Physical Insight Research Articles

Physical Insights into THz Rectification in Metal–Oxide–Semiconductor Transistors

Metal–oxide–semiconductor field-effect transistors (MOSFETs) have proven to be effective devices for rectifying electromagnetic radiation at extremely high frequencies, approximately 1 THz. This paper presents a new interpretation of the THz rectification process in the structure of an MOS transistor. The rectification depends on the nonlinear effect of the carrier dynamics. The paper shows that the so-called self-mixing effect occurs within the interface region between the source and the channel. The basic tool used numerical TCAD simulations, which offer a direct interpretation of different aspects of this interaction. The complex, 2D effect is examined in terms of its basic aspects by comparing the MOS structure with a simplified case study structure. We demonstrate that a contribution to the output-rectified voltage detectable at the drain arises from the charging of the drain well capacitance due to the diffusion of excess electrons from the self-mixing interaction occurring at the source barrier. In addition, the paper provides a quantitative description of the rectification process through the definition of the output equivalent circuit, offering a new perspective for the design of detection systems.

A simple one-electron expression for electron rotational factors.

Within the context of fewest-switch surface hopping (FSSH) dynamics, one often wishes to remove the angular component of the derivative coupling between states J and K. In a previous set of papers, Shu et al. [J. Phys. Chem. Lett. 11, 1135-1140 (2020)] posited one approach for such a removal based on direct projection, while we isolated a second approach by constructing and differentiating a rotationally invariant basis. Unfortunately, neither approach was able to demonstrate a one-electron operatorÔ whose matrix element JÔK was the angular component of the derivative coupling. Here, we show that a one-electron operator can, in fact, be constructed efficiently in a semi-local fashion. The present results yield physical insight into designing new surface hopping algorithms and are of immediate use for FSSH calculations.

Modeling of a liquid nitrogen droplet evaporating inside an immiscible liquid pool

Evaporation of liquid nitrogen in another immiscible liquid occurs in many industrial applications. Existing oversimplified one-dimensional (1D) quasi-steady models, although can quantitatively predict the evaporation rate by introducing an empirical fitting parameter, rely on configurations inconsistent with experimental observation so more rigorous models are required to get in-depth physical insights and improve modeling capability. This study proposes a 2D quasi-steady-state theoretical model, free of fitting parameters, that predicts the bubble growth rate and estimates the heat transfer rate for a liquid nitrogen droplet evaporating inside a liquid pool within the spherical bubble regime. The droplet's shape and position within a spherical bubble are determined by the equilibrium between the gravitational force and the upward pressure force resulting from the vapor flow between the droplet and the pool. The vapor layer thickness is calculated to be on the order of 10 microns. Notably, the primary contribution to heat transfer arises from the lower portion of the droplet, leading to local heat flux values up to approximately six times higher at the bottom compared to the top. The predicted bubble growth is quantitatively consistent with experimental data within the capillary spherical bubble regime. Furthermore, the overall heat transfer rate Q exhibits a distinct scaling relationship with the volume ratio between the bubble and droplet, yielding Q∼(Vb/Vd)−0.1.

An investigation on synergistic resonances of membrane-type acoustic metamaterial with multiple masses

Membrane-type acoustic metamaterials (MAM) represent a class of materials that are lightweight, compact, structurally simple, and simultaneously offer flexibility and moldability, all while demonstrating exceptional sound attenuation properties in the low-frequency range. As a continuation, this study focuses on membrane-based acoustic metamaterials with synergistic resonance of multiple loaded mass blocks. By varying the unit shape, arrangement method, radius and material of the loaded mass blocks, the low-frequency sound insulation performance of membrane-based acoustic metamaterials with synergistic resonance of multiple loaded mass blocks was calculated using a numerical model that are experimentally validated. The results indicate that the peak transmiss loss (TL) frequencies can be tuned to specific values. The optimized membrane-type acoustic metamaterial could achieve a TL bandwidth up to 844 Hz (>5dB) and a TL bandwidth up to 376 Hz (>10 dB) within the low-frequency range. Acoustic-membrane analysis further revealed the vibration patterns of the structure under different frequency excitations, providing profound physical insights into the mechanisms of low-frequency sound insulation. Moreover, by altering the relative angles between the loaded mass blocks could further broaden the effective noise reduction bandwidth which can benefit the understanding on the optimized membrane-acoustic interactions.

