scholarly journals Study on IoT and Big Data Analysis of 12” 7 nm Advanced Furnace Process Exhaust Gas Leakage

2020 ◽  
Author(s):  
Kuo-Chi Chang ◽  
Kai-Chun Chu ◽  
Hsiao-Chuan Wang ◽  
Yuh-Chung Lin ◽  
Tsui-Lien Hsu ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Semiconductor Manufacturing ◽  
Production Management ◽  
High Energy ◽  
Big Data Analysis ◽  
Gas Leakage ◽  
Cloud Database ◽  
Set Up ◽  
Energy Processes

Modern FAB uses a large number of high-energy processes, including plasma, CVD, and ion implantation. Furnaces are one of the important tools for semiconductor manufacturing. According to the requirements of conversion production management, FAB installed a set of IoT-based research based on 12″ 7 nm-level furnaces chip process. Two furnace processing tool measurement points were set up in a 12-inch 7 nm-level factory in Hsinchu Science Park, Taiwan, this is a 24-hour continuous monitoring system, the data obtained every second is sequentially send and stored in the cloud system. This study will be set in the cloud database for big data analysis and decision-making. The lower limit of TEOS, C2H4, CO is 0.4, 1.5, 1 ppm. Semiconductor process, so that IoT integration and big data operations can be performed in all processes, this is an important step to promote FAB intelligent production, and also an important contribution to this research.

scholarly journals A monitoring method of semiconductor manufacturing processes using Internet of Things–based big data analysis

2017 ◽  
Vol 13 (7) ◽  
pp. 155014771772181 ◽  
Author(s):  
Seok-Woo Jang ◽  
Gye-Young Kim
Keyword(s):  
Time Series ◽  
Big Data ◽  
Data Analysis ◽  
Internet Of Things ◽  
Real Time ◽  
Semiconductor Manufacturing ◽  
Reference Model ◽  
Big Data Analysis ◽  
Monitoring Method ◽  
Manufacturing Equipment

This article proposes an intelligent monitoring system for semiconductor manufacturing equipment, which determines spec-in or spec-out for a wafer in process, using Internet of Things–based big data analysis. The proposed system consists of three phases: initialization, learning, and prediction in real time. The initialization sets the weights and the effective steps for all parameters of equipment to be monitored. The learning performs a clustering to assign similar patterns to the same class. The patterns consist of a multiple time-series produced by semiconductor manufacturing equipment and an after clean inspection measured by the corresponding tester. We modify the Line, Buzo, and Gray algorithm for classifying the time-series patterns. The modified Line, Buzo, and Gray algorithm outputs a reference model for every cluster. The prediction compares a time-series entered in real time with the reference model using statistical dynamic time warping to find the best matched pattern and then calculates a predicted after clean inspection by combining the measured after clean inspection, the dissimilarity, and the weights. Finally, it determines spec-in or spec-out for the wafer. We will present experimental results that show how the proposed system is applied on the data acquired from semiconductor etching equipment.

Declarative Big Data Analysis for High-Energy Physics: TOTEM Use Case

2019 ◽  
pp. 241-255
Author(s):  
Valentina Avati ◽  
Milosz Blaszkiewicz ◽  
Enrico Bocchi ◽  
Luca Canali ◽  
Diogo Castro ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
High Energy Physics ◽  
High Energy ◽  
Big Data Analysis ◽  
Use Case ◽  
Energy Physics

Insights into seismic hazard from big data analysis of ground motion simulations

2019 ◽  
Vol 9 (1) ◽  
pp. 01-12 ◽  
Author(s):  
Kristy F. Tiampo ◽  
Javad Kazemian ◽  
Hadi Ghofrani ◽  
Yelena Kropivnitskaya ◽  
Gero Michel
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Seismic Hazard ◽  
Ground Motion ◽  
Big Data Analysis ◽  
Ground Motion Simulations

A Social Big Data Analysis on Sport Participation

2020 ◽  
Vol 25 (2) ◽  
pp. 18-30
Author(s):  
Seung Wook Oh ◽  
Jin-Wook Han ◽  
Min Soo Kim
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Sport Participation ◽  
Big Data Analysis ◽  
Social Big Data

A Study on Perception of Golf Lesson Using Big Data Analysis

2020 ◽  
Vol 14 (1) ◽  
pp. 151-163
Author(s):  
Joon-Seo Choi ◽  
◽  
Su-in Park
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis
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