Simulation Framework Research Articles

Robust self-supervised denoising of voltage imaging data using CellMincer

Voltage imaging is a powerful technique for studying neuronal activity, but its effectiveness is often constrained by low signal-to-noise ratios (SNR). Traditional denoising methods, such as matrix factorization, impose rigid assumptions about noise and signal structures, while existing deep learning approaches fail to fully capture the rapid dynamics and complex dependencies inherent in voltage imaging data. Here, we introduce CellMincer, a novel self-supervised deep learning method specifically developed for denoising voltage imaging datasets. CellMincer operates by masking and predicting sparse pixel sets across short temporal windows and conditions the denoiser on precomputed spatiotemporal auto-correlations to effectively model long-range dependencies without large temporal contexts. We developed and utilized a physics-based simulation framework to generate realistic synthetic datasets, enabling rigorous hyperparameter optimization and ablation studies. This approach highlighted the critical role of conditioning on spatiotemporal auto-correlations, resulting in an additional 3-fold SNR gain. Comprehensive benchmarking on both simulated and real datasets, including those validated with patch-clamp electrophysiology (EP), demonstrates CellMincer’s state-of-the-art performance, with substantial noise reduction across the frequency spectrum, enhanced subthreshold event detection, and high-fidelity recovery of EP signals. CellMincer consistently outperforms existing methods in SNR gain (0.5–2.9 dB) and reduces SNR variability by 17–55%. Incorporating CellMincer into standard workflows significantly improves neuronal segmentation, peak detection, and functional phenotype identification, consistently surpassing current methods in both SNR gain and consistency.

Simulating atherosclerotic plaque mechanics using polyvinyl alcohol (PVA) cryogel artery phantoms, ultrasound imaging and inverse finite element analysis.

The aim of this study is to demonstrate the potential of polyvinyl alcohol phantoms to simulate atherosclerotic features. In addition, a testing and simulation framework is developed for full PVA vessel material characterization using ring tensile testing and inflation testing combined with non-invasive ultrasound imaging and computational modelling. Strain stiffening behavior was observed in PVA through ring tensile tests, particularly at high (n=6) freeze-thaw cycles. Inflation testing of bi-layered phantoms featuring lipid pool inclusions demonstrated high strains at shoulder regions. The application of an inverse finite element framework successfully recovered boundaries and determined the shear moduli for the PVA wall to lie within the range 27 kPa to 53 kPa. The imaging-modelling framework presented facilitates the use and characterization of arterial mimicking phantoms to further explore plaque rupture. It also shows translational potential for non-invasive mechanical characterization of atherosclerotic plaques to improve the identification of clinically relevant metrics of plaque vulnerability.

Research on deformation prediction of CFETR multi-purpose overloaded robot based on real-time structural simulator

The CFETR Multi-Purpose Overload Robot (CMOR) is a key subsystem of the China Fusion Engineering Test Reactor (CFETR), which can perform maintenance tasks of the internal components. However, the large slenderness ratio of the structure results in low control accuracy of the CMOR end-effector. This paper proposes a CMOR deformation prediction method based on the real-time structural simulator. The overall deformation model of CMOR is analyzed based on the single joint deformation mechanism. Based on the principle of layered control, the control framework of the CMOR structural simulator is constructed, and the deformation data of CMOR in different positions are calculated based on the finite element method. A hybrid neural network containing a multilayer perceptron, transformer, and attention mechanism is designed to train the CMOR deformation prediction model. The training results show that the deformation prediction model converges quickly and fits the deformation of the CMOR structure well with small prediction errors. Finally, the real-time structure simulator is developed based on the deformation prediction model, and the deformation of CMOR is reconstructed with the update frequency of 2 Hz and the absolute error at any point within ±10 mm, which verifies the correctness of the CMOR structural deformation prediction method.