A Computational Platform for Automatic Signal Processing for Bender Element Sensors

The small strain shear modulus is an important characteristic of geomaterials that can be measured experimentally using piezoelectric sensors (bender elements). However, most conventional signal interpretation techniques are based on the visual observation of the output signal and therefore inherently subjective. Objective techniques also exist, like the cross-correlation of the input and output signals, but they lack physical insight, as they rely on the (incorrect) assumption that input and output signals are similar. This paper presents GeoHyTE, the first objective and physically consistent toolbox for the automatic processing of the output signal of bender element sensors. GeoHyTE updates a finite element model of the experiment, iteratively searching for the small strain shear modulus that maximises the correlation between the experimental and numerical output signals. The method is objective, as the results do not depend on the experience of the user, and physically consistent, as the wave propagation process is modelled in full and signals of the same nature (output) are correlated. Moreover, GeoHyTE is nearly insensitive to grossly erroneous input by the user, both in terms of the starting point of the iterative maximisation process and refinement of the finite element model. The results obtained with GeoHyTE are validated against benchmark measurements reported in the literature and experimental data obtained by the authors. A detailed statistical analysis of the results obtained with GeoHyTE and conventional interpretation techniques is also presented.

Physics mechanisms underlying the optimization of coherent heat transfer across width-modulated nanowaveguides with calculations and machine learning

Optimization of heat transfer at the nanoscale is necessary for efficient modern technology applications in nanoelectronics, energy conversion, and quantum technologies. In such applications, phonons dominate thermal transport and optimal performance requires minimum phonon conduction. Coherent phonon conduction is minimized by maximum disorder in the aperiodic modulation profile of width-modulated nanowaveguides, according to a physics rule. It is minimized for moderate disorder against physics intuition in composite nanostructures. Such counter behaviors call for a better understanding of the optimization of phonon transport in non-uniform nanostructures. We have explored mechanisms underlying the optimization of width-modulated nanowaveguides with calculations and machine learning, and we report on generic behavior. We show that the distribution of the thermal conductance among the aperiodic width-modulation configurations is controlled by the modulation degree irrespective of choices of constituent material, width-modulation-geometry, and composition constraints. The efficiency of Bayesian optimization is evaluated against increasing temperature and sample size. It is found that it decreases with increasing temperature due to thermal broadening of the thermal conductance distribution. It shows weak dependence on temperature in samples with high discreteness in the distribution spectrum. Our work provides new physics insight and indicates research pathways to optimize heat transfer in non-uniform nanostructures.

A fatigue test based on inclined loading block concept to benchmark delamination growth considering loading history and [formula omitted]-curve effect

The main objective of this paper is to present a delamination benchmark test concept for composite materials that develop non-self-similar delamination in characterization specimens. The non-self-similar delamination is induced by rotating the loading blocks. The simplicity of the test allows for analyzing the loading mode history by concatenating different loading conditions, such as static and fatigue loading, under multiple loading modes. The methodology introduced in this paper can be particularized for any given composite material set and any sequence of loading conditions. To demonstrate the capabilities of the benchmark test, a case study is presented using AS4D/PEKK-FC thermoplastic composite material, which exhibits strong R-curve behavior. A sequence of opening and shear failure modes was applied under static and fatigue loading, providing an experimental data set that is ready to be used as a part of the validation of numerical predictive delamination models. The delamination process was monitored by X-ray radiography, and the final fracture surfaces were analyzed with scanning electron microscopy (SEM), giving a physical insight into the contribution of the fracture mechanisms to the delamination process.

