A Data Driven Cycle Time Prediction With Feature Selection in a Semiconductor Wafer Fabrication System

Semiconductor wafer manufacturing is among the most complex manufacturing processes, and hence a capital-intensive endeavor. The most common non cost-related performance measures for a semiconductor manufacturing facility are machine utilization, production yield, throughput, and last but not least cycle time. We define cycle time as the time a lot of wafers needs to travel through the semiconductor wafer manufacturing process. The overall cycle time of a lot of wafers is the sum of the total processing times of a wafer, waiting times for all resources (such as operators, machines, tools, transportation system) needed for each processing step to become available, times elapsed during measurement and quality inspections, and the time the lot has spent in transportation.

Estimating the cycle time of each job in a wafer fabrication factory is a critical task to every wafer manufacturer. In recent years, a number of hybrid approaches based on job classification (either preclassification or postclassification) for cycle time estimation have been proposed. However, the problem with these methods is that the input variables are not independent. In order to solve this problem, principal component analysis (PCA) is considered useful. In this study, a classifying fuzzy-neural approach, based on the combination of PCA, fuzzy c-means (FCM), and back propagation network (BPN), is proposed to estimate the cycle time of a job in a wafer fabrication factory. Since job classification is an important part of the proposed methodology, a new index is proposed to assess the validity of the classification of jobs. The empirical relationship between theSvalue and the estimation performance is also found. Finally, an iterative process is employed to deal with the outliers and to optimize the overall estimation performance. A real case is used to evaluate the effectiveness of the proposed methodology. Based on the experimental results, the estimation accuracy of the proposed methodology was significantly better than those of the existing approaches.

Job cycle time is the cycle time of a job or the time required to complete a job. Prediction of job cycle time is a critical task for a semiconductor fabrication factory. A predictive model must forecast job cycle time to pursue sustainable development, meet customer requirements, and promote downstream operations. To effectively predict job cycle time in semiconductor fabrication factories, we propose an effective hybrid approach combining the fuzzy c-means (FCM)-based genetic algorithm (GA) and a backpropagation network (BPN) to predict job cycle time. All job records are divided into two datasets: the first dataset is for clustering and training, and the other is for testing. An FCM-based GA classification method is developed to pre-classify the first dataset of job records into several clusters. The classification results are then fed into a BPN predictor. The BPN predictor can predict the cycle time and compare it with the second dataset. Finally, we present a case study using the actual dataset obtained from a semiconductor fabrication factory to demonstrate the effectiveness and efficiency of the proposed approach.

This paper presents an accurate machining feedrate prediction technique by modelling the trajectory generation behaviour of modern CNC machine tools. Typically, CAM systems simulate machines’ motion based on the commanded feedrate and the path geometry. Such approach does not consider the feed planning and interpolation strategy of the machine’s numerical control (NC) system. In this study, trajectory generation behaviour of the NC system is modelled and accurate cycle time prediction for complex machining toolpaths is realised. NC system’s linear interpolation dynamics and commanded axis kinematic profiles are predicted by using finite impulse response (FIR)–based low-pass filters. The corner blending behaviour during non-stop interpolation of linear segments is modelled, and for the first time, the minimum cornering feedrate that satisfies both the tolerance and machining constraints has been calculated analytically for 3-axis toolpaths of any geometry. The proposed method is applied to 4 different case studies including complex machining toolpaths. Experimental validations show actual cycle times can be estimated with > 90% accuracy, greatly outperforming CAM-based predictions. It is expected that the proposed approach will help improve the accuracy of virtual machining models and support businesses’ decision-making when costing machining processes.

An improved Genghis Khan optimizer based on enhanced solution quality strategy for global optimization and feature selection problems

A variety of real-world data and networks can be described by a heavy-tailed probability distribution of its values, vertex degrees, or other significant properties, that follows the power law. Such a scale-free data and networks can be found in both natural phenomena such as protein interaction networks and gene regulation networks and man-made structures like the Internet, language, and various social networks. An efficient analysis of large scale data and networks is often impractical and various heuristic and metaheuristc sampling techniques are deployed to select smaller subsets of the data for analysis and visualisation. A key goal of data and network sampling is to select such a subset of the original data that would accurately represent the original data with respect to selected attributes. In this work we propose a novel genetic algorithm for scale-free data and network sampling and evaluate the algorithm in a series of computational experiments.

