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IEEE CAS Society is pleased to congratulate the following recipients of the 2024 IEEE Circuits and Systems Society Awards. The Society is especially proud and happy to honor their outstanding accomplishments and we look forward to their continued involvement and close contact with the CAS Society. [ABSTRACT FROM AUTHOR]

As this Special Section goes to publication, semiconductor manufacturing in the United Status, and globally, continues to expand at a seemingly torrid pace. Assisted by government funding and driven in part by artificial intelligence workloads that gobble up increasing amounts of data center computing capacity, Intel and TSMC fabs are going up in Arizona, TI and Samsung fabs are coming to Texas, and Micron has big plans in New York. Unfortunately, just like those flying cars we were once promised, AI has not yet eliminated the need for the skilled trades and engineers required to build and successfully operate a fab. As a result, workforce development has become an important part of the increasingly complex semiconductor manufacturing process: Where are the thousands of engineers the semiconductor industry needs to staff these new fabs going to come from? How can we make more students excited about science and engineering? While the Guest Editors don’t have all the answers, we are happy that ASMC contributes to the solution by actively supporting student presentations and posters and annually recognizing the best student paper of the conference. And, maybe some day, the artificial intelligence systems that semiconductor manufacturing has enabled will give us those Star Wars or Star Trek robots that can build fabs and make chips too. [ABSTRACT FROM AUTHOR]

In this study, a flexible capacitive pressure sensor based on filter paper substrate is proposed. The electric double layer (EDL) structure was constructed through the introduction of ionic liquid (IL), and the pressure-sensing was realized by using the change of ion-electron interface contact. The paper-based iontronic pressure sensor possesses a high sensitivity of 221.9 kPa−1, which is hundreds of times higher than that of the capacitive pressure sensors with conventional parallel plate structure, as well as an ultra-low detection limit of 2 Pa. The sensor exhibits an enormous application potential in healthcare wearable electronics and human-machine interface equipment. [ABSTRACT FROM AUTHOR]

Thermal aging and ultraviolet (UV) irradiation are used as methods to prepare samples of kraft paper spanning its full lifecycle within a high-voltage transformer, from new to end of life. The mechanical break strain is correlated with the degree of polymerization (DP), representing a progressive deterioration of mechanical properties during aging. Subsequent exposure to oil flow mimicking both normal plant operation and reclamation activities shows that aged paper exposed to high rates of flow experiences noticeable surface roughening. Despite this, no measurable erosion occurs even after long-term temperature/flow cycling in used mineral oil containing a notable particulate content. This indicates that kraft paper is unlikely to be eroded by normal reclamation activities, even in a severely aged asset. This confirms the validity of using this approach to extend plant life. [ABSTRACT FROM AUTHOR]

The semiconductor industry is the core industry of the information age. As a key link in the semiconductor industry, wafer fabrication plays a key role in its development. In the testing stage of the wafer, each die of the wafer is detected and marked, and a wafer map with a certain spatial pattern can be formed. The analysis and classification of these spatial patterns can identify the cause of wafer defects, thereby improving production yield. However, as wafer size increases, line widths become smaller, etc., the probability of a mixed defect mode wafer pattern increases. Moreover, the mixed defect mode wafer map is more difficult to identify and classify than the single defect mode wafer map. Therefore, this paper proposes an improved deep convolutional neural network (DCNN) structure model for the recognition and classification of mixed defect pattern wafer maps. From the perspective of increasing the width of the DCNN, the improved network structure can avoid problems such as over-fitting and limited extraction of features due to the continuous deepening of the DCNN. The network is called Multi-Path DCNN (MP-DCNN) structure. The experimental results show that the proposed Multi-Path DCNN structure has better performance and higher classification accuracy than existing methods. [ABSTRACT FROM AUTHOR]

This issue of IEEE Vehicular Technology Magazine includes our first invited paper. In the next issues, we plan to publish a series of invited papers on selected topics of high interest that should drive and become a reference for the community. The invited papers will provide a vision on the evolution, open challenges, and trends for technologies of high-expected impact. [ABSTRACT FROM AUTHOR]

The thermal index for thermally upgraded Kraft paper was determined for a series of five mineral insulating liquids and two natural esters using IEEE Standard C57.100 Annex A.4.2 and compared with the values in IEEE Standard C57.91. [ABSTRACT FROM AUTHOR]

The long-term operation of dry-type transformer bushings at high temperatures can impact the performance of their primary insulation material. This article presents an experimental study of the effect of thermal aging time on electrical treeing in the epoxy resin-impregnated paper. The experimental results show that the electrical treeing pattern along the middle path of the electric field and the interlayer creepage along the average direction of the electric field are closely related to the aging time. Under the effect of thermal aging at 150 °C, the tree-starting voltage of epoxy resin-impregnated paper tends to increase and decrease as the aging time increases. An analysis and discussion of the test results in conjunction with scanning electron microscope (SEM) and energy dispersive spectroscopy (EDS) tests suggest that the destruction of the cross-linked network of the epoxy resin-impregnated paper at 150 °C is a fundamental reason for the significant decrease in the material’s tree-starting voltage and the change in electrical treeing morphology and interlayer creepage. Long-term operation at high temperatures can seriously impact the primary insulation performance of dry-type transformer bushings and requires attention. [ABSTRACT FROM AUTHOR]

