Error Contributions Research Articles

Overlay Analysis and Optimization for High-Accuracy Hybrid D2W Bonding

In this study we analyze the misalignment of dielectric bonded die-to-wafer (D2W) assemblies using a direct D2W bonding approach. Understanding the overlay error contributors is essential to enable high accuracy direct hybrid D2W bonding. Next to the translation and rotation placement error, initial results also show an apparent scaling error. A dedicated overlay test vehicle is designed to enable high resolution overlay measurements. Every single 7.2*7.2mm^2 die contains 25 overlay measurement locations, allowing a more detailed intra-die overlay analysis. Patterned 50 µm thin dies are directly bonded to a patterned target wafer using a BESI ChameoUltraPlus high accuracy (hybrid) D2W bonder. After bonding, Scanning Acoustic Microscopy (SAM) shows good bonding yield with very little voids. Next, an infrared (IR) overlay measurement is performed on 25 points per individual bonded die. This high-resolution overlay measurement confirms a significant linear scaling component of around 33ppm, resulting in a ~150nm scaling error in the die corners, in addition to the die placement error. Finite Element Method (FEM) modeling shows that a dielectric layer induced deformation in the thin top die combined with a die-center mechanical bond initiation is the main cause of the observed scaling error. Further analysis of the overlay error reveals only a very small residual fingerprint. This shows the importance of reducing the scaling error, either by exploring alternative bondheads which cause less die deformation, and/or the introduction of stress compensation layers.

Analysis of Repeat Hepatitis C Viremia After Sustained Virologic Response in a Large Cohort of U.S. Veterans.

Chronic hepatitis C virus (HCV) infection affects >1% of the U.S. population, higher among U.S. Veterans. Direct-acting antiviral (DAA) medications are effective for viral cure, termed sustained virologic response (SVR), but repeat viremia after SVR is recognized. Prior work has been limited by use of electronic medical record data. We aim to better understand repeat viremia in the DAA era through detailed chart review. We identified 1,129 individuals from Veteran's Health Administration (VHA) who achieved SVR using DAA therapy but subsequently had detectable serum HCV nucleic acid. A random subset of 110 were chart reviewed and assigned one of four categories using laboratory, diagnosis, and chart review data: definite reinfection (25.5%), probable reinfection (25.5%), false positive (11.8%), and presumed late relapse (37.3%). We conducted between-group analysis of variance to identify demographic, behavioral, and laboratory features specific to each. In our medical record cohort (n=1,129), substance use and unstable housing were common and median time to repeat viremia was 1.9 years. In our chart review cohort (n=110), younger age (18-34) and substance use were more frequent in definite or probable reinfection. Presumed relapse had comparatively more comorbid hepatocellular carcinoma (HCC) (20%, p<0.05) and more than half occurred prior to 1 year. The unique category of false positive has not previously been reported. This study deepens understanding of HCV reinfection and relapse and highlights important features including the HCV and opioid syndemic, contribution of laboratory error, possibility of a viral reservoir in HCC, and clinical engagement implications for those with ongoing risk.

Latency and amplitude of catch-up saccades to accelerating targets.

To track moving targets, humans move their eyes using both saccades and smooth pursuit. If pursuit eye movements fail to accurately track the moving target, catch-up saccades are initiated to rectify the tracking error. It is well known that retinal position and velocity errors determine saccade latency and amplitude, but the extent to which retinal acceleration error influences these aspects is not well quantified. To test this, 13 adult human participants performed an experiment where they pursued accelerating/decelerating targets. During the ongoing pursuit, we introduced a randomly sized target step to evoke a catch-up saccade and analyzed its latency and amplitude. We observed that retinal acceleration error (computed over a 200 ms range centered 100 ms before the saccade) was a statistically significant predictor of saccade amplitude and latency. A multiple linear regression supported our hypothesis that retinal acceleration errors influence saccade amplitude in addition to the influence of retinal position and velocity errors. We also found that saccade latencies were shorter when retinal acceleration error increased the tracking error and vice versa. In summary, our findings support a model in which retinal acceleration error is used to compute a predicted position error ∼100 ms into the future to trigger saccades and determine saccade amplitude.NEW & NOTEWORTHY When visually tracking object motion, humans combine smooth pursuit and saccadic eye movements to maintain the target image on the fovea. Retinal position and velocity errors are known to determine catch-up saccade amplitude and latency, however, it is unknown if retinal acceleration error is also used to predict future target position. This study provides evidence of a small but statistically significant contribution of retinal acceleration error in determining saccade amplitude and latency.