Opening the Black Box: Towards inherently interpretable energy data imputation models using building physics insight

Missing data are frequently observed by practitioners and researchers in the building energy modeling community. In this regard, advanced data-driven solutions, such as Deep Learning methods, are typically required to reflect the non-linear behavior of these anomalies. As an ongoing research question related to Deep Learning, a model's applicability to limited data settings can be explored by introducing prior knowledge in the network. This same strategy can also lead to more interpretable predictions, hence facilitating the field application of the approach. For that purpose, the aim of this paper is to propose the use of Physics-informed Denoising Autoencoders (PI-DAE) for missing data imputation in commercial buildings. In particular, the presented method enforces physics-inspired soft constraints to the loss function of a Denoising Autoencoder (DAE). In order to quantify the benefits of the physical component, an ablation study between different DAE configurations is conducted. First, three univariate DAEs are optimized separately on indoor air temperature, heating, and cooling data. Then, two multivariate DAEs are derived from the previous configurations. Eventually, a building thermal balance equation is coupled to the last multivariate configuration to obtain PI-DAE. Additionally, two commonly used benchmarks are employed to support the findings. It is shown how introducing physical knowledge in a multivariate Denoising Autoencoder can enhance the inherent model interpretability through the optimized physics-based coefficients. While no significant improvement is observed in terms of reconstruction error with the proposed PI-DAE, its enhanced robustness to varying rates of missing data and the valuable insights derived from the physics-based coefficients create opportunities for wider applications within building systems and the built environment.

Wrinkling–dewrinkling transitions in stretched soft spherical shells

Soft shells undergoing intricate buckling and morphological evolutions can serve as a model system for understanding the morphogenesis of organs, tissues, cells, and nuclei. In this paper, we combine experiments, simulations, and theoretical analysis to investigate the wrinkling and subsequent morphological transitions in a soft spherical shell subjected to an outward concentrated force. During loading, the spherical shell first buckles into many shallow radial wrinkles, which soon merge into a single crater-like deep wrinkle, and then the number of wrinkles increases with loading. Surprisingly, after a critical point, all wrinkles disappear and the shell regains an axisymmetric, smooth shape, referred to as dewrinkling. We show how this anomalous wrinkling–dewrinkling transition stems from the interplay between surface curvature and large deformations. Material-independent scaling laws are established from an energy analysis to predict the surface wrinkling pattern, which depends on the loading force. This work provides physical insights into how local forces can regulate the shape evolutions of soft shells, which can take place in, for example, the morphogenesis of developing organisms.

Carrier injection induced degradation of nitrogen passivated SiC–SiO2 interface simulated by time-dependent density functional theory

The performance of SiC-based metal-oxide-semiconductor field-effect transistors (MOSFETs) degrades seriously after a period of continuous operation. To directly understand this issue, we conduct real-time time-dependent density functional theory (TDDFT) simulations on a series of nitrogen passivated SiC–SiO2 interfaces to monitor the interaction between carriers and interface atoms. We find that the nitrogen passivation always leaves behind two local states near the VBM, which gives a chance to the strong interaction between channel carriers and C–N bonds, and finally results in the generation of C dangling bond defects. These processes are vividly presented and confirmed by the TDDFT simulation. Additionally, the results show that the new defects are more easily formed by the passivated C cluster than the passivated Si vacancy. These studies provide physical insights into the degradation mechanisms of working SiC MOSFETs, while simultaneously demonstrating the advantage of TDDFT as a crucial tool for investigating defect generation dynamics in semiconductor devices.

