Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Introduction to Statistical Process Control01:15

Introduction to Statistical Process Control

Statistical Process Control (SPC) is a method used to monitor and control quality within processes, particularly in manufacturing and service delivery, by employing statistical methods. SPC aims to distinguish between natural (common cause) variation and variation due to specific changes or events (special cause), allowing for timely improvements and sustained quality. The control chart, a pivotal tool in SPC, visually displays data over time alongside a central line of upper and lower control...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Recent Advancements in Hyperspectral Image Reconstruction from a Compressive Measurement.

Sensors (Basel, Switzerland)·2025
Same author

Mask-Guided Spatial-Spectral MLP Network for High-Resolution Hyperspectral Image Reconstruction.

Sensors (Basel, Switzerland)·2024
Same author

A Boundary-Enhanced Liver Segmentation Network for Multi-Phase CT Images with Unsupervised Domain Adaptation.

Bioengineering (Basel, Switzerland)·2023
Same author

Deep Unsupervised Fusion Learning for Hyperspectral Image Super Resolution.

Sensors (Basel, Switzerland)·2021
Same author

Spectral Representation vis Data-Guided Sparsity for Hyperspectral Image Super-Resolution<sup>†</sup>.

Sensors (Basel, Switzerland)·2019
Same author

An Improved Random Walker with Bayes Model for Volumetric Medical Image Segmentation.

Journal of healthcare engineering·2017
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jun 4, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K

Using SDPC for Visual Exploratory Analysis of Semiconductor Production Line Sensor Data.

Xinxiao Li1, Xian-Hua Han2, Yongqing Sun3

  • 1Faculty of Information Science, Shonan Institute of Technology, 1-1-25 Tsujido Nishikaigan, Fujisawa City 251-8511, Japan.

Sensors (Basel, Switzerland)
|April 12, 2025
PubMed
Summary
This summary is machine-generated.

Semiconductor manufacturing generates massive sensor data. A new system, SDPC, uses interactive Parallel Coordinate Plots (PCPs) for real-time analysis, speeding up defect diagnosis and improving production yield.

Keywords:
parallel coordinatessemiconductor production linesensor datasuperhigh-dimensional datavisual exploratory analysis

More Related Videos

Fixed Target Serial Data Collection at Diamond Light Source
06:19

Fixed Target Serial Data Collection at Diamond Light Source

Published on: February 26, 2021

3.2K
Mechano-Node-Pore Sensing: A Rapid, Label-Free Platform for Multi-Parameter Single-Cell Viscoelastic Measurements
05:49

Mechano-Node-Pore Sensing: A Rapid, Label-Free Platform for Multi-Parameter Single-Cell Viscoelastic Measurements

Published on: December 2, 2022

2.6K

Related Experiment Videos

Last Updated: Jun 4, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K
Fixed Target Serial Data Collection at Diamond Light Source
06:19

Fixed Target Serial Data Collection at Diamond Light Source

Published on: February 26, 2021

3.2K
Mechano-Node-Pore Sensing: A Rapid, Label-Free Platform for Multi-Parameter Single-Cell Viscoelastic Measurements
05:49

Mechano-Node-Pore Sensing: A Rapid, Label-Free Platform for Multi-Parameter Single-Cell Viscoelastic Measurements

Published on: December 2, 2022

2.6K

Area of Science:

  • Data Visualization
  • Semiconductor Manufacturing
  • High-Dimensional Data Analysis

Background:

  • Semiconductor production lines generate vast, high-dimensional sensor data.
  • Identifying complex relationships for defect diagnosis and yield improvement is challenging.
  • Traditional Parallel Coordinate Plots (PCPs) struggle with superhigh-dimensional datasets.

Purpose of the Study:

  • To develop an interactive visual analysis system, SDPC, for semiconductor production lines.
  • To address the limitations of traditional PCPs in handling superhigh-dimensional data.
  • To enhance defect diagnosis and improve production yield through efficient data exploration.

Main Methods:

  • Proposed SDPC, an interactive PCP-based visual analysis system with a server-client architecture.
  • Implemented dynamic dimension selection and data down-sampling based on user interaction.
  • Integrated user-defined filters and focused on defect-relevant dimensions.

Main Results:

  • SDPC enables real-time visualization of high-dimensional sensor data with minimal delay.
  • The system enhances interpretability and accelerates root cause identification for defects.
  • Evaluations showed a two-thirds reduction in visual analysis time for engineers.

Conclusions:

  • SDPC facilitates efficient, real-time exploratory analysis of semiconductor sensor data.
  • The system boosts operational efficiency and reduces analysis time.
  • SDPC leads to more effective production processes and improved yield rates.