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ZENODO
Dataset . 2021
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2021
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2021
License: CC BY
Data sources: Datacite
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Data Set of Existing Summary Statistics from Equipment Sensor Data

Authors: Pleschberger, Martin;

Data Set of Existing Summary Statistics from Equipment Sensor Data

Abstract

This data set was generated in accordance with the semiconductor industry and contains sensor recordings from high-precision and high-tech production equipment. Basically, the semiconductor production consists of hundreds of process steps performing physical and chemical operations on so-called wafers, i.e. slices based on semiconductor material. Typically, bunches of wafers are aggregated into so-called lots of size 25, which always pass through the same operations in the production chain. In the production chain, each process equipment is equipped with several sensors recording physical parameters like gas flow, temperature, voltage, etc., resulting in so-called sensor data recorded during each process step. Out of these time-dependent sensor data, so called key numbers (KNs) are extracted using a certain time frame in the individual sensor recordings judged by experts to be important for the process. To keep the entire production as stable as possible, the KNs are used in order to intervene in case of deviations. After the production, each device on the wafer is tested in the most careful way resulting in so-called wafer test data. In some cases, suspicious patterns occur in the wafer test data potentially leading to failure. In this case the root cause must be found in the production chain. For this purpose, the given KNs are provided. The aim is to find correlations between the wafer test data and the KNs in order to identify the root cause. The given data is divided into three data sets: "process1.csv", "process2.csv" and "response.csv". "process1.csv" and "process2.csv" represent the extracted KNs from two process equipment. The "response.csv" data set contains the corresponding wafer test data. For the unique identification, the first two columns in each data set are the lot number and the wafer number respectively. The exact column structure is given as follows: for "process1.csv" and "process2.csv": lot: the lot number wafer: the wafer number KN1: the recordings of the first sensor KN2: the recordings of the second sensor . . . KN50: the recordings of the last sensor "KN1"-"KN36" belongs to "process1" and "KN37"-"KN50" belongs to "process2". for "response.csv": lot: the lot number wafer: the wafer number response: the numerical test values class: the "good"/"bad" classification depending on the response value (threshold: 0,75)

Keywords

Semiconductor Industry

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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