Deep learning insights into cosmological structure formation

The evolution of linear initial conditions present in the early Universe into extended halos of dark matter at late times can be computed using cosmological simulations. However, a theoretical understanding of this complex process remains elusive; in particular, the role of anisotropic information in the initial conditions in establishing the final mass of dark matter halos remains a long-standing puzzle. Here, we build a deep learning framework to investigate this question. We train a three-dimensional convolutional neural network to predict the mass of dark matter halos from the initial conditions, and quantify in full generality the amounts of information in the isotropic and anisotropic aspects of the initial density field about final halo masses. We find that anisotropies add a small, albeit statistically significant amount of information over that contained within spherical averages of the density field about final halo mass. However, the overall scatter in the final mass predictions does not change qualitatively with this additional information, only decreasing from 0.9 dex to 0.7 dex. Given such a small improvement, our results demonstrate that isotropic aspects of the initial density field essentially saturate the relevant information about final halo mass. Therefore, instead of searching for information directly encoded in initial conditions anisotropies, a more promising route to accurate, fast halo mass predictions is to add approximate dynamical information based e.g. on perturbation theory. More broadly, our results indicate that deep learning frameworks can provide a powerful tool for extracting physical insight into cosmological structure formation. Published by the American Physical Society 2024

Large-scale kinetic simulations of colliding plasmas within a hohlraum of indirect-drive inertial confinement fusion.

The National Ignition Facility has recently achieved successful burning plasma and ignition using the inertial confinement fusion (ICF) approach. However, there are still many fundamental physics phenomena that are not well understood, including the kinetic processes in the hohlraum. Shan etal. [Phys. Rev. Lett. 120, 195001 (2018)0031-900710.1103/PhysRevLett.120.195001] utilized the energy spectra of neutrons to investigate the kinetic colliding plasma in a hohlraum of indirect drive ICF. However, due to the typical large spatial-temporal scales, this experiment could not be well simulated by using available codes at that time. Utilizing our advanced high-order implicit PIC code, LAPINS, we were able to successfully reproduce the experiment on a large scale of both spatial and temporal dimensions, in which the original computational scale was increased by approximately seven to eight orders of magnitude. Not only is the validity of the explanation of the experiment confirmed by our simulations, i.e., the abnormally large width of neutron spectra comes from beam-target nuclear fusions, but also a different physical insight into the source of energetic deuterium ions is provided. The acceleration of deuterium ions can be categorized into two components: one is propelled by a sheath electric field created by the charge separation at the onset, while the other is a result of the reflection of the potential of the shock wave. The robustness of the acceleration mechanism is analyzed with varying initial conditions, e.g., temperatures, drifting velocity, and ion components. This paper might serve as a reference for benchmark simulations of upcoming simulation codes and may be relevant for future research on mixtures and entropy increments at plasma interfaces.

Mixed convection flow in a square lid-driven cavity subject to inclined magnetic field with highly accurate wavelet-homotopy solutions

Numerical studies of flow in square enclosures attain great interest due to their wide applications in engineering, such as in the meteorology, navigation, mechanology and mining, etc. In this paper, the solutions are obtained to the square lid driven cavity describing classical mixed convection in the presence of an inclined magnetic field, chemical radiation and heat absorption/generation. The problem is mathematically reduced into Navier-Stokes equations. The Coiflet wavelet homotopy approach, a newly suggested computational technique, is then used to solve these nonlinear partial differential equations with their non-homogeneous boundary conditions. The numerical solutions for streamlines, isotherms, iso-concentrations contours, Nusselt number are carefully determined. The solutions so obtained are analyzed through various plots to demonstrate the effects of several physical parameters such as Reynolds numbers (Re), Richardson number (Ri), Grashof number (Gr), Hartman number (Ha), thermal radiation parameter (NR), chemical reaction coefficient (K), heat generation/absorption coefficient (Qh), the angle of inclination of magnetic field (γ), slipping parameter (λ) and Schmidt number (Sc) and their physical insights are also reported. Rigid comparison of with previous studies shows excellent computational effectiveness of newly proposed method. Additionally, according to the results, our recently proposed approach has superior characteristics and strong nonlinear processing capabilities, which render it more appropriate than the Coiflet wavelet analysis method and the traditional homotopy analysis method for tackling complex nonlinear problems.