Chemical mechanical planarization (CMP) is vital for ensuring chip fabrication uniformity at nanometer scales. The emergence of a series of phenomenological CMP process models (Stine et al., 1997; Gbondo-Tugbawa, 2002; Xie, 2007; Vasilev, 2011) suggests that the existing model upgrade approach is largely based on a change in phenomenological model assumptions, demanding deep insights into complex process mechanisms and protracted period for accuracy improvements. To tackle this issue, this paper proposes a decoupling regression strategy for model upgrades. This strategy employs a data-driven approach to enhance the coupling relationships within the model, facilitating continuous improvement of simulation accuracy based on the existing model. It is capable of achieving improvements in model accuracy even in scenarios where modelers lack insight into complex process mechanisms. We validate our method by upgrading the Density Step Height (DSH) model to the Extend-DSH model to address poor erosion predictions at the 28nm node. Comparing model predictions with silicon data reveals that the Extend-DSH model aligns better with the measured data, reducing the root mean square error from 159.31Å to 6.89Å and increasing the coefficient of determination from -0.83561 to 0.6058, showcasing the effectiveness of the proposed chip-level CMP model upgrade method grounded in the decoupling regression strategy. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method for Virtual Metrology (VM) in plasma etch processes based on analysis of all time and wavelength samples of Optical Emission Spectroscopy (OES) signals. The new method flattens each OES signal into a single vector, after which Singular Value Decomposition (SVD) is performed on the matrix formed by vectors of flattened OES signals in the training dataset. Low rank SVD projections of flattened and standardized OES recordings served as inputs for Ridge Regression, Artificial Neural Network, and Random Forest based VM models. A VM study is then conducted on a dataset gathered from a major 300 mm wafer fabrication facility, showing that the use of newly proposed SVD-based OES features consistently outperformed benchmark VM model features. Additional analysis of feature importance performed based on the analytically tractable Ridge Regression VM model form demonstrated distinct time-frequency patterns of OES signal portions that were highly informative for prediction of relevant Critical Dimensions, clearly justifying the need to use the entire OES signals for VM. [ABSTRACT FROM AUTHOR]

He is author of the books "Artificial Neural Networks for Modelling and Control of Non-linear Systems" (Springer) and "Least Squares Support Vector Machines" (World Scientific), co-author of the book "Cellular Neural Networks, Multi-Scroll Chaos and Synchronization" (World Scientific) and editor of the books "Nonlinear Modeling: Advanced Black-Box Techniques" (Springer), "Advances in Learning Theory: Methods, Models and Applications" (IOS Press) and "Regularization, Optimization, Kernels, and Support Vector Machines" (Chapman & Hall/CRC). [ABSTRACT FROM AUTHOR]

In recent years, the establishment of a substantial amount of academic groups on scientific social network has brought new opportunities for the collaboration among researchers. In this situation, conducting paper recommendation to these academic groups is of terrific necessity in that it can further facilitate group activities. However, when producing group recommendation, existing methods fail to make full use of the abundant group information, from which a great deal of valuable information can be inferred to facilitate the recommendation performance. In addition, those methods tend to assign an equal weight to each group member when aggregating their recommendations, which is unreasonable in practice. Although some improvements have been made to remedy this problem by assigning different weights to group members, they fail to take into account the reliabilities of group members. Therefore, a group-oriented paper recommendation method based on probabilistic matrix factorization and evidential reasoning (GPMF_ER) is proposed in this article to tackle these problems. More specifically, the group and paper content information are integrated into the probabilistic matrix factorization model to enhance the accuracy of individual recommendation. Afterward, evidential reasoning rule is introduced in the aggregation step to consider both the weights and reliabilities of group members. Extensive experiments have been conducted on the real world CiteULike dataset and the results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Measuring the furan concentration in transformer oil is one of the most common methods of monitoring and evaluating insulation paper and estimating its degree of polymerization (DP). Here, methods for measuring furan concentration, especially ultraviolet-visible (UV-Vis) spectroscopy method, are reviewed on the old transformers oil. The results show that the excessive turbidity of old oils and noise at low wavelengths are the main disadvantages of this method, which reduce the measurements accuracy. The following solutions are proposed to overcome these disadvantages: 1) diluting insulation oil with hexane to reduce absorption intensity and 2) using average absorption at a given wavelength to reduce noise impact. These solutions are applied to the oil samples of ten old distribution transformer. It is observed that the correlation between 2-furaldehyde (2-FAL) concentration and average absorption intensity in 190–600 nm wavelength range is equal to ${R}^{{2}} =0.98$. A mathematical equation for calculating 2-FAL concentration based on average absorption is presented. It has been shown that to reduce the test time, a 190–400 nm range is the optimal measurement wavelength range. The results show that the average difference between the values of DP in the proposed method and the direct measurement method is equal to 77.5. [ABSTRACT FROM AUTHOR]