Simulating hydrogen-controlled crack growth kinetics in Al-alloys using a coupled chemo-mechanical phase-field damage model

Environmentally Assisted Cracking (EAC) of 7xxx series aluminium alloys involves interactions between multiple physical phenomena, which ultimately influence the in-service life of critical components. In this work, we present a new model to study EAC in 7xxx series alloys, which is implemented in the multiphysics simulation framework, DAMASK. The chemo-mechanical model couples crack tip hydrogen generation, resulting from surface oxidation, and transport, with crystal-plasticity-governed intergranular crack propagation, through the microstructural trapping of hydrogen at dislocations, grain boundaries (GB), and crack tip stress fields. Large-scale simulations with realistic grain structures have been performed to provide novel insight into the dominant rate-controlling processes associated with intergranular EAC in 7xxx series aluminium alloys. The model was able to reproduce experimentally measured crack velocities under different loading conditions. Parametric studies indicate that, in addition to the GB network morphology, the crack growth rate was controlled by hydrogen generation at the crack tip with long-range diffusion having negligible influence. Additionally, the total hydrogen generated through crack tip oxidation appears to be more significant than the peak generation rate.

ECG analysis of ventricular fibrillation dynamics reflects ischaemic progression subject to variability in patient anatomy and electrode location

BackgroundVentricular fibrillation (VF) is the deadliest arrhythmia, often caused by myocardial ischaemia. VF patients require urgent intervention planned quickly and non-invasively. However, the accuracy with which electrocardiographic (ECG) markers reflect the underlying arrhythmic substrate is unknown.MethodsWe analysed how ECG metrics reflect the fibrillatory dynamics of electrical excitation and ischaemic substrate. For this, we developed a human-based computational modelling and simulation framework for the quantification of ECG metrics, namely, frequency, slope, and amplitude spectrum area (AMSA) during VF in acute ischaemia for several electrode configurations. Simulations reproduced experimental and clinical findings in 21 scenarios presenting variability in the location and transmural extent of regional ischaemia, and severity of ischaemia in the remote myocardium secondary to VF.ResultsRegional acute myocardial ischaemia facilitated re-entries, potentially breaking up into VF. Ischaemia in the remote myocardium modulated fibrillation dynamics. Cases presenting a mildly ischaemic remote myocardium yielded sustained VF, enabled by the high proliferation of phase singularities (PS, 11–22) causing remarkably disorganised activation patterns. Conversely, global acute ischaemia induced stable rotors (3–12 PS). Changes in frequency and morphology of the ECG during VF reproduced clinical findings but did not show a direct correlation with the underlying wave dynamics. AMSA allowed the precise stratification of VF according to ischaemic severity in the remote myocardium (healthy: 23.62–24.45 mV Hz; mild ischaemia: 10.58–21.47 mV Hz; moderate ischaemia: 4.82–11.12 mV Hz). Within the context of clinical reference values, apex-anterior and apex-posterior electrode configurations were the most discriminatory in stratifying VF based on the underlying ischaemic substrate.ConclusionThis in silico study provides further insights into non-invasive patient-specific strategies for assessing acute ventricular arrhythmias. The use of reliable ECG markers to characterise VF is critical for developing tailored resuscitation strategies.

Supernova electron-neutrino interactions with xenon in the nEXO detector

Electron-neutrino charged-current interactions with xenon nuclei were modeled in the nEXO neutrinoless double-β decay detector (∼5 metric ton, 90% Xe136, 10% Xe134) to evaluate its sensitivity to supernova neutrinos. Predictions for event rates and detectable signatures were modeled using the Model of Argon Reaction Low Energy Yields (MARLEY) event generator. We find good agreement between MARLEY’s predictions and existing theoretical calculations of the inclusive cross sections at supernova neutrino energies. The interactions modeled by MARLEY were simulated within the nEXO simulation framework and were run through an example reconstruction algorithm to determine the detector’s efficiency for reconstructing these events. The simulated data, incorporating the detector response, were used to study the ability of nEXO to reconstruct the incident electron-neutrino spectrum and these results were extended to a larger xenon detector of the same isotope enrichment. We estimate that nEXO will be able to observe electron-neutrino interactions with xenon from supernovae as far as 5–8 kpc from Earth, while the ability to reconstruct incident electron-neutrino spectrum parameters from observed interactions in nEXO is limited to closer supernovae. Published by the American Physical Society 2024

A comparative analysis of car fleet efficiency evolution in Europe and Australia insights on policy influence