Impurities in Polymer-Lined Autoclaves Affect Zeolite Synthesis and Si Incorporation Behavior.

Polymer-lined autoclaves are commonly believed to be highly durable and inert in hydrothermal reactions. Herein, we use the hydrothermal synthesis of AlPO-18 zeolite as a case study to demonstrate that the choice of autoclave materials (polytetrafluoroethylene or para-polyphenylene) does significantly affect the product of zeolite synthesis. A small amount of glass fiber in the PPL-lined autoclave unexpectedly functions as a source of silicon and yields SAPO-34 instead of AlPO-18 as the product. The outcomes of 19 successive experiments conducted with a single PPL-lined autoclave exhibit significant variations, further highlighting that the impurities arising from the autoclaves should be considered during the hydrothermal synthesis procedure. In contrast to SAPO-34 synthesized by the conventional method, which displays only Si(4Al) at a low Si/Al ratio, SAPO-34 synthesized in the PPL-lined autoclave exhibits multiple silicon coordination environments. This outcome provides new physical insights into the silicon incorporation mechanism and proposes a viable strategy for regulating the silicon coordination environment at low Si/Al ratios.

Explainable AI for optimizing oxygen reduction on Pt monolayer core–shell catalysts

AbstractAs a subfield of artificial intelligence (AI), machine learning (ML) has emerged as a versatile tool in accelerating catalytic materials discovery because of its ability to find complex patterns in high‐dimensional data. While the intricacy of cutting‐edge ML models, such as deep learning, makes them powerful, it also renders decision‐making processes challenging to explain. Recent advances in explainable AI technologies, which aim to make the inner workings of ML models understandable to humans, have considerably increased our capacity to gain insights from data. In this study, taking the oxygen reduction reaction (ORR) on {111}‐oriented Pt monolayer core–shell catalysts as an example, we show how the recently developed theory‐infused neural network (TinNet) algorithm enables a rapid search for optimal site motifs with the chemisorption energy of hydroxyl (OH) as a single descriptor, revealing the underlying physical factors that govern the variations in site reactivity. By exploring a broad design space of Pt monolayer core–shell alloys ( candidates) that were generated from thermodynamically stable bulk structures in existing material databases, we identified novel alloy systems along with previously known catalysts in the goldilocks zone of reactivity properties. SHAP (SHapley Additive exPlanations) analysis reveals the important role of adsorbate resonance energies that originate from ‐band interactions in chemical bonding at metal surfaces. Extracting physical insights into surface reactivity with explainable AI opens up new design pathways for optimizing catalytic performance beyond active sites.

An entropic theory of homogeneous ice nucleation in non-ionic aqueous solutions.

The nucleation of ice from aqueous solutions is a process essential to myriad environmental and industrial processes, but the physical factors affecting the capacity of different solutes to depress the homogeneous nucleation temperature of ice are yet poorly understood. In this work, we demonstrate that for many binary aqueous solutions of non-ionic solutes, this depression is dominated by the entropy of the liquid phase. Employing the classic Turnbull interpretation of the interfacial free energy γ∼TSliquid-Ssolid and estimating solution entropies with a Flory-style modification of the ideal entropy of mixing that accounts for solute size effects, we demonstrate that mixing entropy alone predicts experimental homogeneous nucleation temperatures across a wide variety of non-ionic solutions. We anticipate that this physical insight will not only enhance a fundamental understanding of homogeneous nucleation processes across fields but also open new avenues to the rational design of aqueous solutions for desired nucleation behaviors.