The total/partial oil change treatment can affect the furfural partitioning in oil-paper-pressboard insulation, resulting in poor accuracy of furfural analysis. Moreover, due to the high cost of total oil change, partial oil change is more practical during transformer operation. This paper investigates the furfural partitioning in the oil-paper-pressboard insulation under partial oil change conditions. First, the initial furfural partitioning equation with non-oil change is proposed based on the obtained furfural content in oil, paper, and pressboard. Then, the modified furfural partitioning model under partial oil change condition is established by introducing a correction factor. Moreover, a preliminary correction scheme for the furfural partitioning model considering the influence of moisture and acid is provided. Finally, furfural content in oil-Kraft paper and oil-thermally upgraded Kraft (TUK) paper insulation are discussed, and results indicate that the proposed modified furfural partitioning model is not suitable for the oil-TUK paper insulation. This work can provide theoretical support for furfural analysis of oil-immersed transformers. [ABSTRACT FROM AUTHOR]

Transistor random threshold voltage variations due to process fluctuations seriously affects the stability of Static Random Access Memory (SRAM). In this paper, a SRAM bit transistors threshold voltage $({Vth})$ deviation monitoring scheme and system is proposed. This scheme ingeniously achieves on-chip measurement of all transistors threshold voltages without altering compact SRAM bit array layout. Control signal strategies and Transistor ${Vth}$ Determination Circuit (TVDC) for different types of Devices Under Test (DUTs) have been proposed. The system is implemented using a 65 nm CMOS process with a core area of 0.01875mm2. Through Monte Carlo analysis, the Weighted Average (WA) difference of the proposed scheme and the direct measurement method is not more than 10mV, and the Root Mean Square Error (RMSE) difference is not more than 3mV. This system can also effectively detect the cell position of the transistor threshold voltage mismatch simulated by modifying the substrate voltage. For SRAM arrays of different scales, the method proposed in this paper has area efficiency and flexible reconfigurability. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

The ecological environment is increasingly damaged due to the impact of mineral oil extraction and leakage. As a kind of sustainable resource, a vegetable oil transformer has bright application prospects in future. However, the stability of vegetable oil has always been a problem to be solved. In addition, the metallic copper inside the transformer has a certain catalytic effect on the deterioration of vegetable oil. In this article, a copper–oil–paper insulation model is established. Based on the molecular dynamics simulation method, the catalytic mechanism of copper on the deterioration of oil-paper insulation is revealed from the microscopic level. Then, the effects of temperature, contact area, and oxygen on the catalytic effect of copper are discussed. The results show that copper could accelerate the decomposition of oil-paper insulation by attracting H atoms and O atoms in oil-paper. The increase in temperature accelerates the progress of the catalytic reaction and aggravates the deterioration of oil-paper insulation. The oxygen concentration has an important influence on the catalytic reaction of copper. As the oxygen concentration increases, the catalytic effect of copper is weakened. The abovementioned research could provide some theoretical reference for further exploration of effective oil-paper insulation deterioration protection technology. [ABSTRACT FROM AUTHOR]

Novel designs of paper-based bandstop frequency-selective surfaces (FSSs) are presented in this article. The characteristics of the paper-based honeycomb structure are investigated first. One folded dipole resonator with first-order bandstop response is analyzed and further printed on the sidewalls of the honeycomb structure accordingly. A second-order response with two cascade FSSs is proposed with controllable bandwidth by the coupling coefficient of the resonators with the aid of the equivalent circuit model. Two paper-based FSS samples were fabricated and measured to demonstrate the validity of the proposed design strategies. Benefiting from the merits of lightweight and strong mechanical properties, the proposed paper-based FSS is a potential candidate for application in radar systems and aircraft. [ABSTRACT FROM AUTHOR]

We report our recent progress in quantitative aging assessment of the oil-impregnated-paper (OIP) equipment (i.e., degree of polymerization, DP) by near infrared spectroscopy (NIRS). The NIRS database is built by incorporating 8 types of insulating paper and total 478 differently aged samples. We propose an improved PCA-RBF-NN model to address the nonlinear correlation between DP of insulating paper and spectra, and hence strengthening the prediction accuracy for field assessment. In the improved model, the principle component analysis (PCA) and the filtering layer are two essential procedures for eliminating the noises and unrelated information from the spectra. The field practices show that the improved PCA-RBF-NN model owns better performance than the classic PLS model and general RBF-NN model on the disassembled bushing (RMSE: 56 vs 109 vs 124) and transformer (RMSE: 50 vs 237 vs 244), respectively. The NIRS powered by the improved algorithm can provide a rapid solution to the aging condition assessment of the OIP power equipment in the field. [ABSTRACT FROM AUTHOR]

The generation and distribution of furfural are significantly affected by moisture content in the insulating paper. However, the diffusion mechanism of furfural under moisture effect is not clear, hence, this study investigates the effect of moisture on the diffusion characteristics of furfural by combining macro experiment and micro molecular dynamics simulation. The simulation results demonstrate that the increased moisture content in paper accelerates the diffusion of furfural from paper to oil, which is also proved by the experimental data. Then, the results of interaction energy analysis indicate that the increase of moisture enhances the van der Waals force between oil and furfural. The present findings are expected to improve the theoretical level of furfural analysis, which will facilitate the aging evaluation of the transformer insulating paper based on furfural. [ABSTRACT FROM AUTHOR]