Regulators worldwide introduce measures to improve energy consumption and reduce greenhouse emissions of road vehicles. The present study focuses on the impacts of passenger car carbon dioxide (CO2) and fuel efficiency measures, attempting to evaluate their effectiveness using vehicle and fleet modelling. To obtain a robust counterfactual result, two regions are compared as a case study: the European Union (EU), where CO2 emissions have been regulated for over fifteen years, and Australia, a region that recently introduced such a policy element. The average difference in the certified CO2 emissions of new cars registered in the two regions increased from 50 g/km in 2018 to 60 g/km in 2021 due to accelerated electrification in the EU. To demonstrate the importance of mandatory targets, a sensitivity analysis of the 2020 EU registrations showed that lower by one-third sales of zero and low-emission vehicles (Z-L-EVs) would have resulted in seven out of ten manufacturer pools failing to meet the 2020 target of 95 g/km. The simulation framework was validated against certified values of 2021 registrations and was subsequently used to calculate real-world fleet performance to quantify the actual emissions savings. The real-world CO2 emissions in the registered fleets for 2021 were estimated to be 143 g/km and 204 g/km, for the EU and Australia, respectively. With these results as a reference, the share of ZEVs required to meet the targets set in both regions is calculated: 51% of the new registrations in 2030 in the EU for achieving 49.5 g/km, and 60% in Australia for recently set target of 58 g/km for 2029. The respective calculated average fleet-wide real-world tailpipe CO2 emissions were estimated to be 66 g/km (EU) and 77 g/km (Australia). As a last step, the study discusses the application of similar tools and analysis in the design of future policies.

Single-Scene SAR Image Data Augmentation Based on SBR and GAN for Target Recognition

High-performance neural networks for synthetic aperture radar (SAR) automatic target recognition (ATR) often encounter the challenge of data scarcity. The lack of sufficient labeled SAR image datasets leads to the consideration of using simulated data to supplement the dataset. On the one hand, electromagnetic computation simulations provide high amplitude accuracy but are inefficient for large-scale datasets due to their complex computations and physical models. On the other hand, ray tracing simulations offer high geometric accuracy and computational efficiency but struggle with low amplitude correctness, hindering accurate numerical feature extraction. Furthermore, the emergence of generative adversarial networks (GANs) provides a way to generate simulated datasets, trying to balance computational efficiency with image quality. Nevertheless, the simulated SAR images generated based on random noise lack constraints, and it is also difficult to generate images that exceed the parameter conditions of the real image’s training set. Hence, it is essential to integrate physics-based simulation techniques into GANs to enhance the generalization ability of the imaging parameters. In this paper, we present the SingleScene-SAR Simulator, an efficient framework for SAR image simulation that operates under limited real SAR data. This simulator integrates rasterized shooting and bouncing rays (SBR) with cycle GAN, effectively achieving both amplitude correctness and geometric accuracy. The simulated images are appropriate for augmenting datasets in target recognition networks. Firstly, the SingleScene-SAR Simulator employs a rasterized SBR algorithm to generate radar cross section (RCS) images of target models. Secondly, a specific training pattern for cycle GAN is established to translate noisy RCS images into simulated SAR images that closely resemble real ones. Finally, these simulated images are utilized for data augmentation. Experimental results based on the constructed dataset show that with only one scene SAR image containing 30 target chips, the SingleScene-SAR Simulator can efficiently produce simulated SAR images that exhibit high similarity in both spatial and statistical distributions compared with real images. By employing simulated SAR images for data augmentation, the accuracy of target recognition networks can be consistently and significantly enhanced.

Towards resilience in Industry 5.0: application of system dynamics in matrix-structured manufacturing system

In the face of increasing global disruptions, resilient manufacturing systems are critical to ensuring operational stability, a concept central to the Industry 5.0 vision. The Matrix-Structured Manufacturing System (MMS) is a key example of such resilience, designed to maintain functionality and recover quickly under major disruptions. This paper introduces a novel System Dynamics (SD)-based modelling and simulation framework to quantitively assess the performance indicators and resilience of MMS. By simulating key disruption scenarios, including partial equipment failures and order fluctuations with resource constraints, this study evaluates the system’s response and recovery capabilities using a Comprehensive Resilience Index (CRI). Unlike conventional approaches, the proposed SD model captures the dynamic interactions across various production configurations and simulates the operational logic governing them, which can also be applied to other production systems. The simulation results demonstrate the potential of SD to support decision-making by providing insights into the effectiveness of reconfiguration strategies and resource management. This work aims at offering robust methods for quantitative resilience assessment and strategic planning towards the practical implementation of resilient manufacturing in Industry 5.0 envisions.

