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Discovery of Resource-Oriented Transition Systems for Yield Enhancement in Semiconductor Manufacturing
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/bimj.200810425openalex-snapshot
Simultaneous Inference in General Parametric Models
inferredcites →https://doi.org/10.1002/qre.1173openalex-snapshot
Addressing multicollinearity in semiconductor manufacturing
inferredcites →https://doi.org/10.1007/s00170-016-9490-7openalex-snapshot
A comprehensive study of linear variation propagation modeling methods for multistage machining processes
inferredcites →https://doi.org/10.1007/s10696-012-9161-4openalex-snapshot
Semiconductor fault detection and classification for yield enhancement and manufacturing intelligence
inferredcites →https://doi.org/10.1007/s10845-013-0791-5openalex-snapshot
An empirical study of design-of-experiment data mining for yield-loss diagnosis for semiconductor manufacturing
inferredcites →https://doi.org/10.1016/j.asoc.2017.11.034openalex-snapshot
Bayesian inference for mining semiconductor manufacturing big data for yield enhancement and smart production to empower industry 4.0
inferredcites →https://doi.org/10.1016/j.ejor.2005.11.032openalex-snapshot
Model-based clustering for integrated circuit yield enhancement
inferredcites →https://doi.org/10.1016/j.eswa.2006.04.014openalex-snapshot
Data mining for yield enhancement in semiconductor manufacturing and an empirical study
inferredcites →https://doi.org/10.1016/j.is.2010.09.001openalex-snapshot
Time prediction based on process mining
inferredcites →https://doi.org/10.1016/j.jesp.2013.03.013openalex-snapshot
Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median
inferredcites →https://doi.org/10.1016/j.procir.2014.02.001openalex-snapshot
Service Innovation and Smart Analytics for Industry 4.0 and Big Data Environment
inferredcites →https://doi.org/10.1039/c5ra07164jopenalex-snapshot
An empirical study-based state space model for multilayer overlay errors in the step-scan lithography process
inferredcites →https://doi.org/10.1049/cce:20040105openalex-snapshot
Geometry unifies process control, production control and alarm management
inferredcites →https://doi.org/10.1080/07408170600899532openalex-snapshot
Knowledge discovery from observational data for process control using causal Bayesian networks
inferredcites →https://doi.org/10.1109/66.400999openalex-snapshot
Prediction of wafer state after plasma processing using real-time tool data
inferredcites →https://doi.org/10.1109/asmc.2015.7164425openalex-snapshot
Predictive Maintenance in semiconductor manufacturing
inferredcites →https://doi.org/10.1109/icivc.2017.7984728openalex-snapshot
State space model and numerical simulation of overlay error for multilayer overlay lithography processes
inferredcites →https://doi.org/10.1109/issm.2016.7934507openalex-snapshot
Data mining approaches to optimize the allocation of production resources in semiconductor wafer fabrication
inferredcites →https://doi.org/10.1109/tase.2006.876617openalex-snapshot
Stream-of-Variation (SoV)-Based Measurement Scheme Analysis in Multistation Machining Systems
inferredcites →https://doi.org/10.1109/tra.2003.808852openalex-snapshot
State space modeling of dimensional variation propagation in multistage machining process using differential motion vectors
inferredcites →https://doi.org/10.1109/tsm.2010.2041294openalex-snapshot
Yield Management Enhanced Advanced Process Control System (YMeAPC)—Part I: Description and Case Study of Feedback for Optimized Multiprocess Control
inferredcites →https://doi.org/10.1109/tsm.2011.2118775openalex-snapshot
Cycle-Time Key Factor Identification and Prediction in Semiconductor Manufacturing Using Machine Learning and Data Mining
inferredcites →https://doi.org/10.1109/tsm.2015.2445320openalex-snapshot
Advanced Semiconductor Manufacturing Using Big Data
inferredcites →https://doi.org/10.1115/1.2833137openalex-snapshot
State Space Modeling of Sheet Metal Assembly for Dimensional Control
inferredcites →https://doi.org/10.2202/1544-6115.1027openalex-snapshot
Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments
inferredcites →https://doi.org/10.5812/ijem.3505openalex-snapshot
Normality Tests for Statistical Analysis: A Guide for Non-Statisticians
inferredcites →https://doi.org/10.5860/choice.47-3859openalex-snapshot
Nonparametric statistics for non-statisticians: a step-by-step approach
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4690886openalex-extracted
Process Mining in Manufacturing and Logistics: A Systematic Mapping and New Taxonomy Proposal
inferred← citeshttps://doi.org/10.1007/978-3-031-31007-2_40openalex-snapshot
A Data-Driven Methodology for Analyzing Field Nonconformities in Semiconductor Production Applied to a Brazilian Manufacturer
inferred← citeshttps://doi.org/10.1007/978-3-031-56107-8_8openalex-snapshot
Turning Logs into Lumber: Preprocessing Tasks in Process Mining
inferred← citeshttps://doi.org/10.1007/s00170-025-15029-5openalex-snapshot
Manufacturing process analysis framework for process mining: case study of fully automated factory applications
inferred← citeshttps://doi.org/10.1007/s00170-026-18104-7openalex-snapshot
Temporal-enhanced semiconductor yield prediction by combining equipment usage data and accumulated cycle time data
inferred← citeshttps://doi.org/10.1016/j.cie.2024.109934openalex-snapshot
Semiconductor industry's transformation path under circular economy: Evidence from micro-level efficiency evaluation
inferred← citeshttps://doi.org/10.1016/j.datak.2026.102601openalex-snapshot
An optimized backbone-based process layout generation method using integer programming and heuristics to enhance user comprehension
inferred← citeshttps://doi.org/10.1016/j.eswa.2025.126480openalex-snapshot
Development of a suspiciousness measure with reduced redundancy for screening machine sets in serial-parallel multistage manufacturing processes
inferred← citeshttps://doi.org/10.1080/0951192x.2022.2090024openalex-snapshot
Exploiting a combined process mining approach to enhance the discovery and analysis of support processes in manufacturing
inferred← citeshttps://doi.org/10.1109/access.2026.3665663openalex-snapshot
HAN-Semi: A Hierarchical Attention Network for Yield Prediction Based on Production Resources in Semiconductor Manufacturing
inferred← citeshttps://doi.org/10.1109/asmc57536.2023.10121092openalex-snapshot
Graphical approach of equipment health monitoring using network analysis
inferred← citeshttps://doi.org/10.1109/edtm55494.2023.10102976openalex-snapshot
Graphical approach of equipment health monitoring using network analysis
inferred← citeshttps://doi.org/10.1109/tsm.2026.3673232openalex-snapshot
Comprehensive Framework for Identifying and Visualizing Key Yield Factors in Semiconductor Manufacturing
inferred← citeshttps://doi.org/10.3390/app11125730openalex-snapshot
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