Search for: All records

Typical ductile materials are metals, which deform by the motion of defects like dislocations in association with non-directional metallic bonds. Unfortunately, this textbook mechanism does not operate in most inorganic semiconductors at ambient temperature, thus severely limiting the development of much-needed flexible electronic devices. We found a shear-deformation mechanism in a recently discovered ductile semiconductor, monoclinic-silver sulfide (Ag₂S), which is defect-free, omni-directional, and preserving perfect crystallinity. Our first-principles molecular dynamics simulations elucidate the ductile deformation mechanism in monoclinic-Ag₂S under six types of shear systems. Planer mass movement of sulfur atoms plays an important role for the remarkable structural recovery of sulfur-sublattice. This in turn arises from a distinctively high symmetry of the anion-sublattice in Ag₂S, which is not seen in other brittle silver chalcogenides. Such mechanistic and lattice-symmetric understanding provides a guideline for designing even higher-performance ductile inorganic semiconductors.

 

Optoelectronic properties of devices made of two-dimensional materials depend largely on the dielectric constant and thickness of a substrate. To systematically investigate the thickness dependence of dielectric constant from first principles, we have implemented a double-cell method based on a theoretical framework by Martyna and Tuckerman [J. Chem. Phys. 110, 2810 (1999)] and therewith developed a general and robust procedure to calculate dielectric constants of slab systems from electric displacement and electric field, which is free from material-specific adjustable parameters. We have applied the procedure to a prototypical substrate, Al 2 O 3 , thereby computing high-frequency and static dielectric constants of a finite slab as a function of the number of crystalline unit-cell layers. We find that two and four layers are sufficient for the high-frequency and static dielectric constants of (0001) Al 2 O 3 slabs to recover 90% of the respective bulk values computed by a Berry-phase method. This method allows one to estimate the thickness dependence of dielectric constants for various materials used in emerging two-dimensional nanophotonics, while providing an analytic formula that can be incorporated into photonics simulations. 

Abstract Neuromorphic hardware implementation of Boltzmann Machine using a network of stochastic neurons can allow non-deterministic polynomial-time (NP) hard combinatorial optimization problems to be efficiently solved. Efficient implementation of such Boltzmann Machine with simulated annealing desires the statistical parameters of the stochastic neurons to be dynamically tunable, however, there has been limited research on stochastic semiconductor devices with controllable statistical distributions. Here, we demonstrate a reconfigurable tin oxide (SnO x )/molybdenum disulfide (MoS 2 ) heterogeneous memristive device that can realize tunable stochastic dynamics in its output sampling characteristics. The device can sample exponential-class sigmoidal distributions analogous to the Fermi-Dirac distribution of physical systems with quantitatively defined tunable “temperature” effect. A BM composed of these tunable stochastic neuron devices, which can enable simulated annealing with designed “cooling” strategies, is conducted to solve the MAX-SAT, a representative in NP-hard combinatorial optimization problems. Quantitative insights into the effect of different “cooling” strategies on improving the BM optimization process efficiency are also provided. 

Cyber-threats are continually evolving and growing in numbers and extreme complexities with the increasing connectivity of the Internet of Things (IoT). Existing cyber-defense tools seem not to deter the number of successful cyber-attacks reported worldwide. If defense tools are not seldom, why does the cyber-chase trend favor bad actors? Although cyber-defense tools monitor and try to diffuse intrusion attempts, research shows the required agility speed against evolving threats is way too slow. One of the reasons is that many intrusion detection tools focus on anomaly alerts’ accuracy, assuming that pre-observed attacks and subsequent security patches are adequate. Well, that is not the case. In fact, there is a need for techniques that go beyond intrusion accuracy against specific vulnerabilities to the prediction of cyber-defense performance for improved proactivity. This paper proposes a combination of cyber-attack projection and cyber-defense agility estimation to dynamically but reliably augur intrusion detection performance. Since cyber-security is buffeted with many unknown parameters and rapidly changing trends, we apply a machine learning (ML) based hidden markov model (HMM) to predict intrusion detection agility. HMM is best known for robust prediction of temporal relationships mid noise and training brevity corroborating our high prediction accuracy on three major open-source network intrusion detection systems, namely Zeek, OSSEC, and Suricata. Specifically, we present a novel approach for combined projection, prediction, and cyber-visualization to enable precise agility analysis of cyber defense. We also evaluate the performance of the developed approach using numerical results.