Predicting the evolution of texture and grain size during deformation and recrystallization of polycrystals using field fluctuations viscoplastic self-consistent crystal plasticity

This paper advances a recent formulation of a field fluctuations viscoplastic self-consistent (FF-VPSC) crystal plasticity model to predict not only the evolution of texture in polycrystalline metals undergoing deformation and recrystallization but also the evolution of grain size. The model considers stress and lattice rotation rate fluctuations inside grains to calculate intragranular misorientation spreads. The spreads are used for modeling of grain fragmentation during deformation and grain nucleation during recrystallization enabling the predictions of concomitant evolution of texture and grain size distributions. The evolutions of textures and grain size distributions are first simulated for commercially pure Cu undergoing severe plastic deformation (SPD) in high pressure torsion to agree well with corresponding experimental data. Next, the evolution of texture and grain size in an aluminum alloy (AA) 5182-O after simple tension and static recrystallization are predicted and compared with experiments. Finally, the predictions of texture and grain size distributions in a magnesium alloy WE43 undergoing a thermo-mechanical loading in the dynamic recrystallization regime are presented and compared with experiments. After validation, the model is coupled with the implicit finite element method (FEM) via a user-material subroutine (UMAT) in Abaqus to facilitate modeling of complex boundary conditions and geometries. The multilevel approach is referred to as FE-FF-VPSC in which every integration point embeds the FF-VPSC constitutive law, considering texture evolution and the directionality of deformation mechanisms operating at the single crystal level. FE-FF-VPSC is applied to simulate a sequence of rolling, recrystallization, and deep drawing of a circular cup of AA6022-T4. The simulated processing sequence demonstrates the versatility of the simulation framework developed in the present paper in not only predicting texture and grain size evolution and phenomena pertaining to behavior of materials but also geometrical shape changes important for the optimization of metal forming processes. To this end, the effects of initial texture and underlying anisotropy on the formation of earing profiles during deep drawing are simulated and discussed.

A refined model of lithium-ion battery considering agglomerated active material and non-uniform graphite particle

This study presents a refined lithium-ion battery model based on the Newman model, incorporating agglomerate structures in the positive electrode and non-uniform particle size distribution in the negative electrode. The agglomerates in the positive region consist of nano-scale primary particles, pore spaces, and binders, while the negative region features a random distribution of particle sizes. The model employs the volume averaging method and integrates a thermal model to provide a comprehensive simulation framework. The results of the numerical calculations and experimental measurements show that the model is in good agreement with the experimental data. The predicted discharge capacity of the proposed model is significantly higher than that of the traditional P2D model. This improvement is primarily due to the increased diffusion distance in agglomerates and the non-uniform particle sizes, which result in substantial changes in overpotential, electrolyte concentration, and solid-phase lithium-ion distribution. The model effectively captures the complex electrochemical behavior during discharge, underscoring the importance of considering structural heterogeneities in battery modeling.

Multiphysics Simulation of Continuous Liquid Interface Production (CLIP) 3D Printing Technology

Abstract This study explores the advancements of 3D printing through Continuous Liquid Interface Production (CLIP), which has achieved a remarkable 100-fold increase in print speed over conventional stereolithography. CLIP’s rapid printing is enabled by an oxygen inhibition layer above the resin-vat window, initiating photopolymerization above the deadzone for faster resin flow. Despite CLIP’s notable speed advantage, it struggles with artifacts arising from non-optimal print cofigurations. Our research addresses this challenge by developing a novel multiphysics simulation tool. In order to evaluate the effects of various parameters, this study introduces a 2D-CLIP multiphysics simulation tool integrating optical and chemical models. The simulation tool employs a MATLAB-PDE solver that incorporates multiphysics equations to forecast deadzone thickness and cured dimensions at various print settings. This approach allows for a comprehensive understanding of the CLIP process and its variables. The simulation tool effectively predicts key parameters, aiding in the fine-tuning of the printing process. It significantly reduces experimental costs and time while enhancing the precision of CLIP 3D printing. The tool’s predictions are instrumental in optimizing print parameters, thereby mitigating the prevalent artifacts in printed objects. This research contributes a pioneering simulation tool for CLIP 3D printing, addressing the critical gap in optimizing print configurations. Its innovative approach in integrating multiphysics models within a simulation framework offers a valuable asset in advancing the capabilities of high-speed 3D printing technologies.

