ENBIS: European Network for Business and Industrial Statistics
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ENBIS18 in Nancy
2 – 25 September 2018; Ecoles des Mines, Nancy (France) Abstract submission: 20 December 2017 – 4 June 2018The following abstracts have been accepted for this event:

R2R Control in Semiconductor Manufacturing Based on Gaussian Bayesian Network
Authors: WeiTing Yang (École des Mines de SaintÉtienne), Jakey Blue (École des Mines de SaintÉtienne), Agnès Roussy (École des Mines de SaintÉtienne), Marco S. Reis (University of Coimbra), Jacques Pinaton (STMicroelectronics)
Primary area of focus / application: Process
Secondary area of focus / application: Mining
Keywords: RuntoRun (R2R), Fault Detection and Classification (FDC), Wafer metrology, Equipment condition, Gaussian Bayesian Network (GBN)
Submitted at 23Apr2018 11:02 by WeiTing Yang
Accepted
For this purpose, we employ the Gaussian Bayesian Network (GBN) to analyze the implicit relationship not only between the control factors and process parameters but also among the process parameters and metrology. With Gaussian Bayesian Network, the causeeffect between these variables can be explicitly expressed by means of a connected graph; consequently, the variation of process parameters caused by control factor can be estimated and the corresponding predicted metrology can be obtained simultaneously. Based on this model, we are able to consider process control in a more global view.
The effectiveness of this approach is demonstrated and validated using the case study, in collaboration with our industrial partner. 
Direct Policy Search: An Introduction
Authors: Jérôme Collet (EDF R&D)
Primary area of focus / application: Process
Secondary area of focus / application: Modelling
Keywords: Stochastic optimization, Direct policy search, Linear decision rules, Binary decision trees
This bypassing can fulfil the following goals: reduce computational burden (regarding memory use or processing time), optimize in a multiobjective setting (since some stochastic optimization methods are inherently singleobjective), or avoid the modelling of a poorly known stochastic process.
EDF is now facing new energy management problems, in small energy storages (home energy storages, electric vehicles), with poorly known demand processes, so our goal is to bypass its modelling.
Research on “Direct Policy Search” is increasingly active since the 2000s, so we propose a survey on recent papers. Then, we focus on two policy sets: Linear Decision Rules, and Binary Decision Trees. We show in details its use on small examples, both theoretical and real, and compare the results we obtained. 
Stepwise Multiblock Latent Variable Regression
Authors: Maria Campos (University of Coimbra, Department of Chemical Engineering), Marco Reis (University of Coimbra, Department of Chemical Engineering)
Primary area of focus / application: Modelling
Secondary area of focus / application: Process
Keywords: Regression methods, Multiblock methods, SOPLS, Stepwise SOPLS, Big data
Submitted at 24Apr2018 18:41 by Maria Campos
Accepted

Metrology Modelling Based on Process Parameters in Semiconductor Manufacturing
Authors: Aabir Chouichi (Ecole des Mines de SaintEtienne ( Campus George Charpak )), Jakey Blue (Ecole des Mines de SaintEtienne ( Campus George Charpak )), Claude Yugma (Ecole des Mines de SaintEtienne ( Campus George Charpak )), Francois Pasqualini (STMicroelectronics)
Primary area of focus / application: Mining
Secondary area of focus / application: Metrology & measurement systems analysis
Keywords: Sensor signal analytics, Fault detection and classification, Advanced process control, Virtual metrology
Submitted at 25Apr2018 09:55 by Aabir CHOUICHI
Accepted
Apart from monitoring the equipment/process faults, machine signals and wafer metrology can be used together to quantify the relationship between the recipe setup and the metrology measurements. This is especially important given the fact that the leading edge manufacturing technology demands to reduce the measuring time as much as possible. Consequently, our research aims at modeling the link between machine parameters and metrology data. The predicted metrology can be used as the measured features in the daily routines of the process control. The obtained results are further validated in the industrial environment for potential implementation in real practice. 