The space charge effect of transformer insulating paper will distort its internal electric field, and its space charge effect will be more prominent under extremely complex working conditions. At present, the micromechanism of charge transport and electric field distortion characteristics of paperboard are not clear under complex working conditions. So in this article, based on the bipolar carrier transport (BCT) model and the dynamic carrier balance model, the effects of high temperature, aging degree, moisture content, and bent degree on charges accumulation in paperboard are studied under dc high voltage by numerical simulation and the measured data needed for simulation. The results show that the higher temperature strengthens the carrier migration, deepens the injection depth of the charge, and aggravates the distortion of electric field intensity in paperboard. The more extended the aging time of paperboard, the greater the internal trap density and the deeper the trap depth, which lead to charges migration difficultly. Increasing water content in the paperboard will accelerate space charges to reach a steady state, while further increasing it will slow down this process, which is related to the shallow trap getting deeper. When the critical part of paperboard is extruded, many traps are introduced, resulting in charges accumulation seriously. The research results can reference the design of paperboard and the maintenance of transformer insulation system. [ABSTRACT FROM AUTHOR]

Methanol is considered to be a new chemical marker to estimate the aging state of transformer cellulose insulation paper. The key to improve the assessment accuracy is to establish the precise relationship between the concentration of methanol in oil and degree of polymerization (DP) of insulation paper. The previous research in this area has shortcomings and needs to be further improved. In this article, first, an aging assessment basic model (temperature 20 °C, moisture content of insulation paper 0.637%) is established based on the theoretical derivation of cellulose degradation kinetics, and is calculated by 130 °C thermal aging experiment. Furthermore, the effects of different temperatures (20 °C–90 °C) and moisture contents of insulation paper (0.6%–3.4%) on methanol in oil are studied by diffusion experiment. Based on the above effects, the temperature and moisture correction factors are established by mathematical reduction. What is more, a modified model is proposed by integrating the basic model and the correction factors. Eventually, the verification experiment is randomly prepared to test the accuracy of modified model and the result shows that the relative error of DP is within 7%. The physical meaning and accuracy of aging assessment are improved by model derivation and modification of influencing factors. [ABSTRACT FROM AUTHOR]

Although the circulating flow of insulating oil can enhance heat dissipation in converter transformers, it can also influence the space charge distribution in oil–paper composite insulation. In this article, the oil–paper composite insulation between flat electrodes was used as the experimental model. Based on the pulsed electroacoustic (PEA) method, we investigated the effect of uncharged flowing oil on the space charge distribution in composite insulation under a dc electric field and polarity reversal. Under a dc electric field, as the oil flow velocity increased, the amount of interface charge between the oil and paper first increased and then decreased. During the polarity reversal process, the field distortion was aggravated at slower oil flows. These experimental results were explained in terms of the effect of oil flow on ionized charges in the oil. [ABSTRACT FROM AUTHOR]

Due to rapid temperature rise and insulation moisture, bubbles can generate in the oil-paper insulation of oil-immersed power transformers, which can reduce the dielectric strength of oil-paper insulation and even bring about the risk of insulation breakdown. In order to accurately evaluate the conditions of bubble formation, the physical process of bubble formation in oil-paper insulation is studied in this article. Considering the effects of water vaporization, gas dissolution in oil, and moisture migration on bubble formation, a bubble formation prediction model is constructed. Based on Henry’s law, the dissolution equilibrium of gas in oil is taken into consideration and the upper boundary of the initial temperature of bubble effect (ITBE) envelope is obtained in this model. Based on the moisture equilibrium in oil-paper insulation, the bubble formation process caused by the emergence and rapid evaporation of free water is analyzed, and then, the lower boundary of the ITBE envelope is obtained in this model. The model results show that the moisture content of the pressboard and the presence of free water have a great impact on ITBE. Specifically, the higher the moisture content, the lower the ITBE value. In addition, with the further increase of moisture content, the upper and lower boundaries of the ITBE envelope gradually tend to be the same. However, once free water is present on the surface of cellulose, ITBE decreases rapidly to below 120 °C. The model built in this article is of great significance to study the temperature limit of the power transformer. Hence the ITBE envelope can provide an important theoretical reference for reducing the risk of bubble formation during power transformer operation. [ABSTRACT FROM AUTHOR]

Healthy sleep is an important indicator of good physical and mental health. The current technology employed for monitoring sleep quality is typically expensive and invasive, which demands innovations in respiration monitoring. In addition, recent effort in developing wearable respiration sensors has faced great challenges in concurrently obtaining low cost, high stretchability, and high sensitivity. Herein, we demonstrate a simple fabrication strategy to construct low-cost, stretchable, skin-breathable and ultrasensitive respiration sensors using pencil drawing, electrode pasting, and laser cutting methods. With smart designs, the sensors exhibited high sensitivity, good stability, decent skin-breathability and capability to detect respiration rate and depth in real-time. The sensors fabricated by the simple low-cost method in this study can be utilized to measure sleep quality, providing warning signals for apnea and sleep disorders. The proposed technology holds promise for developing cost-effective wearable ultrasensitive sensors for sleep quality monitoring. [ABSTRACT FROM AUTHOR]

