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When the devices are scaled to decanano dimensions the discreteness of charge and matter introduces significant 'intrinsic' parameter fluctuations. Atomic level 3D process and device modelling on statistical scale is required to understand the effects, the scale of the fluctuations and to design fluctuation resistant devices.
In this paper we use 3D simulations to study the amplitudes of random telegraph signals (RTS) associated with the trapping of a single carrier in interface states in the channel of sub 100 nm (decanano) MOSFETs. Both simulations using continuous doping charge and random discrete dopants in the active region of the MOSFETs are presented. We have studied the dependence of the RTS amplitudes on the position of the trapped charge in the channel and on the device design parameters. We have observed a significant increase in the maximum RTS amplitude when discrete random dopants are employed in the simulations.
The main objective of this paper is to determine mechanisms of crack propagation in a nickel single crystal. Motivation for selecting nickel as a case study is because we believe that its physical properties are very close to that of nickel-base super alloy. We are directed in identifying some generic trends that would lead a single crystalline material to failure. We believe that the results obtained here would be of interest to the experimentalists in guiding them to a more optimized experimental strategy. The dynamic crack propagation experiments are very difficult to do. We are partially motivated to fill the gap by generating the simulation results in lieu of the experimental ones for the cases where experiment can not be done or when the data is not available.
We report here on the predicted impact of species such as ice-water, CO2, CH4, and NH3, on oxidized titanium, as well as HC species on diamond surfaces. These simulations provide the dynamics of product distributions during and after a hypervelocity impact event, ionization fractions, and dissociation probabilities for the various species of interest as a function of impact velocity (energy). We are using these results to determine the relevance of the fragmentation process to Cassini INMS results, and to quantify its effects on the observed spectra.
Compared to DNA with simple DSBs, the complex lesions can enhance the hydrogen bonds opening rate at the DNA terminus, and increase the mobility of the whole duplex. Binding of Ku drastically reduces the structural disruption and flexibility caused by the complex lesions. In all complex DSBs systems, the binding of DSB terminus with Ku70 is softened while the binding of the middle duplex with Ku80 is tightened. Binding of Ku promotes the rigidity of DNA duplexes, due to the clamp structure of the inner surface of the rings of Ku70/80.
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The use of a recently developed machine learning (ML) interatomic potential for molecular dynamics simulations of aluminum melt properties will be presented. Such properties are critical for process modeling in additive manufacturing, including the melt pool size, solidification, and formation of solidification microstructures. Direct first-principles modeling of these processes is computationally prohibitive whereas simulations employing ML potentials combine the high accuracy of quantum-mechanical methods with high computational speeds. The physically-informed neural network (PINN) method used herein, integrates a high-dimensional regression implemented by an artificial neural network with a physics-based bond-order interatomic potential. PINN potentials can accurately reproduce many properties of aluminum in both crystalline-solid and liquid phases. We examine the accuracy of a PINN Al potential in predicting the density, self-diffusivity, viscosity, and the tension of the liquid surface and liquid-solid interfaces. Comparison with experimental data and ab initio molecular dynamics calculations shows very good agreement for all properties tested.
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The striking variation in damage tolerance among refractory complex concentrated alloys is examined through the analysis of atomistic fracture simulations, contrasting behavior in elemental Nb with that in brittle NbMoTaW and ductile Nb45Ta25Ti15Hf15. We employ machine-learning interatomic potentials (MLIPs), including a new MLIP developed for NbTaTiHf, in atomistic simulations of crack tip extension mechanisms based on analyses of atomistic fracture resistance curves. While the initial behavior of sharp cracks shows good correspondence with the Rice theory, fracture resistance curves reveal marked changes in fracture modes for the complex alloys as crack extension proceeds. In NbMoTaW, compositional complexity appears to promote dislocation nucleation relative to pure Nb, despite theoretical predictions that the alloy should be relatively more brittle. In Nb45Ta25Ti15Hf15, alloying alters the fracture mode compared to elemental Nb, promoting crack tip blunting and enhancing resistance to crack propagation.