Web 上の大規模データの解析による Moon & Spencer の色彩調和論における「美度」の妥当性の検証

Graph analytics has emerged as an important tool in the analysis of large scale data from diverse application domains such as social networks, cyber security and bioinformatics. Counting the number of triangles in a graph is a fundamental kernel with several applications such as detecting the community structure of a graph or in identifying important vertices in a graph. The ubiquity of massive datasets is driving the need to scale graph analytics on parallel systems. However, numerous challenges exist in efficiently parallelizing graph algorithms, especially on distributed-memory systems. Irregular memory accesses and communication patterns, low computation to communication ratios, and the need for frequent synchronization are some of the leading challenges. In this paper, we present TriC, our distributed-memory implementation of triangle counting in graphs using the Message Passing Interface (MPI), as a submission to the 2020 Graph Challenge competition. Using a set of synthetic and real-world inputs from the challenge, we demonstrate a speedup of up to 90 x relative to previous work on 32 processor-cores of a NERSC Cori node. We also provide details from distributed runs with up to 8192 processes along with strong scaling results. The observations presented in this work provide an understanding of the system-level bottlenecks at scale that specifically impact sparse-irregular workloads and will therefore benefit other efforts to parallelize graph algorithms.

Automated video and image analysis can be a very efficient tool to behavior study, especially in hard access environments for researchers. The understanding of this social behavior can play a key role in the sustainable design of control policies of many species. This paper proposes the use of computer vision algorithms to identify and track, the Norway lobster, Nephrops norvegicus, a burrowing decapod with relevant commercial value which is captured by trawling. These animals can only be captured when are engaged in seabed excursions, which are strongly related with their social behavior. This emergent behavior is modulated by the day-night cycle, but social interactions remain unknown to the scientific community. The paper introduces an identification scheme made of four distinguishable black and white tags (geometric shapes). The project has recorded 15-day experiments in laboratory, under monochromatic blue light (472 nm.) and darkness conditions (recorded using Infra Red light). Using this massive image set, we propose a comparative of state-of-the-art computer vision algorithms to distinguish and track the different animals' movements. We evaluate the robustness to the high noise presence in the infrared video signals and free out-of-plane rotations due to animal movement. The experiments show promising accuracies under a cross-validation protocol, being adaptable to the automation and analysis of large scale data. In a second contribution, we created an extensive dataset of shapes (46027 different shapes) from four daily experimental video recordings, which will be available to the community.

The analysis of large scale data logged from complex cyber-physical systems, such as microgrids, often entails the discovery of invariants capturing functional as well as operational relationships underlying such large systems. We describe a latent factor approach to infer invariants underlying system variables and how we can leverage these relationships to monitor a cyber-physical system. In particular we illustrate how this approach helps rapidly identify outliers during system operation.

An ANN-based load model for fast transient stability calculations

Defining transition rules is an important issue in cellular automaton (CA)-based land use modeling because these models incorporate highly correlated driving factors. Multicollinearity among correlated driving factors may produce negative effects that must be eliminated from the modeling. Using exploratory regression under pre-defined criteria, we identified all possible combinations of factors from the candidate factors affecting land use change. Three combinations that incorporate five driving factors meeting pre-defined criteria were assessed. With the selected combinations of factors, three logistic regression-based CA models were built to simulate dynamic land use change in Shanghai, China, from 2000 to 2015. For comparative purposes, a CA model with all candidate factors was also applied to simulate the land use change. Simulations using three CA models with multicollinearity eliminated performed better (with accuracy improvements about 3.6%) than the model incorporating all candidate factors. Our results showed that not all candidate factors are necessary for accurate CA modeling and the simulations were not sensitive to changes in statistically non-significant driving factors. We conclude that exploratory regression is an effective method to search for the optimal combinations of driving factors, leading to better land use change models that are devoid of multicollinearity. We suggest identification of dominant factors and elimination of multicollinearity before building land change models, making it possible to simulate more realistic outcomes.

This study proposes content and interaction analysis techniques for a large repository created from social media content. Though we have presented our study for a large platform dedicated to discussions around financial topics, the proposed methods are generic and applicable to all platforms. Along with an extension of topic extraction method using Latent Dirichlet Allocation, we propose a few measures to assess user participation, influence and topic affinities specifically. Our study also maps user-generated content to components of behavioral finance. While these types of information are usually gathered through surveys, it is obvious that large scale data analysis from social media can reveal many potentially unknown or rare insights. Characterising users based on their platform behavior to provide critical insights about how communities are formed and trust is established in these platforms using graphical analysis is also studied.