Detecting wafer map anomalies is crucial for preventing yield loss in semiconductor fabrication, although intricate patterns and resource-intensive labeled data prerequisites hinder precise deep-learning segmentation. This paper presents an innovative, unsupervised method for detecting pixel-level anomalies in wafer maps. It utilizes an efficient dual attention module with a knowledge distillation network to learn defect distributions without anomalies. Knowledge transfer is achieved by distilling information from a pre-trained teacher into a student network with similar architecture, except an efficient dual attention module is incorporated atop the teacher network’s feature pyramid hierarchies, which enhances feature representation and segmentation across pyramid hierarchies that selectively emphasize relevant and discard irrelevant features by capturing contextual associations in positional and channel dimensions. Furthermore, it enables student networks to acquire an improved knowledge of hierarchical features to identify anomalies across different scales accurately. The dissimilarity in feature pyramids acts as a discriminatory function, predicting the likelihood of an abnormality, resulting in highly accurate pixel-level anomaly detection. Consequently, our proposed method excelled on the WM-811K and MixedWM38 datasets, achieving AUROC, AUPR, AUPRO, and F1-Scores of (99.65%, 99.35%), (97.31%, 92.13%), (90.76%, 84.66%) respectively, alongside an inference speed of 3.204 FPS, showcasing its high precision and efficiency. [ABSTRACT FROM AUTHOR]

Three dimensional Not-And (3D NAND) flash memory devices are scaling in the vertical direction to more than 200 oxide/sacrificial wordline nitride layers to further increase storage capacity and enhance energy efficiency. The accurate measurement of the thicknesses of these layers is critical to controlling stress-induced wafer warping and pattern distortion. While traditional optical metrology in the UV-vis-NIR range offers a non-destructive inline solution for high volume manufacturing, we demonstrate in this paper, that mid-IR metrology has advantages in de-correlating oxide and nitride thicknesses owing to their unique absorption signatures. Furthermore, because of the depths sensitivity of oxide and nitride absorptions, the simulated measurement results show the ability to differentiate thickness variations in the vertical zones. Good blind test results were obtained with a machine learning model trained on pseudo-references and pseudo spectra with added skew. [ABSTRACT FROM AUTHOR]

It is known that the determination of the good-dice-in-bad-neighborhoods (GDBNs) has been regarded as an effective technique to reduce the value of the defect parts per million (DPPM) by identifying and rejecting the suspicious dice even though they are good in testing. Instead of examining eight immediate neighbors in a small-sized $3\times 3$ window or exploiting simple linear regression, a large-sized window can be used to recognize the broad-sighted neighborhoods and accurately infer the suspiciousness level for any given die. In this paper, the artificial neural networks (ANN)-based method can be proposed to solve the GDBN identification. Furthermore, two enhanced techniques can be further presented to improve the inference accuracy of the original ANN-based method by considering the variation of the time-dependent wafer patterns and the wafer-to-wafer relationship between two adjacent wafers. After applying the two enhanced techniques, the business profits can be improved in the new ANN-based method. Various experiments on two datasets clearly reveal the superiority of the proposed ANN-based method over the other existing methods. In addition to the reduction of the DPPM value, the new ANN-based method can achieve the 1.5X–2X better reduction in the cost of the return merchandise authorization (RMA). On the other hand, the experimental results show that the similar result can also be obtained in the other lower-yield products. By using the new ANN-based method, the relationships on bad dice cross wafers can be captured and the highly-accurate inference results can be simultaneously maintained. [ABSTRACT FROM AUTHOR]

Titanium tetrachloride (TiCl4) is a well-known source of titanium (Ti) for the formation of titanium nitride (TiN) barrier material in the semiconductor interconnection process; however, the reaction of by-products with airborne molecules can cause unexpected pump trips and equipment breakdown from the by-product powder build-up. Plasma scrubbers are used to decompose by-products, but hydrogen chloride (HCl) and nitrogen oxides are produced during and after the process. The process mechanisms change when the temperature and applied power of the heat source change. In this paper, we study the influence of the reactor temperature and applied power to the heat source on the decomposition capacity of TiCl4 in a plasma pretreatment system (PPS). We examine the effect of the temperature and heat source power to understand the reaction mechanisms for the composition and decomposition of gaseous species with chemical reactions through simultaneous methods. We analyzed the system with computational fluid dynamics (CFD) and chemical kinetic simulation to investigate the changes of the system mechanism. Subsequently, we achieved results for the correlation between the temperature of the reactor, power applied to the heat source, composition and decomposition of species, and chemical reaction mechanisms. [ABSTRACT FROM AUTHOR]

Semiconductor manufacturing processing can be jeopardized due to process fluctuations, and the degradation of equipment parts can significantly influence process variation. Timely diagnosing equipment faults causing process variations is desired in current high-end product manufacturing. This paper proposes a diagnostic method for the SiH4 gas flow rate drift using N2 vibrational transition in oxide deposition. In this research, optical emission spectroscopy (OES) and quadrupole mass spectrometer (QMS) are employed as condition monitoring sensors serving as a reference model to compare the diagnostic performance for gas flow rate drift. The study observes that the OES model exhibits much higher performance for minor diagnoses of less than 5% drift. The diagnostic model performance can be enhanced by incorporating plasma condition and gas indicators compared to when these indicators are used individually. This suggests that when conducting diagnostics for equipment and processes, it is crucial to consider indirect indicators like plasma indicators along with direct indicators such as gas radical density. The comprehensive use of both types of indicators enhances the diagnostic performance, providing a more accurate assessment of the conditions and potential problem in semiconductor manufacturing. [ABSTRACT FROM AUTHOR]

