Line-Edge Roughness from Extreme Ultraviolet Lithography to Fin-Field-Effect-Transistor: Computational Study

Although extreme ultraviolet lithography (EUVL) has potential to enable 5-nm half-pitch resolution in semiconductor manufacturing, it faces a number of persistent challenges. Line-edge roughness (LER) is one of critical issues that significantly affect critical dimension (CD) and device performance because LER does not scale along with feature size. For LER creation and impacts, better understanding of EUVL process mechanism and LER impacts on fin-field-effect-transistors (FinFETs) performance is important for the development of new resist materials and transistor structure. In this paper, for causes of LER, a modeling of EUVL processes with 5-nm pattern performance was introduced using Monte Carlo method by describing the stochastic fluctuation of exposure due to photon-shot noise and resist blur. LER impacts on FinFET performance were investigated using a compact device method. Electric potential and drain current with fin-width roughness (FWR) based on LER and line-width roughness (LWR) were fluctuated regularly and quantized as performance degradation of FinFETs.

Distinct effects of heterogeneity and noise on gamma oscillation in a model of neuronal network with different reversal potential

Gamma oscillation is crucial in brain functions such as attentional selection, and is inextricably linked to both heterogeneity and noise (or so-called stochastic fluctuation) in neuronal networks. However, under coexistence of these factors, it has not been clarified how the synaptic reversal potential modulates the entraining of gamma oscillation. Here we show distinct effects of heterogeneity and noise in a population of modified theta neurons randomly coupled via GABAergic synapses. By introducing the Fokker-Planck equation and circular cumulants, we derive a set of two-cumulant macroscopic equations. In bifurcation analyses, we find a stabilizing effect of heterogeneity and a nontrivial effect of noise that results in promoting, diminishing, and shifting the oscillatory region, and is largely dependent on the reversal potential of GABAergic synapses. These findings are verified by numerical simulations of a finite-size neuronal network. Our results reveal that slight changes in reversal potential and magnitude of stochastic fluctuations can lead to immediate control of gamma oscillation, which would results in complex spatio-temporal dynamics for attentional selection and recognition.

Effects of parity, frustration, and stochastic fluctuations on integrated information of consciousness for networks with two small-sized loops

Background: Integrated Information Theory (IIT) has been attracting attention as a theory of consciousness. The latest version, IIT3.0, is still at the stage of accumulating knowledge concerning fundamental networks. This paper presents an evaluation of the system-level integrated conceptual information of a major complex, ΦMax, associated with the center of consciousness for a small-scale network containing two small loops in accordance with the IIT3.0 framework. We focus on the following parameters characterizing the system model: 1) number of nodes in the loop, 2) frustration of the loop, and 3) temperature controlling the stochastic fluctuation of the state transition. Specifically, assuming that the two loops are coupled systems, such as cerebral hemispheres, the effect of these parameters on the values of ΦMax and conditions for major complexes formed by a single loop, rather than the entire network, is investigated.Results: Our first finding is that parity of the number of nodes forming a loop has a strong effect on the integrated conceptual information ΦMax. For loops with an even number of nodes, the number of concepts tends to decrease, and ΦMax becomes smaller. When the loop is formed with an odd number of nodes, the system without frustration and the system with two frustrated loops can have exactly the same ΦMax. It is also shown that, although counterintuitive, the value of ΦMax can be maximized in the presence of stochastic fluctuations. Our second finding is that a major complex is more likely to be formed by a small number of nodes under small stochastic fluctuations. In particular, this tendency is enhanced for larger numbers of nodes constituting a loop. On the other hand, the entire network can easily become a major complex under larger stochastic fluctuations, and this tendency can be reinforced by frustration.Conclusions: Our results indicating that the entire network dominates and maintains a high level of consciousness in the presence of a certain degree of fluctuation and frustration may qualitatively correspond to actual neural behaviors. The results of this study are expected to contribute to the verification of the consistency of IIT with the actual nervous system in the future.

Deterministic Sudden Changes and Stochastic Fluctuation Effects on Stability and Persistence Dynamics of Two-Predator One-Prey Model

In this paper, we present new results on deterministic sudden changes and stochastic fluctuations’ effects on the dynamics of a two-predator one-prey model. We purpose to study the dynamics of the model with some impacting factors as the problem statement. The methodology depends on investigating the seasonality and stochastic terms which make the predator-prey interactions more realistic. A theoretical analysis is introduced for studying the effects of sudden deterministic changes, using three different cases of sudden changes. We show that the system in a good situation presents persistence dynamics only as a stable dynamical behavior. However, the system in a bad situation leads to three main outcomes as follows: first, constancy at the initial conditions of the prey and predators; second, extinction of the whole system; third, extinction of both predators, resulting in the growth of the prey population until it reaches a peak carrying capacity. We perform numerical simulations to study effects of stochastic fluctuations, which show that noise strength leads to an increase in the oscillations in the dynamical behavior and became more complex and finally leads to extinction when the strength of the noise is high. The random noises transfer the dynamical behavior from the equilibrium case to the oscillation case, which describes some unstable environments.