Analysis of meteorological effects of cosmic ray neutron component based on data from mid-latitude stations

Precision neutron monitors providing continuous monitoring with a statistical accuracy of ~0.15 %/hr are effective for studying cosmic ray variations; therefore, contributions from other error sources should not exceed the contribution of this statistical error. Such possible sources primarily include changes in atmospheric pressure and humidity. The aim of the work is to estimate the barometric effect of the neutron component of cosmic rays for the low-latitude stations Tashkent and Alma-Ata (mountain), including periods of maximum solar activity. The technique developed on the basis of multifactor correlation analysis is applicable to processing data from any detectors of the worldwide network of neutron monitors. As a result, we have obtained annual average barometric coefficients of the neutron component at the stations Tashkent and Alma-Ata. The humidity effect was also estimated for the mid-latitude station Moscow. The study draws the conclusion that the approach considered can effectively solve the problem.

Анализ метеорологических эффектов нейтронной компоненты космических лучей по данным среднеширотных станций

Precision neutron monitors providing continuous monitoring with a statistical accuracy of ~0.15 %/hr are effective for studying cosmic ray variations; therefore, contributions from other error sources should not exceed the contribution of this statistical error. Such possible sources primarily include changes in atmospheric pressure and humidity. The aim of the work is to estimate the barometric effect of the neutron component of cosmic rays for the low-latitude stations Tashkent and Alma-Ata (mountain), including periods of maximum solar activity. The technique developed on the basis of multifactor correlation analysis is applicable to processing data from any detectors of the worldwide network of neutron monitors. As a result, we have obtained annual average barometric coefficients of the neutron component at the stations Tashkent and Alma-Ata. The humidity effect was also estimated for the mid-latitude station Moscow. The study draws the conclusion that the approach considered can effectively solve the problem.

Examining the Role of Organizational Culture in Shaping Security Practices: A Case Study of Tertiary Institutions

This study investigates the critical role of the human factor in enhancing organizational cybersecurity resilience, particularly within the context of tertiary institutions in Nigeria. As organizations increasingly depend on digital technologies, understanding how employee knowledge, behavior, and awareness impact information security is paramount. The research highlights the significant contribution of human error to security breaches, underscoring that even the most sophisticated technological defenses are vulnerable when individuals do not adhere to security protocols. Utilizing a comprehensive literature review, the study examines the implications of human behavior, including negligence and social engineering, on organizational security outcomes. Key findings indicate that insufficient training, lack of supervision, and poor understanding of security policies exacerbate vulnerabilities. Recommendations include implementing robust security awareness programs and ensuring that only qualified personnel teach keyboarding skills, as these practices can mitigate risks. Ultimately, the research advocates for a socio-technical approach to cybersecurity, emphasizing the need for collaboration between technical solutions and human factors to foster a more secure operational environment in the educational sector.

Abel transform of laser-induced plasma image: Overcoming challenges of noisy and insufficient data for reliable reconstruction

Reconstructing spatial distribution of emissivity is important for study of heterogeneous plasma sources. However, often used Radon and Abel transforms tend to amplify noise in the data. The small size and short lifetime of laser-induced plasma result in short exposures for signal acquisition, and, consequently limited number of points along the source available for measurements and noisy signals. We compared 14 Abel transform algorithms and various noise processing methods, focusing on both limited data points and high noise levels. All algorithms were presented in matrix form to ensure fair comparison, and we proposed a metric based on the largest singular value of matrix operator for error assessment. An error analysis is performed to show the contribution of approximation and noise errors to the overall reconstruction error. Our results indicate that at higher noise levels, the choice of noise reduction method is more critical than the inversion algorithm itself, with regularization proving to be the most effective for noise control. Given the minimal impact of algorithm choice on the final result, simple and easy-to-implement methods like “Onion Peeling” are recommended to be combined with regularization. Furthermore, transforms with regularization were the most stable for reconstructing complex profiles. Summarizing the results for all considered cases, “Hankel-Fourier” and “Cubic Spline” are the most accurate for noiseless data with limited number of spatial points, while for cases with a large amount of data, “Polynomial”, “Piessens-Verbaeten”, and “Minerbo-Levy” are better, especially for noisy data.