Heart sound auscultation has been applied in clinical usage for early screening of cardiovascular diseases. Due to the high demand for auscultation expertise, automatic auscultation can help with auxiliary diagnosis and reduce the burden of training professional clinicians. Nevertheless, there is a limit to classic machine learning's performance improvement in the era of Big Data. Deep learning has outperformed classic machine learning in many research fields, as it employs more complex model architectures with a stronger capability of extracting effective representations. Moreover, it has been successfully applied to heart sound analysis in the past years. As most review works about heart sound analysis were carried out before 2017, the present survey is the first to work on a comprehensive overview to summarise papers on heart sound analysis with deep learning published in 2017–2022. This work introduces both classic machine learning and deep learning for comparison, and further offer insights about the advances and future research directions in deep learning for heart sound analysis. [ABSTRACT FROM AUTHOR]

Molecular retrosynthesis is a significant and complex problem in the field of chemistry, however, traditional manual synthesis methods not only need well-trained experts but also are time-consuming. With the development of Big Data and machine learning, artificial intelligence (AI) based retrosynthesis is attracting more attention and has become a valuable tool for molecular retrosynthesis. At present, Monte Carlo tree search is a mainstream search framework employed to address this problem. Nevertheless, its search efficiency is compromised by its large search space. Therefore, this paper proposes a novel approach for retrosynthetic route planning based on evolutionary optimization, marking the first use of Evolutionary Algorithm (EA) in the field of multi-step retrosynthesis. The proposed method involves modeling the retrosynthetic problem into an optimization problem, defining the search space and operators. Additionally, to improve the search efficiency, a parallel strategy is implemented. The new approach is applied to four case products and compared with Monte Carlo tree search. The experimental results show that, in comparison to the Monte Carlo tree search algorithm, EA significantly reduces the number of calling single-step model by an average of 53.9%. The time required to search three solutions decreases by an average of 83.9%, and the number of feasible search routes increases by 1.38 times. The source code is available at https://github.com/ilog-ecnu/EvoRRP. [ABSTRACT FROM AUTHOR]

Practitioners of multi-objective optimization currently lack open tools that provide decision support through knowledge discovery. There exist many software platforms for multi-objective optimization, but they often fall short of implementing methods for rigorous post-optimality analysis and knowledge discovery from the generated solutions. This paper presents Mimer, a multi-criteria decision support tool for solution exploration, preference elicitation, knowledge discovery, and knowledge visualization. Mimer is openly available as a web-based tool and uses state-of-the-art web-technologies based on WebAssembly to perform heavy computations on the client-side. Its features include multiple linked visualizations and input methods that enable the decision maker to interact with the solutions, knowledge discovery through interactive data mining and graph-based knowledge visualization. It also includes a complete Python programming interface for advanced data manipulation tasks that may be too specific for the graphical interface. Mimer is evaluated through a user study in which the participants are asked to perform representative tasks simulating practical analysis and decision making. The participants also complete a questionnaire about their experience and the features available in Mimer. The survey indicates that participants find Mimer useful for decision support. The participants also offered suggestions for enhancing some features and implementing new features to extend the capabilities of the tool. [ABSTRACT FROM AUTHOR]

Recently, evolutionary computation (EC) has experienced significant advancements due to the integration of machine learning, distributed computing, and big data technologies. These developments have led to new research avenues in EC, such as distributed EC and surrogate-assisted EC. While these advancements have greatly enhanced the performance and applicability of EC, they have also raised concerns regarding privacy leakages, specifically the disclosure of optimal results and surrogate models. Consequently, the combination of evolutionary computation and privacy protection becomes an increasing necessity. However, a comprehensive exploration of privacy concerns in evolutionary computation is currently lacking, particularly in terms of identifying the object, motivation, position, and method of privacy protection. To address this gap, this paper aims to discuss three typical optimization paradigms, namely, centralized optimization, distributed optimization, and data-driven optimization, to characterize optimization modes of evolutionary computation and proposes BOOM (i.e., oBject, mOtivation, pOsition, and Method) to sort out privacy concerns related to evolutionary computation. In particular, the centralized optimization paradigm allows clients to outsource optimization problems to a centralized server and obtain optimization solutions from the server. The distributed optimization paradigm exploits the storage and computational power of distributed devices to solve optimization problems. On the other hand, the data-driven optimization paradigm utilizes historical data to address optimization problems without explicit objective functions. Within each of these paradigms, BOOM is used to characterize the object and motivation of privacy protection. Furthermore, this paper discuss the potential privacy-preserving technologies that strike a balance between optimization performance and privacy guarantees. Finally, this paper outlines several new research directions for privacy-preserving evolutionary computation. [ABSTRACT FROM AUTHOR]

The carbon oxides (CO2 and CO) in the insulating oil of power transformers come from the thermal aging of cellulosic insulating paper; the CO2/CO ratio can reflect the aging state of cellulosic insulating paper. At present, the formation mechanisms of CO2 and CO in oil-immersed power transformers are still unclear. In this article, the ReaxFF reaction force field is used to simulate the thermal aging process of cellulosic insulating paper, and the main reaction pathways of CO2 and CO molecules formed by cellulose pyrolysis are obtained. The study finds that the amount of CO2 and CO generated is related to temperature. When the pyrolysis temperature continues to rise, the amount of CO2 generated shows a downward trend, while the amount of CO generated continues to increase, and the CO2/CO ratio continues to decrease. The simulation results are consistent with the previous experimental results. Through pathway tracking, it is found that the CO2 and CO molecules are mainly derived from certain specific atoms of cellulose molecules. The COSMO-RS module is used to analyze the change trend of the solubility of CO2 and CO in insulating oil with temperature. The results show that the solubility of CO2 and CO decreases with the increase of insulating oil temperature, which provides theoretical support for accurate application of carbon oxides aging evaluation index at different temperatures. [ABSTRACT FROM AUTHOR]