Noise-induced scaling in skull suture interdigitation

Sutures, the thin, soft tissue between skull bones, serve as the major craniofacial growth centers during postnatal development. In a newborn skull, the sutures are straight; however, as the skull develops, the sutures wind dynamically to form an interdigitation pattern. Moreover, the final winding pattern had been shown to have fractal characteristics. Although various molecules involved in suture development have been identified, the mechanism underlying the pattern formation remains unknown. In a previous study, we reproduced the formation of the interdigitation pattern in a mathematical model combining an interface equation and a convolution kernel. However, the generated pattern had a specific characteristic length, and the model was unable to produce a fractal structure with the model. In the present study, we focused on the anterior part of the sagittal suture and formulated a new mathematical model with time–space-dependent noise that was able to generate the fractal structure. We reduced our previous model to represent the linear dynamics of the centerline of the suture tissue and included a time–space-dependent noise term. We showed theoretically that the final pattern from the model follows a scaling law due to the scaling of the dispersion relation in the full model, which we confirmed numerically. Furthermore, we observed experimentally that stochastic fluctuation of the osteogenic signal exists in the developing skull, and found that actual suture patterns followed a scaling law similar to that of the theoretical prediction.

At the Crossroads of Bioenergetics and Antibiotic Discovery

Dr. Vladimir Skulachev was my mentor, and his pioneering work in the field of bioenergetics inspired the discoveries described in this review, written in the form of a personal account of events. Examining basic mechanisms of chemiosmotic coupling unexpectedly led us to transenvelope multidrug resistance pumps (MDR pumps) that severely limit development of novel antibiotics. One of the major advances of Skulachev and his group was the discovery of the mitochondrial membrane potential with the use of permeant cations such as TPP+, which served as electric probes. We describe our finding of their natural counterparts in plants, where they act as antimicrobials. The most challenging problems in antimicrobial drug discovery are antibiotic tolerance of chronic infections caused by dormant persister cells; antibiotic resistance, responsible for the current antimicrobial resistance crisis (AMR); and finding novel compounds acting against Gram-negative bacteria, protected by their powerful multidrug resistance pumps. Our study of persisters shows that these are rare cells formed by stochastic fluctuation in expression of Krebs cycle enzymes, leading to a drop in ATP, target shutdown, and antibiotic tolerance. Searching for compounds that can corrupt targets in the absence of ATP, we identified acyldepsipeptide (ADEP) that activates the ClpP protease, forcing cells to self-digest. Growing previously uncultured bacteria led us to teixobactin, a novel cell wall acting antibiotic. Teixobactin avoids efflux by targeting lipid II and lipid III, precursors of peptidoglycan and wall teichoic acid, located on the surface. The targets are immutable, and teixobactin is the first antibiotic with no detectable resistance. Our search for compounds acting against Gram-negative bacteria led to the discovery of darobactins, which also hit a surface target, the essential chaperone BamA.

Line-Edge Roughness on Fin-Field-Effect-Transistor Performance for 7-nm and 5-nm Patterns

The line-edge roughness (LER) is a critical issue that significantly impacts the critical dimension (CD) because the LER does not scale with the feature size. Hence, the LER influences the device performance with 7-nm and 5-nm patterns. In this study, LER impact on the performance of the fin-field-effect-transistors (FinFETs) are investigated using a compact device method. The fin-width roughness (FWR) is based on the stochastic fluctuation such as the LER and the line-width roughness (LWR) in the lithography process. The calculated results of the FWRs and the gate lengths L = 7-nm and 5-nm are addressed with the cases of electric potentials with the y-direction along the gate length, electric potentials with the x-direction along the fin width, and the absolute drain currents with the gate lengths L = 7-nm or 5-nm due to gate voltages. According to the gate length, the impact of the FWR patterns on the performance of fin-field-effect-transistors (FinFETs) can find regular fluctuations.

Elk population dynamics when carrying capacities vary within and among herds

Population and land management relies on understanding population regulation and growth, which may be impacted by variation in population growth parameters within and among populations. We explored the interactions between variation in carrying capacity (K), intrinsic population growth rate (r), and strength of density dependence (β) within and among elk (Cervus elaphus) herds in a small part of the geographic range of the species. We also estimated stochastic fluctuations in abundance around K for each herd. We fit linear Ricker growth models using Bayesian statistics to seven time series of elk population survey data. Our results indicate that K and β varied among herds, and that r and β varied temporally within herds. We also found that herds with smaller K had less stochastic fluctuation in abundances around K, but higher temporal variation in β within herds. Population regulation and the rate of return to the equilibrium abundance is often understood in terms of β, but ecological populations are dynamic systems, and temporal variation in population growth parameters may also influence regulation. Population models which accommodate variation both within and among herds in population growth parameters are necessary, even in mild climates, to fully understand population dynamics and manage populations.