An automated toolbox for microcalcification cluster modeling for mammographic imaging.

Mammographic imaging is essential for breast cancer detection and diagnosis. In addition to masses, calcifications are of concern and the early detection of breast cancer also heavily relies on the correct interpretation of suspicious microcalcification clusters. Even with advances in imaging and the introduction of novel techniques such as digital breast tomosynthesis and contrast-enhanced mammography, a correct interpretation can still be challenging given the subtle nature and large variety of calcifications. Computer simulated lesion models can serve to develop, optimize, or improve imaging techniques. In addition to their use in comparative (virtual clinical trial) detection experiments, these models have potential application in training deep learning models and in the understanding and interpretation of breast lesions. Existing simulation methods, however, often lack the capacity to model the diversity occurring in breast lesions or to generate models relevant for a specific case. This study focuses on clusters of microcalcifications and introduces an automated, flexible toolbox designed to generate microcalcification cluster models customized to specific tasks. The toolbox allows users to control a large number of simulation parameters related to model characteristics such as lesion size, calcification shape, or number of microcalcifications per cluster. This leads to the capability of creating models that range from regular to complex clusters. Based on the input parameters, which are either tuned manually or pre-set for a specific clinical type, different sets of models can be simulated depending on the use case. Two lesion generation methods are described. The first method generates three-dimensional microcalcification clusters models based on geometrical shapes and transformations. The second method creates two-dimensional (2D) microcalcification cluster models for a specific 2D mammographic image. This novel method employs radiomics analysis to account for local textures, ensuring the simulated microcalcification cluster is appropriately integrated within the existing breast tissue. The toolbox is implemented in the Python language and can be conveniently run through a Jupyter Notebook interface, openly accessible at https://gitlab.kuleuven.be/medphysqa/deploy/breast-calcifications. Validation studies performed by radiologists assessed the level of malignancy and realism of clusters tuned with specific parameters and inserted in mammographic images. The flexibility of the toolbox with multiple simulation methods is illustrated, as well as the compatibility with different simulation frameworks and image types. The automation allows for the straightforward and fast generation of diverse microcalcification cluster models. The generated models are most likely applicable for various tasks as they can be configured in a variety of ways and inserted in different types of mammographic images of multiple acquisition systems. Validation studies confirmed the capacity to simulate realistic clusters and capture clinical properties when tuned with appropriate parameter settings. This simulation toolbox offers a flexible means of simulating microcalcification cluster models with potential use in both technical and clinical research in mammography imaging. The 3D generation methods allow for specifying many characteristics regarding the calcification shape and cluster architecture, and the 2D generation method presents a novel manner to create microcalcification clusters tailored to existing breast textures.

A predictive large-eddy simulation framework for the analysis of wind loads on a realistic low-rise building geometry

The accuracy of large-eddy simulations (LESs) for predicting wind-induced pressure loads remains an important topic of inquiry. This paper aims to advance this topic by validating an LES workflow for predicting wind pressures on a realistic low-rise building model exposed to a suburban neutral surface layer. We compare two wind tunnel data sets and LES predictions, obtained using a two-step workflow. First, we ensure that an accurate representation of the surface layer wind flow is obtained at the building location. Next, we assess the resulting wind loads on the building model. Using this workflow, we demonstrate consistent agreement between LES predictions and wind tunnel tests, where the discrepancies between the LES and wind tunnel results mimic the discrepancies between the two wind tunnel tests. This finding underscores that the pressure signals in certain locations are sensitive to inevitable, small differences in the approach flow. LES-based flow visualization uncovered that the most negative pressure peaks, which occur on the building roof, arise from hairpin-like vortices that are lifted from the separation region near the upstream roof edge. The results shed light on the complex dynamics of wind-induced pressure loads and contribute to quantifying the reliability of LES for wind load estimation.