Investigation of MultiRun Independent Component Analysis on Simulated Semiconductor Data
Authors: Anja Zernig (KAI  Kompetenzzentrum Automobil und Industrieelektronik GmbH)
Primary area of focus / application: Reliability
Secondary area of focus / application: Quality
Keywords: Semiconductor industry, Independent Component Analysis, Multirun ICA, Reliability
Submitted at 25Apr2018 10:29 by Anja Zernig
Accepted
Ideally, the pattern of one Frontend process step is directly visible in one wafermap of the measurement data. However, often patterns are distributed over various measurements and hence they are only visible if these measurements are evaluated together. To unhide latent patterns in multivariate semiconductor data, Independent Component Analysis (ICA) is proposed. ICA is applied to a selected set of measurement data. After the ICA transformation a set of sources is given that are maximum independent of each other. To calculate the ICA transformation, the fastICA algorithm invented and implemented by A. Hyvärinen [1] is used. Like for any stochastic optimization procedure, also for the ICA the outcome depends on the starting condition. Since the “best” starting condition cannot be determined, remedy provides a repetition of the algorithm, called multirun ICA [2].
Multirun ICA consists of the following calculation steps. The fastICA algorithm is performed several times with different starting conditions. This leads to different source matrices, where in a next step the most reliable sources need to be identified. To this end, the individual matrices are combined to one large matrix. Then, hierarchical clustering is used together with a distance measure and a fixed cluster number, which is equal to the number of sources per run. With this method the best combination of sources can be identified and used for further analysis.
For the evaluation of the multirun ICA, simulated data of semiconductor wafermaps have been investigated. Since the simulated wafermaps are constructed independent from each other and are linearly mixed, only minor influences of varying starting conditions can be observed. Generally, this cannot be stated for real semiconductor measurement data because real data never exactly follow the ICA model. For this reason, applying multirun ICA can prevent from being fooled by a suboptimal solution.
[1] A. Hyvärinen, J. Karhunen and E. Oja. Independent Component Analysis. John Wiley & Sons, 2001
[2] J. Himberg and A. Hyvaerinen, "Icasso: software for investigating the reliability of ICA estimates by clustering and visualization," 2003 IEEE XIII Workshop on Neural Networks for Signal Processing, 2003, pp. 259268. 
Parsimonious Batch Data Analysis
Authors: Marco P. Seabra dos Reis (Department of Chemical Engineering, University of Coimbra)
Primary area of focus / application: Modelling
Secondary area of focus / application: Process
Keywords: Batch processes, Process monitoring, Quality prediction, Feature oriented analysis, Multiresolution modeling
Submitted at 25Apr2018 12:45 by Marco P. Seabra dos Reis
Accepted
The current standard method to deal with these datasets requires synchronization (e.g. using dynamic time warping) and batchwise unfolding (BWU) of the 3way array into a 2way array (I×(J×K)) [1, 2]. Synchronization is not a trivial task and the unfolding step usually originates a wide matrix with hundreds or thousands of pseudovariables, leading to overparametrized models where the potential for overfitting is high.
The aforementioned approaches for batch data analysis rank high in terms of complexity, both in terms of “estimation complexity” as well as of “implementation complexity”. These two dimensions have consequences on the performance of the methods (accuracy, robustness) and on the tangibility of their impact in industry. Recently, alternative parsimonious formulations for batch data analysis have been developed, presenting lower complexity features in both dimensions. Examples include the class of featureoriented approaches and multiresolution methodologies. This talk summarizes these new developments on batch data analysis, and demonstrate their benefits with several illustrative examples.
References
1. Nomikos, P. and J.F. MacGregor, Monitoring batch processes using multiway principal component analysis. AIChE Journal, 1994. 40(8): p. 13611375.
2. GonzálezMartínez, J.M., et al., Effect of Synchronization on Bilinear Batch Process Modeling. Industrial & Engineering Chemistry Research, 2014. 53: p. 43394351.