Timely identification of the aging stage of the transformer can effectively ensure the safe operation of the transformer and prevent the aging fault of the transformer. This work proposes the aging diagnosis of transformer using the Raman spectroscopic recurrence plot combined with learning vector quantization (LVQ) neural network. Oil-paper insulation samples at different aging stages are obtained by accelerated thermal aging tests. Acquisition of Raman spectra of insulating oil is from 800 to 3000 cm−1 and converts to the Raman spectroscopic recurrence plot. The gray-level cooccurrence matrix (GLCM) is used to extract the characteristic information of the Raman spectroscopic recurrence plot. The optimal number of neurons in competitive layer of LVQ neural network is determined by the experimental method. A transformer aging diagnosis model based on the Raman spectroscopic recurrence plot is established. The results show that the diagnostic accuracy of the proposed diagnostic model reached 95%. The Raman spectroscopic recurrence plot has more abundant aging “fingerprint” characteristic information and the proposed diagnostic method shows potential for monitoring oil-paper insulation. [ABSTRACT FROM AUTHOR]

This paper investigates the behavior of interface charges between oil and oil immersed paper insulations in unaged and aged samples. Combined accelerated thermal and electrical aging experiments of oil immersed paper insulations are conducted to obtain aged oil and aged paper samples. The interface charges between oil and paper samples with different aging status are measured with the Pulsed Electro-acoustic (PEA) method. Zeta potentials in samples with different aging status are determined. It is shown that the amount of interface charge increases with the aging duration, and does not follow the prediction of Maxwell-Wagner-Sillars (MWS) law. This is explained in terms of the increase of the interface charge barrier due to the effect of electric double layer. It is proposed that interface charge may act as an optional characterization parameter for the assessment of oil immersed paper insulations. [ABSTRACT FROM AUTHOR]

A growing body of evidence suggests that the ethanol dissolved in the oil can be used as an indicator to effectively monitor the aging behavior of insulating paper. In this study, thermal aging experiments are performed on oil-paper systems with different initial moisture contents and aging temperatures, and then a gas chromatography-mass spectrometer equipped with an automatic headspace sampler is used to test the ethanol contents of the oil samples. The variations and correlations of ethanol contents with temperature, moisture and aging degree are analyzed. The experimental results show that the ethanol contents in oil had a strong correlation with temperature, and is also affected by the cellulose hydrolysis. In addition, a preliminary lifespan assessment of the oil-paper systems under laboratory conditions is conducted by establishing the relationship between ethanol and the degree of polymerization. Then, the time-temperature superposition method is applied to the relationship, and a lifespan model with any temperature is determined according to the ethanol kinetic equation. Based on the model, further observation of the fitting parameters in the case of the two moisture contents (0.8%, 2.3%), the goodness of fit reached higher values of 0.961 and 0.973, respectively. The results of this promising model can help us to a better understanding of ethanol indicator to condition assessment for the transformer paper insulation. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

It is known that 1,2,3 benzotriazole (BTA) can effectively limit the oil flow electrification in oil–paper insulation used in HVAC transformers, but it is still unclear whether BTA is applicable to converter transformers. Thus, the effects of BTA on interface charge behaviors and charge migration in oil–paper system under dc stress are investigated in this article. The experimental results show that BTA reduces the interface barrier between oil and oil-immersed paper by altering the charge distribution of the electric double layer (EDL) at the oil–paper interface, which is beneficial to the operation stability of converter transformers in the condition of polarity inversion. However, it is also found that the BTA additive promotes the charge accumulation at oil–paper interface and enhances the local electric field by increasing the carrier mobility of oil–paper insulation. The experimental results in this article provide references for the selection of insulating oil additives for converter transformers. [ABSTRACT FROM AUTHOR]

The 9TH AAAC Conference on Affective Computing and Intelligent Interaction 2021 was held in a virtual format in the fall of 2021. It was technically co-sponsored by the IEEE Computer Society and featured the recent work on Affective Computing. The six best papers from this conference were selected by the technical program chairs. They were invited to submit their extended version to be considered for this special section at the IEEE Transactions on Affective Computing. Each submission was reviewed by at least three expert reviewers and was evaluated in terms of overall contribution and the adequacy of the additional content to warrant a new article. This special section features five accepted submissions whose major contributions are summarized below. [ABSTRACT FROM AUTHOR]