MiNNIE: a Mixed Multigrid Method for Real-time Simulation of Nonlinear Near-Incompressible Elastics

We propose MiNNIE, a simple yet comprehensive framework for real-time simulation of nonlinear near-incompressible elastics. To avoid the common volumetric locking issues at high Poisson's ratios of linear finite element methods (FEM), we build MiNNIE upon a mixed FEM framework and further incorporate a pressure stabilization term to ensure excellent convergence of multigrid solvers. Our pressure stabilization strategy injects bounded influence on nodal displacement which can be eliminated using a quasiNewton method. MiNNIE has a specially tailored GPU multigrid solver including a modified skinning-space interpolation scheme, a novel vertex Vanka smoother, and an efficient dense solver using Schur complement. MiNNIE supports various elastic material models and simulates them in real-time, supporting a full range of Poisson's ratios up to 0.5 while handling large deformations, element inversions, and self-collisions at the same time.

Deep Reinforcement Learning for Dynamic Task Scheduling in Edge-Cloud Environments

With The advent of the Internet of Things (IoT) and its use cases there is a necessity for improved latency which has led to edgecomputing technologies. IoT applications need a cloud environment and appropriate scheduling based on the underlying requirements of a given workload. Due to the mobility nature of IoT devices and resource constraints and resource heterogeneity, IoT application tasks need more efficient scheduling which is a challenging problem. The existing conventional and deep learning scheduling techniques have limitations such as lack of adaptability, issues with synchronous nature and inability to deal with temporal patterns in the workloads. To address these issues, we proposed a learning-based framework known as the Deep Reinforcement Learning Framework (DRLF). This is designed in such a way that it exploits Deep Reinforcement Learning (DRL) with underlying mechanisms and enhanced deep network architecture based on Recurrent Neural Network (RNN). We also proposed an algorithm named Reinforcement Learning Dynamic Scheduling (RLbDS) which exploits different hyperparameters and DRL-based decision-making for efficient scheduling. Real-time traces of edge-cloud infrastructure are used for empirical study. We implemented our framework by defining new classes for CloudSim and iFogSim simulation frameworks. Our empirical study has revealed that RLbDS out performs many existing scheduling methods.

A transient source-function-based embedded discrete fracture model for simulation of multiscale-fractured reservoir: Application in coalbed methane extraction

The classic embedded discrete fracture model (EDFM) may cause certain errors when simulating the transient flow process within the multi-scale fractured system. In this study, the transient transmissibility was derived by using the point (matrix-fracture) and line (fracture-fracture) source functions. The transient source-function-based EDFM (tSEDFM) is established via this method. Then, based on the dual criteria that dNNC is less than a certain distance and its projection point is within the corresponding fracture domain, a 3D fracture meshing method for arbitrary fracture geometry and orientation is established. The finite volume method is used to discrete the fluid flow equations for matrix and fracture systems, considering the multi-deformation-induced permeability evolution. Consequently, the numerical simulation framework of FVM-tSEDFM is proposed to investigate the transient mass transfer characteristics. Compared with the current pEDFM, the pressure drop near fractures of the proposed model is higher and the gap narrows as pressure wave further spreads. Compared with AEDFM, the Tmf of tSEDFM is almost the same, but AEDFM's Tff is substantially higher in the initial stage and the attenuation rate is slower, resulting in an error of nearly 15% at the hydraulic fracture location. As fracture density increases, more transient flow makes AEDFM- and EDFM-calculated BHP values deviate from tSEDFM. History matching using the tSEDFM with only 10000 meshes shows better results where the mismatching rate is larger than 90%. Simulation of CBM extraction indicate that sharp pressure decline occurs near the fractures, resulting in gas desorption enhancement and rapid productivity increment. The tSEDFM demonstrates good practicality in calculating the production and long-term permeability evolution. Results also show that the tSEDFM can correct the underestimation of mass exchange in the early stage and overestimation since the pseudo-steady stage, and can be better applied to the numerical simulation of multi-scale fractured reservoirs. As the dominant factor for gas extraction, sensitivity analyses of fracture properties are also documented.