This paper has two goals related to the development of a physically-grounded methodology for modeling the initial stages of fatigue crack growth in an aluminum alloy. The aluminum alloy, AA 7075-T651, is susceptible to fatigue cracking that nucleates from cracked second phase iron-bearing particles. Thus, the first goal of the paper is to validate an existing framework for the prediction of the conditions under which the particles crack. The observed statistics of particle cracking (defined as incubation for this alloy) must be accurately predicted to simulate the stochastic nature of microstructurally small fatigue crack (MSFC) formation. Also, only by simulating incubation of damage in a statistically accurate manner can subsequent stages of crack growth be accurately predicted. To maintain fidelity and computational efficiency, a filtering procedure was developed to eliminate particles that were unlikely to crack. The particle filter considers the distributions of particle sizes and shapes, grain texture, and the configuration of the surrounding grains. This filter helps substantially reduce the number of particles that need to be included in the microstructural models and forms the basis of the future work on the subsequent stages of MSFC, crack nucleation and microstructurally small crack propagation. A physics-based approach to simulating fracture should ultimately begin at nanometer length scale, in which atomistic simulation is used to predict the fundamental damage mechanisms of MSFC. These mechanisms include dislocation formation and interaction, interstitial void formation, and atomic diffusion. However, atomistic simulations quickly become computationally intractable as the system size increases, especially when directly linking to the already large microstructural models. Therefore, the second goal of this paper is to propose a method that will incorporate atomistic simulation and small-scale experimental characterization into the existing multiscale framework. At the microscale, the nanoscale mechanics are represented within cohesive zones where appropriate, i.e. where the mechanics observed at the nanoscale can be represented as occurring on a plane such as at grain boundaries or slip planes at a crack front. Important advancements that are yet to be made include: 1. an increased fidelity in cohesive zone modeling; 2. a means to understand how atomistic simulation scales with time; 3. a new experimental methodology for generating empirical models for CZMs and emerging materials; and 4. a validation of simulations of the damage processes at the nano-micro scale. With ever-increasing computer power, the long-term ability to employ atomistic simulation for the prognosis of structural components will not be limited by computation power, but by our lack of knowledge in incorporating atomistic models into simulations of MSFC into a multiscale framework.
Fracture processes within a material begin at the nanometer length scale at which the formation, propagation, and interaction of fundamental damage mechanisms occur. Physics-based modeling of these atomic processes quickly becomes computationally intractable as the system size increases. Thus, a multiscale modeling method, based on the aggregation of fundamental damage processes occurring at the nanoscale within a cohesive zone model, is under development and will enable computationally feasible and physically meaningful microscale fracture simulation in polycrystalline metals. This method employs atomistic simulation to provide an optimization loop with an initial prediction of a cohesive zone model (CZM). This initial CZM is then applied at the crack front region within a finite element model. The optimization procedure iterates upon the CZM until the finite element model acceptably reproduces the near-crack-front displacement fields obtained from experimental observation. With this approach, a comparison can be made between the original CZM predicted by atomistic simulation and the converged CZM that is based on experimental observation. Comparison of the two CZMs gives insight into how atomistic simulation scales.
3D 'atomistic' simulations are used to study random dopant related threshold voltage fluctuations in 50 nm MOSFETs. Comparisons are made between conventionally doped transistors and transistors with thin epitaxial silicon layers on heavily doped silicon. Issues related to both the optimum threshold voltage control and the suppression of the threshold voltage dispersion are addressed.
We have introduced in a simple and efficient manner quantum mechanical corrections in our 3D 'atomistic' MOSFET simulator using the density gradient formalism. We have studied in comparison with classical simulations the effect of the quantum mechanical corrections on the simulation of random dopant induced threshold voltage fluctuations, the effect of the single charge trapping on interface states and the effect of the oxide thickness fluctuations in decanano MOSFETs with ultrathin gate oxides. The introduction of quantum corrections enhances the threshold voltage fluctuations but does not affect significantly the amplitude of the random telegraph noise associated with single carrier trapping. The importance of the quantum corrections for proper simulation of oxide thickness fluctuation effects has also been demonstrated.
