ML SnSe_(2) metal-oxide-semiconductor FETs(MOSFETs)and deduce their performance limit to an ultrashort gate length(L_(g))and ultralow supply voltage(V_(dd))by using the ab initio quantum transport simulation.An ultrahigh I_(on) of 5,660 and 3,145µA/µm is acquired for the n-type 10-nm-L_(g) ML SnSe_(2) MOSFET at V_(dd)=0.7 V for high-performance(HP)and low-power(LP)applications,respectively.Specifically,until L_(g) scales down to 2 and 3 nm,the MOSFETs(at V_(dd)=0.65 V)surpass I_(on),intrinsic delay time(τ),and power-delay product(PDP)of the International Roadmap for Device and Systems(IRDS,2020 version)for HP and LP devices for the year 2028.Moreover,the 5-nm-L_(g) ML SnSe_(2) MOSFET(at V_(dd)=0.4 V)fulfills the IRDS HP device and the 7-nm-L_(g) MOSFET(at V_(dd)=0.55 V)fulfills the IRDS LP device for the year 2034.

Quantifying nonclassicality of multimode bosonic fields via skew information

We quantify the nonclassicality of multimode bosonic field states by adopting an information-theoretic approach involving the Wigner-Yanase skew information.The fundamental properties of the quantifier such as convexity,superadditivity,monotonicity,and conservation relations are revealed.The quantifier is illustrated by a variety of typical examples,and applications to the quantification of nonclassical correlations are discussed.Various extensions are indicated.

Selection of Single-Walled Carbon Nanotubes According to Both Their Diameter and Chirality via Nanotweezers

Diameter- and chirality-dependent interactions between aromatic molecule-based nanotweezers and single-walled carbon nanotubes (SWNTs) are revealed by density functional theory calculations. We found that the threshold diameter of selected SWNTs is determined by the end-to-end distance of the nanotweezer. Large-diameter SWNTs are preferred by a nanotweezer with an obtuse folding angle, whereas small-diameter SWNTs are favored by a nanotweezer with an acute folding angle. The adsorption can be further stabilized by the orientational alignment of the hexagonal rings of the nanotweezer and the SWNT sidewall. Therefore, by taking advantage of the supramolecular recognition ability of the aromatic molecule-based nanotweezer, SWNTs can be enriched with both controllable diameter and chirality.

Fulde-Ferrell-Larkin-Ovchinnikov pairing states between s- and p-orbital fermions

We study the pairing states in a largely imbalanced two-component Fermi gas loaded in an anisotropic two-dimensional optical lattice, where the spin-up and spin-down fermions are filled to the s- and px-orbital bands, respectively. We show that owing to the relative inversion of the band structures of the s and px orbitals, the system favors pairing between two fermions on the same side of the Brillouin zone, leading to a large stable regime for states with a finite center-of-mass momentum, i.e., the Fulde-Ferrell-Larkin-Ovchinnikov （FFLO） state. In particular, when two Fermi surfaces are close in momentum space, a nesting effect stabilizes a special type of π-FFLO phase with a spatial modulation of π along the easily tunneled x direction. We map out the zero-temperature phase diagrams within the mean-field approach for various aspect ratios within the two-dimensional plane and calculate the Berezinskii-Kosterlitz-Thouless （BKT） transition temperatures TBKT for different phases.

Interpretable learning of voltage for electrode design of multivalent metal-ion batteries

Deep learning(DL)has indeed emerged as a powerful tool for rapidly and accurately predicting materials properties from big data,such as the design of current commercial Li-ion batteries.However,its practical utility for multivalent metal-ion batteries(MIBs),the most promising future solution of large-scale energy storage,is limited due to scarce MIB data availability and poor DL model interpretability.Here,we develop an interpretable DL model as an effective and accurate method for learning electrode voltages of multivalent MIBs(divalent magnesium,calcium,zinc,and trivalent aluminum)at small dataset limits(150–500).Using the experimental results as validation,our model is much more accurate than machine-learning models,which usually are better than DL in the small dataset regime.Besides the high accuracy,our feature-engineering-free DL model is explainable,which automatically extracts the atom covalent radius as the most important feature for the voltage learning by visualizing vectors from the layers of the neural network.The presented model potentially accelerates the design and optimization of multivalent MIB materials with fewer data and less domain-knowledge restriction and is implemented into a publicly available online tool kit in http://batteries.2dmatpedia.org/for the battery community.

Progress in femtochemistry and femtobiology

Femtoscience offers a unique way to understand the dynamics in physics, chemistry and biology. This subject focuses on the process happening at femto-to pico-second time scale by femtosecond optical methods. Widely used in chemistry it reveals chemical reactions, including bond breaking, forming, and stretching, which happens at an ultrafast time scale. Femtoscience is also important in the biological system, for example, light harvesting system and vision system. Femtoscience in physics is also widely used, but it is not studied in this paper. Instead, we report new advances in femtochemistry and femtobiology, including structural dynamics, coherent control, enzyme function dynamics and hydration in the protein system. We also introduce attosecond science, focusing on electron dynamics at an extreme short time scale.

High-performance sub-10-nm monolayer black phosphorene tunneling transistors

Moore＇s law is approaching its physical limit. Tunneling field-effect transistors （TFETs） based on two-dimensional （2D） materials provide a possible scheme to extend Moore＇s law down to the sub-10-nm region owing to the electrostatic integrity and absence of dangling bonds in 2D materials. We report an ab initio quantum transport study on the device performance of monolayer （ML） black phosphorene （BP） TFETs in the sub-10-nm scale （6-10 nm）. Under the optimal schemes, the ML BP TFETs show excellent device performance along the armchair transport direction. The on-state current, delay time, and power dissipation of the optimal sub-10-nm ML BP TFETs significantly surpass the latest International Technology Roadmap for Semiconductors （ITRS） requirements for high- performance devices. The subthreshold swings are 56-100 mV/dec, which are much lower than those of their Schottky barrier and metal oxide semiconductor field-effect transistor counterparts.

Revealing nonclassicality via s-ordered phase-space distribution

Nonclassical states play a crucial role in both theoretical and experimental investigations of quantum optics, and there is a wide interest in characterization and quantification of nonclassicality. By exploiting the freedom of the parameter s in the s-ordered phase-space distribution introduced by Cahill and Glauber [Phys. Rev. 177, 1882(1969)], we develop a method to reveal and quantify optical nonclassicality via the divided difference of the s-ordered phase-space distribution. Our approach yields naturally a family of quantifiers of optical nonclassicality, which have many desirable properties such as convexity and monotonicity under the Gaussian noise channels. The quantifiers are illustrated by evaluating nonclassicality of several typical states. Two simple and convenient criteria for nonclassicality are established, which in particular certify all nonclassical Gaussian states.