Process Synthesis, Design and Techno-Economic Assessment of Malonic Acid Production

This work focuses on the design and techno-economic evaluation of an industrial facility for the production of malonic acid. The raw material utilized is commercial glucose syrup with a concentration of 95%. Based on a patent of Lygos, Inc., an innovative biotechnology research company, this study presents a comprehensive synthesis, design, and simulation framework for the production of malonic acid through oligosaccharide fermentation. An integrated process flowsheet is proposed and simulated using SuperPro Designer™. The analysis indicates that for an installation capacity of about 8000 MT/yr of the final product with a purity of 99.5%, the production cost is estimated at USD 7.92/kg. A comprehensive study of the capacity’s impact on economics reveals that this cost could decrease to as low as USD 6.05/kg. A parametric analysis and optimization conducted at the flowsheet level identifies opportunities for further reducing production costs, laying the groundwork for a potential decrease in the product’s selling price.

A Comprehensive Microstructure-Aware Electromigration Modeling Framework; Investigation of the Impact of Trench Dimensions in Damascene Copper Interconnects.

As electronic devices continue to shrink in size and increase in complexity, the current densities in interconnects drastically increase, intensifying the effects of electromigration (EM). This renders the understanding of EM crucial, due to its significant implications for device reliability and longevity. This paper presents a comprehensive simulation framework for the investigation of EM in nano-interconnects, with a primary focus on unravelling the influential role of microstructure, by considering the impact of diffusion heterogeneity through the metal texture and interfaces. As such, the resulting atomic flux and stress distribution within nano-interconnects could be investigated. To this end, a novel approach to generate microstructures of the conductor metal is presented, whereby a predefined statistical distribution of grain sizes obtained from experimental texture analyses can be incorporated into the presented model, making the model predictive under various scales and working conditions with no need for continuous calibration. Additionally, the study advances beyond the state-of-the-art by comprehensively simulating all stages of electromigration including stress evolution, void nucleation, and void dynamics. The model was employed to study the impact of trench dimensions on the dual damascene copper texture and its impact on electromigration aging, where the model findings were corroborated by comparing them to the available experimental findings. A nearly linear increase in normalized time to nucleation was detected as the interconnect became wider with a fixed height for aspect ratios beyond 1. However, a saturation was detected with a further increase in width for lines of aspect ratios below 1, with no effective enhancement in time to nucleation. An aspect ratio of 1 seems to maximize the EM lifetime for a fixed cross-sectional area by fostering a bamboo-like structure, where about a 2-fold of increase was estimated when going from aspect ratio 2 to 1.

A generic random field approach for stratification uncertainty quantification

Geological uncertainty is a crucial factor in the reliability design of geotechnical structures, necessitating advanced probabilistic approaches for robust subsurface stratification and uncertainty quantification. However, the spatial existence of geological formations at a specific location is indeed a nominal categorical variable. Unlike the spatial variability of soil properties that has been widely addressed using random field theory or geostatistics, quantifying the spatial correlations of formation existence poses unique challenges. In this study, a generic framework for geological uncertainty quantification is proposed. The spatial existence of each formation is modeled with individual latent random fields, such that the spatial correlation strength of formation existence can be defined in a consistent manner as that in geotechnical random fields. Moreover, an effective distance metric is introduced to accommodate spatial correlations within a distorted random field, allowing for the incorporation of stratigraphic dip naturally. The latent random fields are subsequently input into the generalized linear mixed model (GLMM) to derive the probabilistic distribution of formation existence in the form of discrete random fields. The simulation framework is further formulated as a Bayesian inference problem to incorporate existing information from borehole records of a site. The proposed formulation offers great flexibility and versatility in simulating various complex stratigraphic configurations, which is demonstrated through a hypothetical case study with an inter-layer and a real case study in Perth, West Australia. This study advances rigorous quantification of stratification uncertainty, facilitating more reliable geotechnical design and risk assessment in complex geological environments.