Physics-informed and data-driven hybrid method for transmission accuracy design optimization of planetary roller screw mechanism

The planetary roller screw mechanism (PRSM) faces an ever-increasing precision transmission demand in current advanced equipment. The relationship between machining errors and transmission accuracy remains elusive due to the over-simplified physical models and small-sample experimental datasets. This work proposes a physics-informed and data-driven hybrid strategy for PRSM transmission accuracy evaluation and tolerance optimization. In the physical model, a PRSM transmission accuracy model is developed to calculate transmission error that considers 16 machining errors in eccentric, nominal diameter, pitch, flank angle, and roller consistency. In the dataset establishment, thread profile measurements and dynamic leadscrew inspections are conducted for the machining error and transmission accuracy data acquisition. A data augmentation approach combining the physical model with the generative adversarial network is utilized to predict travel deviation, variations, and axial backlash and estimate machining error contribution with the small-sample experimental dataset. It is firstly found that the roller consistency of nominal diameter significantly affects PRSM travel variation V2π with a 17.3 % importance value. With the developed framework, the key tolerances for screw, roller, nut, and roller consistency are optimized toward a typical precision transmission requirement using the non-dominated sorting genetic algorithm. It also provides a tolerance grade recommendation table with PRSM transmission accuracy level in engineering practice.

Improved frequency shift compensation technique and pulse sequence for multi-loop atom interference experiments

With the rapid development of atom interference technology, multi-loop atom interferometers are widely used in the high-precision measurement of various physical constants and testing of various gravity-related effects. However, in ground-based multi-loop atom interference experiments, the systematic error contribution by classical effects is an important factor that affects the experimental measurement accuracy and gravitational effect detection. Based on this, we used the atomic wave-function evolution-phase accumulation method to provide a high-order interference phase of multi-loop atom interferometers in an inhomogeneous gravitational field containing Earth’s rotation. Furthermore, we propose a new scheme that combines optimised frequency-shift compensation technology with an improved pulse sequence to eliminate systematic errors due to the gravity gradient, Earth’s rotation, and their coupling effect with the pulse duration, as well as the coupling effect of laser detuning with the pulse duration. This study lays a theoretical foundation for experiments on multi-loop atom interferometers with higher precision.

Estimation of seismometer clock time offsets using Kalman Filter towards accurate seismic velocity change

SUMMARY Monitoring seismic velocity changes obtained from ambient noise correlations is widely used to understand changes in rock properties in response to earthquakes, volcanic activities and environmental changes. Since continuous seismic data have been accumulated, this method can estimate long-term changes in seismic velocity, such as crustal recovery after a major earthquake and temporal variations in seismic velocity related to long-term environmental change. Changes in seismic velocity can be estimated with a high temporal resolution by measuring the phase differences of ambient noise correlations based on a seismic interferometry method. Still, these phase differences are influenced not only by seismic wave velocity changes but also by errors in clock timing in seismometers. The clock drift occurs due to out-of-synchronization with the GPS clock and the drift of the internal clock. Therefore, to accurately monitor temporal changes in crustal structure by measuring the phase differences of noise correlations, it is crucial to evaluate the contribution of errors in clock timing to the phase differences. Recently, a method using an extended Kalman filter based on a state-space model was developed for reliable detection of temporal changes in the waveforms of ambient noise correlations, with the state-space model offering the advantage of flexible modelling of time-series data. In this study, we incorporated the time-shifts caused by clock time errors of the seismometer into the state-space model of the temporal changes in ambient noise correlations. We estimated seismic velocity changes, amplitude changes of noise correlations and clock time errors from 2010 April to 2021 September at seismic stations around the Shinmoe-dake volcano in Japan, which experienced eruptions in 2011 and 2018, respectively. Several stations exhibited clear clock time offsets, and the occurrence of clock time-shifts coincided with the dates when the data logger was turned off for seismic station maintenance or replacement of the seismometer. The proposed method provides stable estimations with respect to the signal-to-noise ratio of the waveform, and this stable estimation facilitates accurate timing of seismic recordings, enabling precise analysis of seismic phase arrival times.