For this work, atomistic molecular dynamics (MD) simulations coupled with potential of mean force (PMF) calculations were employed to investigate the interactions between PVC polymer chains containing 6–20 repeating units and various plasticizers, with the goal of identifying potential replacements for the toxic plasticizer di(2-ethylhexyl)phthalate (DEHP) used in blood bags. The selected plasticizers belong to various chemical families, such as orthophthalates, citrates, adipates, and the terephthalate DEHT. Both the polymer and the plasticizers lack ionizable groups, and their interactions are primarily governed by van der Waals and electrostatic forces. A model correlating PMF profiles with interaction forces was developed and validated across polyvinyl chloride (PVC) polymers of different lengths and all investigated plasticizers. This model provides insight into how structural variations in plasticizers influence their respective PMF values. The study ranks the investigated plasticizers based on binding affinity, identifying TOTM (tris(2-ethylhexyl) trimellitate, TEHTM), BTHC (butyryl trihexyl citrate, Citroflex B-6), ATHC (acetyl trihexyl citrate, Citroflex A-6, CA-6), and DEHT (bis(2-ethylhexyl)terephthalate, DOTP/DEHTP) as promising alternatives for further investigation. This comprehensive study encompasses PVC polymers of four different lengths and 14 plasticizers, with all data averaged over 30 independent simulations. The approach provides a deeper understanding of molecular interactions, enabling the tailoring of polymer–plasticizer systems for diverse applications, including extractables and leachables, with relevance spanning materials science to biomedical engineering, and also serves as a basis for developing coarse-grained simulation protocols. Overall, this study provides valuable insights for designing safer and more efficient plasticizer substitutes and is well supported by other studies.
Although atomistic simulations have contributed significantly to our understanding of twin boundary structure and migration in metals and alloys with hexagonal close packed (HCP) crystal structures, few direct atomistic studies of twinning have been conducted for other types of low symmetry materials, in large part due to a lack of reliable interatomic potentials. In this work, we examine twin boundary structure and migration in a tetragonal material, β-Sn, comparing high resolution Transmission Electron Microscopy (TEM) images of deformation twins in β-Sn to the results of direct atomistic simulations using multiple interatomic potentials. ML-based potentials developed in this work are found to give results consistent with our experimental data, revealing faceted twin boundary structures formed by the nucleation and motion of twinning disconnections. We use bicrystallographic methods in combination with atomistic simulations to analyze the structure, energy and shear coupled migration of observed twin facets in β-Sn. In analogy to Prismatic-Basal (PB/BP) interfaces in HCP metals, we discover low energy asymmetric Prismatic-A-plane (PA/AP) interfaces important to twin growth in β-Sn. Finally, a Moment Tensor Potential (MTP) and Rapid Artificial Neural Network (RANN) interatomic potential suitable for studying twinning and phase transformations in Sn are made publicly available as part of this work.
A detailed three-dimensional (3-D) statistical 'atomistic' simulation study of fluctuation-resistant sub-0.1-(micron)meter MOSFET architectures with epitaxial channels and delta doping is presented. The need for enhancing the fluctuation resistance of the sub-0.1-(micron)meter generation transistors is highlighted by presenting summarized results from atomistic simulations of a wide range of conventional devices with uniformly doped channel. According to our atomistic results, the doping concentration dependence of the random dopant-induced threshold voltage fluctuations in conventional devices is stronger than the analytically predicted fourth-root dependence. As a result of this, the scaling of such devices will be restricted by the "intrinsic" random dopant-induced fluctuations earlier than anticipated. Our atomistic simulations confirm that the introduction of a thin epitaxial layer in the MOSFET's channel can efficiently suppress the random dopant-induced threshold voltage fluctuations in sub-0.1-(micron)meter devices. For the first time, we observe an "anomalous" reduction in the threshold voltage fluctuations with an increase in the doping concentration behind the epitaxial channel, which we attribute to screening effects. Also, for the first time we study the effect of a delta-doping, positioned behind the epitaxial layer, on the intrinsic threshold voltage fluctuations. Above a certain thickness of epitaxial layer, we observe a pronounced anomalous decrease in the threshold voltage fluctuation with the increase of the delta doping. This phenomenon, which is also associated with screening, enhances the importance of the delta doping in the design of properly scaled fluctuation-resistant sub-0.1-(micron)meter MOSFET's. Index Terms-Doping, fluctuations, MOSFET, semiconductor device simulation, silicon devices, threshold.