Big Data and Predictive Analytics Methods for Modeling and Analysis of Semiconductor Manufacturing Processes

Butte, Sujata; Patil, Sainath

doi:10.1109/wmed.2016.7458273

Cited by 9 publications

(4 citation statements)

References 18 publications

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“…The classification also provides a useful perspective for better understanding and also evaluating many learning algorithms. For hypothesis, this will helps to determine exact probabilities and also it is robust to noise in input data [11].…”

Section: Naïve Bayesmentioning

confidence: 99%

Predictive Analytic on Human Resource Department Data Based on Uncertain Numeric Features Classification

Huda¹,

Ardi²

2021

Int. J. Interact. Mob. Technol.

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Business Intelligence is very popular and useful for a better understanding of business progress these days, and there are many different methods or tools being used in Business Intelligence. It uses combination of artificial intelligence, data mining, math, and statistic to gain better understanding and insight on the business process performance. As employees have an important role in business process, the desire to have a tool for classifying and predicting their wages are desirable. In this research, we tried to analyzed dataset from Human Resource Department, and this dataset can be used to analyst the data in order to draw a conclusion about whether any employees would prematurely leave the company, and then, a preventive action based on those parameters can be proposed. This is a kind of predictive analytic system which bases on Naïve Bayes, and it can predict whether an employee would leave or stay according to his or her characteristics. But the Naïve Bayes itself does not enough. So we develop a way to solve the problem using uncertain Numeric features classification on it. The accuracy of the result is depended on the amount and effectiveness of the training sets.

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Section: Naïve Bayesmentioning

confidence: 99%

Predictive Analytic on Human Resource Department Data Based on Uncertain Numeric Features Classification

Huda¹,

Ardi²

2021

Int. J. Interact. Mob. Technol.

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“…The widely used approaches from the reviewed articles include regression [34,44,45,83,86,89,110,126,127], analysis of variance (ANOVA) [27,79], GA [55,83], Las Vegas filter [60,78], Pearson coefficient [79], Cramer's V correlation coefficients [85,87], and so on. Clustering [38,90,101,126], aggregation [34,87], and sampling [82] based approaches were applied to reduce the data numerosity. PCA or the modified PCA [64,65,83,92,94], and multi-dimensional scaling [84] were employed to compress the representation of the original data.…”

Section: Data Handlingmentioning

confidence: 99%

“…One of the research papers deals with predicting the performance (yield) of a manufacturing process or system in terms of critical functional characteristics. Months may pass before a chip is completed; hence, there is a great interest in mining production data to predict its performance prior to the final testing of the wafers [100][101][102][103][104][105][106][107][108]. In order to infer to the possible causes of faults and manufacturing process variations in semiconductor manufacturing after the whole fab process is completed, the clustering, classification, and association analyses are conducted based on different DMTs such as k-means, SOM, SVM, and decision tree to identify critical poor yield factors and determine the root cause of low yield.…”

Section: Application Of Dm and Big Data For Productionmentioning

confidence: 99%

“…In common, these data are structurally stored in ERP, MES, and DCS that can be used for the optimization of process control such as AEC/APC and production management. Taking an example from wafer fab, the equipment status data such as chamber pressure, gases flow, and chuck temperature are collected in real time by sensors placed on tools, and valuable data that are generated from clean room environment monitoring[101] 3. Production plan and arrangement data: These data include the plan of the project, the hierarchy production plan, the inventory/material and procurement plan, and scheduling that defines when and how many products have to be manufactured.…”

mentioning

confidence: 99%

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A Review of Data Mining with Big Data towards Its Applications in the Electronics Industry

Kim

Zheng

et al. 2018

Applied Sciences

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Data mining (DM) with Big Data has been widely used in the lifecycle of electronic products that range from the design and production stages to the service stage. A comprehensive analysis of DM with Big Data and a review of its application in the stages of its lifecycle will not only benefit researchers to develop strong research themes and identify gaps in the field but also help practitioners for DM application system development. In this paper, a brief clarification of DM-related topics is presented first. A flowchart of DM and the main content of the flowchart steps are given in which commonly used data preparation and preprocessing approaches, DM functions and techniques, and performances indicators are summarized. Then, a comprehensive review covering 105 articles from 2007 to 2017 on DM or Big Data applications in the electronics industry is provided according to the flowchart from various points of view such as data handling, applications of DM, or Big Data at different lifecycle stages, and the software used in the applications. On this basis, a diagram of data content for different knowledge areas and a framework for DM and Big Data applications in the electronics industry are established. Finally, conclusions and future research directions are given.

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Introduction to Wafer Tomography: Likelihood-Based Prediction of Integrated-Circuit Yield

Baron

Yashchin

Takken

2022

Artificial Intelligence, Big Data and Data Science in Statistics

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Big Data and Predictive Analytics Methods for Modeling and Analysis of Semiconductor Manufacturing Processes

Cited by 9 publications

References 18 publications

Predictive Analytic on Human Resource Department Data Based on Uncertain Numeric Features Classification

Predictive Analytic on Human Resource Department Data Based on Uncertain Numeric Features Classification

A Review of Data Mining with Big Data towards Its Applications in the Electronics Industry

Introduction to Wafer Tomography: Likelihood-Based Prediction of Integrated-Circuit Yield

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