Text-based emotion prediction is an important task in the field of affective computing. Most prior work has been restricted to predicting emotions corresponding to a few high-level emotion classes. This paper explores and experiments with various techniques for fine-grained (27 classes) emotion prediction $^\dagger$ † . In particular, 1) we present a method to incorporate multiple annotations from different raters, 2) we analyze the model's performance on fused emotion classes and with sub-sampled training data, 3) we present a method to leverage the correlations among the emotion categories, and 4) we propose a new framework for text-based fine-grained emotion prediction through emotion definition modeling. The emotion definition-based model outperforms the existing state-of-the-art for fine-grained emotion dataset GoEmotions. The approach involves a multi-task learning framework that models definitions of emotions as an auxiliary task while being trained on the primary task of emotion prediction. We model definitions using masked language modeling and class definition prediction tasks. We show that this trained model can be used for transfer learning on other benchmark datasets in emotion prediction with varying emotion label sets, domains, and sizes. The proposed models outperform the baselines on transfer learning experiments demonstrating the model's generalization capability. [ABSTRACT FROM AUTHOR]

This work presents a single-use, disposable, paper sensor for blood urea measurement, directly from whole-blood sample. The sensor is compact, inexpensive and can be used even by an unskilled person. It is a part of a small, hand-held meter which adopts an impedimetric sensing technology using graphite electrodes. Here, the urea concentration of the blood is estimated in five different levels from the measured impedance of the whole-blood sample. Only, 0.2 mL of blood is required for each measurement and the response time is only 30 s. The look up table and pre-calibration are discussed here with detailed experimental results and analysis. The work also discusses the design of an electronic interface and an embodiment to clamp and hold the sensor in the measuring platform. The proposed prototype of blood-urea meter will provide a point-of-care diagnosis and monitoring for the house-held patient. [ABSTRACT FROM AUTHOR]

This paper focuses on the in-depth analysis of the physical mechanism of the broadband frequency domain spectroscopy of oil-paper insulation. The complex permittivity shows two low-frequency dispersion phenomena, and the real part of the complex conductivity exhibits two slope changes under the synergistic effect of aging and moisture. The two slope changes are mainly attributed to the migration characteristics of water molecules and polar aging products with a degradation of the oil-paper insulation, resulting in the transition of the frequency dependence between the DC conductivity and AC conductivity. When the oil-paper insulation deteriorates seriously, the imaginary part of the dielectric modulus shows double relaxation peaks. Physical models of parasitic capacitance and molecular polarity interface polarization are proposed to reveal the physical mechanism of the double relaxation peaks. The left peak of the double relaxation peaks is caused by the accumulation of water molecules and polar aging products at the electrode/insulation interface, and the right peak is due to the charge disturbance of the cellulose molecular chain surface. This paper can provide theoretical support and technical guidance for the condition diagnosis and evaluation of oil-paper insulation. [ABSTRACT FROM AUTHOR]

Selective detection and quantification of biomarkers related to human diseases are essential for preventive healthcare. Surface-enhanced Raman scattering (SERS) spectroscopy is a powerful analytical tool offering high sensitivity. However, the success of this promising analytical tool relies on the ability to effectively fabricate SERS substrate. Herein we have demonstrated a plasmonic paper-based flexible substrate (PPFS) for SERS sensing. In situ growth of silver nanostructures (AgNS) on the paper-based substrate was achieved by using a simple one-step silver mirror reaction (SMR). FESEM and TEM results depicts that the increasing silver ion content influences the morphology (growth of multifacets), as well as size of AgNS. Further, the PPFS substrate was tested with Rhodamine-6G (Rh-6G) dye and an attomole sensitivity with a LOD of 4.54 $\times \,\,10^{-{18}}$ M was achieved. Further, two biomarkers, lactic acid (LA) and uric acid (UA) were detected on the PPFS substrate, with ${\mu }\text{M}$ and pM sensitivity, having LOD values of 0.6 $\times \,\,10^{-{6}}$ and 0.3 $\times \,\,10^{-{12}}$ M respectively. Above detection levels for UA on PPFS is two orders better than reported values, whereas for LA it is comparable with reported substrates. Finally, UA, LA and their mixtures were tested on PPFS and results compared with commercial substrate. The performance of PPFS were found better in all cases, thus, multifaceted AgNS paper based PPFS offers the potential to be used as a biosensor for detection of various biomarkers from body fluids, responsible for the detection of the critical disease for preventive health care. [ABSTRACT FROM AUTHOR]

It is now possible to detect dangerous arcing faults in the paper insulation of transformers and distinguish them from arcing faults in oil, using new sub-zones of Duval Pentagons 1 and 2. [ABSTRACT FROM AUTHOR]

Frequency-domain dielectric spectroscopy (FDS) is widely used in the insulation assessment of oil-immersed power equipment, and the moisture in insulating paper could be qualitatively predicted by mathematical approximation. However, due to the still lots unexplored underlying mechanisms, until now, there is still no convincing and physical method for the diagnosis of moisture content. With the aid of spectral decomposition methods, this article aims to explore the effects and mechanism of moisture on the independent dielectric response spectra. First, the measured dielectric response curve is unfolded and reconstructed by derivation of the real part curve. Second, the independent polarization and conductance spectra of damp samples are extracted, and then, the relationships between moisture content in insulating paper and spectral parameters are established quantitatively. The results show that moisture introduces a new polarization in the midfrequency region at room temperature, and both polarization and conductance increase with the moisture content. The multiple adsorption model and the proton exchange model of moisture are employed to illustrate the changes of the polarization and the conductance spectra, respectively. This research provides a methodological reference for the moisture assessment of oil–paper insulation. [ABSTRACT FROM AUTHOR]