Baryon Acoustic Oscillation Theory and Modelling Systematics for the DESI 2024 results

Abstract This paper provides a comprehensive overview of how fitting of Baryon Acoustic Oscillations (BAO) is carried out within the upcoming Dark Energy Spectroscopic Instrument’s (DESI) 2024 results using its DR1 dataset, and the associated systematic error budget from theory and modelling of the BAO. We derive new results showing how non-linearities in the clustering of galaxies can cause potential biases in measurements of the isotropic (αiso) and anisotropic (αap) BAO distance scales, and how these can be effectively removed with an appropriate choice of reconstruction algorithm. We then demonstrate how theory leads to a clear choice for how to model the BAO and develop, implement and validate a new model for the remaining smooth-broadband (i.e., without BAO) component of the galaxy clustering. Finally, we explore the impact of all remaining modelling choices on the BAO constraints from DESI using a suite of high-precision simulations, arriving at a set of best-practices for DESI BAO fits, and an associated theory and modelling systematic error. Overall, our results demonstrate the remarkable robustness of the BAO to all our modelling choices and motivate a combined theory and modelling systematic error contribution to the post-reconstruction DESI BAO measurements of no more than 0.1% (0.2%) for its isotropic (anisotropic) distance measurements. We expect the theory and best-practices laid out to here to be applicable to other BAO experiments in the era of DESI and beyond.

Development and study of ultrasonic pulse velocity measurement system using wave analysis and two-point detection approach

Abstract This article focuses on the development and analysis of an Ultrasonic Pulse Velocity (UPV) measurement system used for the evaluation of concrete and Reinforced Cement Concrete (RCC) in civil construction. The UPV tester is essential for on-site assessments of structures, as it is used to measure the velocity of ultrasonic waves within the material, directly correlating with its strength. UPV testing is affected by the attenuation of ultrasonic waves in concrete, particularly due to the interfacial transition zone. Excess ultrasonic attenuation results in the reduction in the received signal amplitude which may also results in the omission of the initial pulses due to threshold comparison at the receiver. The study highlights the impact of receiver gain on threshold error and discusses the limitations associated with ADC sampling rate and amplitude resolution. UPV measurement, including counter or data acquisition approach, have error contributions associated with threshold voltage comparison. The error due to threshold amplitude selection is quantified, emphasizing the importance of accurate signal analysis, particularly in highly attenuating medium. The article presents the design and development of a PC-based UPV tester with automatic threshold error compensation. The system includes a transmitter, receiver, 32-bit microcontroller, and a Graphical User Interface (GUI) for data analysis. The article introduces a two-point linear detection logic to minimize errors caused by selected signal amplitude and omission of initial pulses in transit time measurements. The proposed method provides effective resolution of 10 ns through software, even at low sampling rate of 2 MS/s. The experimental results demonstrate the effectiveness of the developed UPV tester, with comparisons to a reference calibration facility at CSIR-NPL. The standard deviation in the ultrasonic transit time measurement by the developed UPV device, with threshold error correction, was ±70 ns.

Constellation design and performance of future quantum satellite gravity missions

Temporal aliasing is currently the largest error contributor to time-variable satellite gravity field models. Therefore, the evolution of sensor technologies has to be complemented by strategies to reduce temporal aliasing errors. The most straightforward way to improve temporal aliasing is through extended satellite constellations because they improve the observation geometry and increase the achievable temporal resolution. Therefore, strategies to optimize the design of larger satellite constellations are investigated in this contribution. A complete constellation modeling procedure is presented, starting from primary design variables (such as the required targeted resolutions) and concluding with concrete orbital elements for the individual satellites. In parallel, it is evaluated if improved instrument sensitivities based on quantum technologies (cold atom interferometry) can be fully exploited in the case of larger constellations. For this, future quantum satellite gravity missions adopting the gradiometry concept (similar to the GOCE mission) and the low-low satellite-to-satellite tracking concept (similar to GRACE/-FO) are simulated on optimized constellations with up to 6 satellites/pairs. The retrieval performance of a 6-pair mission in terms of the global equivalent water height RMS can be improved by a factor of roughly 3 compared to an inclined double-pair mission. 3D-gradiometry intrinsically has a better de-aliasing behavior but has extremely high accuracy requirements for the gradiometer (about 10 µEotvos) and the attitude reconstruction to be of any benefit. All simulations show that when incorporating improved sensor technologies, such as future quantum sensing instruments in extended constellations, temporal aliasing will remain the dominant error source by far, up to five orders of magnitude larger than the instrument errors. Therefore, improving sensor technologies has to go hand in hand with larger satellite constellations and improved space–time parameterization strategies to further reduce temporal aliasing effects.Graphical