The BETTER4U project is an EU-funded project (GAP: 101080117) producing a coordinated sequence of publications that together aim to advance the scientific underpinnings of personalized obesity prevention and management. This collection seeks to elucidate how biological and behavioral tailored products, supported by large scale data analyses, innovative assessment tools, and newly emerging digital and artificial intelligence (AI)-enabled technologies, could transform current paradigms in obesity research and care. The series will spell out the objective, the scope, and the scientific rationale of this collective effort to showcase the transformative potential of personalization and its critical importance in achieving meaningful and equitable, as well as scalable, impact. Across the series, BETTER4U aims to define the parameters of personalization in obesity research. This series makes the case for joining forces between diverse scientific sectors, namely, genomics, metabolomics, microbiome, behavior, digital health, health economics and population level analytics, to accelerate the delivery of personalized obesity prevention and management. The contributions identify gaps and highlight findings in the effective communication of personalized risk, understanding heterogeneity and vulnerability, real-time monitoring tools and the need for stronger economic evaluations to guide policy and practice. Through identification and mapping of these gaps, the series sets out a forward-looking research agenda that could enable the development of the next generation of personalized interventions. Together, the papers outline a roadmap for future research and implementation that can support the emergence of a more precise and context-sensitive standard of care. We are pleased to partner with Obesity Reviews in this endeavor. In addition to the core BETTER4U manuscripts, the Special Issue will also feature a number of papers submitted in response to a dedicated Call for Papers under the banner of “Personalized obesity prevention and management.” These contributions, appearing in upcoming issues of Obesity Reviews, will further enrich the scientific dialogue and broaden the evidence base supporting personalization in obesity research and care. The series begins with the strategic framework paper, Advancing Personalized Strategies for Obesity Prevention Through Biologically and Behaviorally Tailored Interventions, which explains the conceptual basis of BETTER4U and the scientific pillars of the initiative. Following pieces each take on interrelated aspects of the various personalization elements. One examines the effectiveness of behavior change strategies for communicating genetic risk, offering insights into how individuals interpret and act upon personalized genetic information. Another meta-review identifies the behavior change techniques consistently used in effective interventions for vulnerable populations, showing how personalization can support equity rather than amplify disparity. Additionally, a comprehensive review of microbiome and metabolomics research synthesizes emerging evidence across the lifespan and explores its implications for tailored interventions. Economic considerations are addressed through a systematic review of the cost-effectiveness of personalized lifestyle interventions, clarifying when personalization may offer added value. Furthermore, methodological innovation is represented by the introduction of the BETTER4U indicators, novel tools for unobtrusive, scalable assessment of behavioral and environmental determinants relevant to obesity risk. Moreover, the BETTER4U Database manuscript demonstrates the power of integrating harmonized data from 14 countries and over 1.1 million participants, enabling unprecedented cross-national analyses of obesity-related determinants. Further empirical depth is provided by an individual-level meta-analysis across 21 BETTER4U cohorts, examining associations between BMI, lifestyle factors, and mental health in vulnerable contexts. Finally, the clinical trial protocol for the BETTER4All personalized intervention illustrates how AI-driven recommendations and real-time monitoring can be operationalized in a multicenter trial, translating the project's conceptual and methodological innovations into practice. Collectively, these results represent a well-integrated scientific plan with predictive capacity. Through its examination of the biological, behavioral, environmental, technological, and economic dimensions of obesity prevention, the BETTER4U series demonstrates how complementary lines of inquiry can work collectively toward a new standard of care, one that is data-driven, equitable and responsive across the life course. The series papers are united by a common view; to help the world move away from one size fits all approaches to tailored approaches that can achieve meaningful and scalable impact. The BETTER4U project has received funding from the European Union's Horizon Europe Research and Innovation program under Grant Agreement no. 101080117. Additionally, BETTER4U is funded by UK Research and Innovation (UKRI) under the UK government's Horizon Europe funding guarantee (grant number 10093560 for QMUL and 10106435 for BiB) and by the Swiss State Secretariat for Education, Research and Innovation (SERI). The authors declare no conflicts of interest.

The detection of useful patterns in large datasets has attracted considerable interest recently. The hierarchical K-means clustering algorithm (HKM) is very efficient in large scale data analysis. It has been extensively used for building visual vocabulary in large scale image/video retrieval. However, the accuracy and speed of HKM still have room for improvement. In this paper, we propose a Parallel N-path labeling HKM clustering algorithm (PNHKM) which improves on the HKM clustering algorithm in the following ways. Firstly, we adopt a Greedy N-best Paths Labeling (GNPL) method to improve the clustering accuracy. Secondly, we focus on developing a parallel clustering algorithm for multicore processors. Our results confirm that the PNHKM is much faster and more effective.