Theoretical investigation of multiplexed quantum light source in fiber ring cavities with oscillating dispersion

Correlated photon pairs are the core resources for photonic quantum information technology. Multiplexed quantum light sources can appreciably improve the efficiency of quantum information processing. Here, we develop a coupled-mode mean-field model and its corresponding semiclassical theory analysis to investigate the multiple parametric-gain sidebands in dispersion-oscillation fiber-ring cavities. We present a cycle transfer matrix method to give the analytic expressions for parametric gain. From this, we can reach a physical insight, that is additional degree of freedom introduced by the modulation along fiber length is responsible for the multiple parametric-gain sidebands, where the underlying mechanism is the quasi-phase matching of four-wave mixing. Two twin signal and idler photons generated from parametric-gain sidebands are appreciably of frequency and temporal correlation, revealing promising applications in frequency-multiplexed photon pair sources. When we construct an optical cavity with spliced single mode fiber and dispersion shifted fibers, numerous parametric-gain sidebands grow in the vicinity of zero group-velocity-dispersion wavelengths of two fibers, i.e., 1310 nm and 1550 nm, both corresponding to low transmission loss windows of fibers. Therefore, frequency-multiplexed quantum light sources established on dispersion-oscillation fiber-ring cavities have the advantages in economy, remote transmission, scalability and compatibility with fiber communication network. Our work offers inspiration for the development of a multiplexed quantum light source.

A theoretical framework to investigate the effect ofhighpermittivity materials in MRI using anatomy-mimicking cylinders.

Recent numerical and empirical results proved that high permittivity materials (HPM) used in pads placed near the subject or directly integrated with coils can increase the SNR and reduce the specific absorption rate (SAR) in MRI. In this paper, we propose an analytical investigation of the effect on the magnetic field distribution of a layer of HPM surrounding an anatomy-mimicking cylindrical sample. The study is based on a reformulation of the Mie scattering for cylindrical geometry, following an approach recently introduced for spherical samples. The total field in each medium is decomposed in terms of inward and outward electromagnetic waves, and the fields are expressed as series of cylindrical harmonics, whose coefficients can be interpreted as classical reflection and transmission coefficients. Our new formulation allows a quantitative evaluation of the effect of the HPM layer for varying permittivity and thickness, and it provides an intuitive understanding of such effect in terms of propagation and scattering of the RF field. We show how HPM can filter out the modes that only contribute to the noise or RF power deposition, resulting in higher SNR or lower SAR, respectively. Our proposed framework provides physical insight on how to properly design HPM for MRI applications.

Machine Learning in Membrane Design: From Property Prediction to AI-Guided Optimization.

Porous membranes, either polymeric or two-dimensional materials, have been extensively studied because of their outstanding performance in many applications such as water filtration. Recently, inspired by the significant success of machine learning (ML) in many areas of scientific discovery, researchers have started to tackle the problem in the field of membrane design using data-driven ML tools. In this Mini Review, we summarize research efforts on three types of applications of machine learning in membrane design, including (1) membrane property prediction using ML, (2) gaining physical insight and drawing quantitative relationships between membrane properties and performance using explainable artificial intelligence, and (3) ML-guided design, optimization, or virtual screening of membranes. On top of the review of previous research, we discuss the challenges associated with applying ML for membrane design and potential future directions.

Physical insights into trapping effects on vertical GaN-on-Si trench MOSFETs from TCAD

Vertical GaN power MOSFET is a novel technology that offers great potential for power switching applications. Being still in an early development phase, vertical GaN devices are yet to be fully optimized and require careful studies to foster their development. In this work, we report on the physical insights into device performance improvements obtained during the development of vertical GaN-on-Si trench MOSFETs (TMOS’s) provided by TCAD simulations, enhancing the dependability of the adopted process optimization approaches. Specifically, two different TMOS devices are compared in terms of transfer-curve hysteresis (H) and subthreshold slope (SS), showing a ≈ 75% H reduction along with a ≈ 30% SS decrease. Simulations allow attributing the achieved improvements to a decrease in the border and interface traps, respectively. A sensitivity analysis is also carried out, allowing to quantify the additional trap density reduction required to minimize both figures of merit.