Drug Errors in Obstetric Anesthesia: A Narrative Review

The subspecialty of obstetric anaesthesiology is different from other subspecialties in anaesthesiology in that, at any point of time, two lives are at stake, making drug errors particularly critical. This narrative review explores the incidence, contributing factors, and preventive strategies for medication errors in Obstetric Anaesthesia. Key contributors of medication errors include distractions, fatigue, look-alike/sound-alike drugs, lack of standardized protocols, poor communication strategies, inadequate training and education. Effective strategies for reducing errors include double-check procedures, bar-coded medication administration systems, implementing “Tall Man lettering for look-alike/sound-alike drugs, structured communication tools, continuous education and training. The review also introduces the mnemonic "SAFE-LABEL CHECK" to encapsulate best practices for minimizing drug errors. Future directions suggest integrating advanced technologies and fostering a multidisciplinary approach to enhance patient safety in obstetric anaesthesia.

Active learning for adaptive surrogate model improvement in high-dimensional problems

This paper investigates a novel approach to efficiently construct and improve surrogate models in problems with high-dimensional input and output. In this approach, the principal components and corresponding features of the high-dimensional output are first identified. For each feature, the active subspace technique is used to identify a corresponding low-dimensional subspace of the input domain; then a surrogate model is built for each feature in its corresponding active subspace. A low-dimensional adaptive learning strategy is proposed to identify training samples to improve the surrogate model. In contrast to existing adaptive learning methods that focus on a scalar output or a small number of outputs, this paper addresses adaptive learning with high-dimensional input and output, with a novel learning function that balances exploration and exploitation, i.e., considering unexplored regions and high-error regions, respectively. The adaptive learning is in terms of the active variables in the low-dimensional space, and the newly added training samples can be easily mapped back to the original space for running the expensive physics model. The proposed method is demonstrated for the numerical simulation of an additive manufacturing part, with a high-dimensional field output quantity of interest (residual stress) in the component that has spatial variability due to the stochastic nature of multiple input variables (including process variables and material properties). Various factors in the adaptive learning process are investigated, including the number of training samples, range and distribution of the adaptive training samples, contributions of various errors, and the importance of exploration versus exploitation in the learning function.

Validation and reliability of the 3D calibration evaluation algorithm for SPM

Abstract Novel marker-based 3D standards, in the form of cascading step-slope pyramids, enable the calibration of three scaling factors and three coupling factors of scanning probe microscopes using only a single sample and a single measurement. They offer better convenience than conventional methods and are therefore increasingly applied. From a quality management perspective, it is of high importance to validate the evaluation algorithm of the 3D calibration strategy and analyse its error contribution. This paper proposes and develops a new software validation method based on both synthetic data and measurement data. Finally, results of our study are discussed, and the impact of the computational error on the measurement uncertainty is examined.

Predicting Abiotic TCE Transformation Rate Constants—A Bayesian Hierarchical Approach

AbstractFe(II) minerals can mediate the abiotic reduction of trichloroethylene (TCE), a widespread groundwater contaminant. If reaction rates are sufficiently fast for natural attenuation, the process holds potential for mitigating TCE pollution in groundwater. To assess the variability of abiotic TCE reduction rate constants, we collected pseudo‐first‐order rate constants for natural sediments and rocks from the literature, as well as intrinsic (surface‐area‐normalized) rate constants of individual minerals. Using a Bayesian hierarchical modeling approach, we were able to differentiate the contributions of natural variability and experimental error to the total variance. Applying the model, we also predicted rate constants at new sites, revealing a considerable uncertainty of several orders of magnitude. We investigated whether incorporating additional information about sediment composition could reduce this uncertainty. We tested two sets of predictors: reactive mineral content (measured by X‐ray diffraction) combined with surface areas and intrinsic rate constants, or the extractable Fe(II) content. Knowledge of the mineral composition only marginally reduced the uncertainty of predicted rate constants. We attribute the low information gain to the inability to measure the (reactive) surface areas of individual minerals in sediments or rocks, which are subject to environmental factors like aqueous geochemistry and redox potential. In contrast, knowing the Fe(II) content reduced the uncertainty about the first‐order rate constant by nearly two orders of magnitude, because the relationship between Fe(II) content and rate constants is approximately log–log‐linear. We demonstrate how our approach provides estimates for the range of cleanup times for a simple example of diffusion‐controlled transport in a contaminated aquitard.

Stochastic analysis of drift error of gyroscope in the single-axis attitude determination

Understanding and mitigating the various stochastic errors in gyroscope measurements is crucial for accurate attitude determination. White noise and bias instability can introduce drift errors, reducing the system’s accuracy. Efficient sensor selection and algorithm design necessitate a thorough understanding of these influences and how they propagate throughout the system. This paper addresses the propagation of significant stochastic errors in the attitude determination system, including white noise (angle random walk), bias instability, rate random walk, and correlated noise. The paper discusses the modeling strategy and statistical characteristics of these error contributors. It then evaluates the variance propagation of these errors within the output of a single-axis attitude determination system. The presented analysis calculates and compares the impact of each source of error on the system’s precision over time. Experiments with the MPU6050 and L3G4200D sensors are conducted to validate the analytical results.

Communication-efficient federated learning via personalized filter pruning

With the popularity of mobile devices and the continuous growth of interactive data, FL (Federated Learning) has gradually become an effective mean to address the problems of privacy leakage and data silos. However, due to the heterogeneity and imbalance of participants, FL faces many challenges, including model accuracy, security, heterogeneous devices and data, privacy preservation, as well as communication overhead and efficiency. To address challenges such as high communication overhead and low model accuracy in FL, we innovatively introduce model pruning into the FL framework and propose a personalized filter pruning-based FL method named PF2 Learning (Personalized Filter Pruning Federal Learning). This method achieves reasonable filter pruning for all local models by performing personalized pruning on each local model. Specifically, on the device side, we use a pruning strategy based on the norm geometric median, which also considers the order dependence between adjacent layers and evaluates the contribution of filters involved in pruning to optimize the pruning for local models at a unified pruning rate. On the server side, we calculate the unified pruning ratio of the model based on the contribution of participants' model filters and training errors to maintain the consistency of the participants' model structures. To validate the effectiveness of our proposed method, we conducted federated image classification tasks on ResNet-18, VGG-11, DenseNet-121, and InceptionNet-V1 models using CIFAR-10, FEMNIST, and ImageNet datasets. The experimental results show that PF2 Learning outperforms most FL pruning methods and exhibits better model performance and accuracy.

Health Status Detection for Motor Drive Systems Based on Generalized-Layer-Added Principal Component Analysis

Health status detection for motor drive systems includes detecting the working status of the motor and diagnosing open-circuit (OC) faults in the inverter. This paper proposes a generalized-layer-added principle component analysis (GPCA) to determine the load-up/load-shedding status of a motor and diagnose faults in its inverter. Most current methods for detecting OC faults are constrained by changes in the current amplitude and frequency, potentially leading to misjudgments during load-up/load-shedding transient states. The proposed method addresses this issue. Initially, this paper employs a homogenization method to process current data, eliminating the impact of transient processes during motor load-up/load-shedding states on inverter fault diagnosis. Subsequently, the fast Fourier transform (FFT) is used to extract the frequency domain characteristics of the data. If the PCA method is trained with a singular matrix, this can lead to an unreliable result. This paper introduces a generalization layer based on the PCA method, leading to the GPCA method, which enables training with singular matrices. The GPCA method is then developed to compute data features. By presetting thresholds and utilizing the prediction error value and contribution rate index of the GPCA method, the relevant state of the motor drive system can be determined. Finally, through simulations and experiments, it has been demonstrated that the method, using data from the stable working state, can effectively detect the working status of a motor and diagnose OC faults in its inverter, with a diagnostic time of 0.05 current cycles.