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

End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting the...
Wind Turbine Machine Models01:24

Wind Turbine Machine Models

In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...
Laminar Flow: Problem Solving01:24

Laminar Flow: Problem Solving

Laminar flow occurs when a fluid moves smoothly in parallel layers with minimal mixing and turbulence. In fluid mechanics, ensuring laminar flow within a pipe is essential for precise control of flow characteristics, especially in engineering applications. The key factor in determining whether flow remains laminar is the Reynolds number, a dimensionless quantity that depends on the fluid's velocity, density, viscosity, and the pipe's diameter. A Reynolds number of 2100 or lower indicates...

You might also read

Related Articles

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

Sort by
Same author

Childhood Fractional Exhaled Nitric Oxide Predicts Incident Allergic Rhinitis in Adolescence and Young Adulthood.

Allergy·2026
Same author

Truth-telling experiences and preferences of school-aged children with acute lymphoblastic leukemia: A phenomenological study.

Patient education and counseling·2026
Same author

High Perpendicular Anisotropy in Mo-Inserted Mg Composite Free Layer for Nonvolatile Magnetoresistive Random Access Memory in 4K-400K Universal Temperature Applications.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

A hierarchical sub-modeling approach for the thermo-mechanical analysis of a TFT-FOPLP.

Science progress·2025
Same author

Communication Skills Training to Improve Confidence and Skills in Pediatric Cancer Truth-Telling of Registered Nurses: A Quasi-Experimental Study.

Psycho-oncology·2025
Same author

DFT Study of Au<sub>3</sub>In and Au<sub>3</sub>In<sub>2</sub> Intermetallic Compounds: Structural Stability, Fracture Toughness, Anisotropic Elasticity, and Thermophysical Properties for Advanced Applications.

Materials (Basel, Switzerland)·2025

Related Experiment Video

Updated: May 9, 2026

Electric Field-controlled Directed Migration of Neural Progenitor Cells in 2D and 3D Environments
11:15

Electric Field-controlled Directed Migration of Neural Progenitor Cells in 2D and 3D Environments

Published on: February 16, 2012

11.6K

Development of GUI-Driven AI Deep Learning Platform for Predicting Warpage Behavior of Fan-Out Wafer-Level Packaging.

Ching-Feng Yu1, Jr-Wei Peng2, Chih-Cheng Hsiao2

  • 1Department of Mechanical Engineering, National United University, Miaoli 360302, Taiwan.

Micromachines
|March 27, 2025
PubMed
Summary

This study introduces an AI platform for predicting warpage in fan-out wafer-level packaging (FOWLP). Its user-friendly interface makes deep learning accessible to engineers, improving electronic packaging design efficiency.

Keywords:
AI prediction platformdeep learningfan-out wafer-level packaging (FOWLP)finite element analysis (FEA)graphical user interface (GUI)warpage prediction

More Related Videos

Laser-induced Forward Transfer for Flip-chip Packaging of Single Dies
08:21

Laser-induced Forward Transfer for Flip-chip Packaging of Single Dies

Published on: March 20, 2015

12.3K
Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

618

Related Experiment Videos

Last Updated: May 9, 2026

Electric Field-controlled Directed Migration of Neural Progenitor Cells in 2D and 3D Environments
11:15

Electric Field-controlled Directed Migration of Neural Progenitor Cells in 2D and 3D Environments

Published on: February 16, 2012

11.6K
Laser-induced Forward Transfer for Flip-chip Packaging of Single Dies
08:21

Laser-induced Forward Transfer for Flip-chip Packaging of Single Dies

Published on: March 20, 2015

12.3K
Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

618

Area of Science:

  • Materials Science
  • Computer Science
  • Electrical Engineering

Background:

  • Predicting warpage in fan-out wafer-level packaging (FOWLP) is challenging for traditional electronic engineers.
  • Implementing AI-driven models requires specialized programming and algorithmic expertise, limiting accessibility.

Purpose of the Study:

  • To develop an accessible AI prediction platform for warpage behavior in FOWLP.
  • To simplify the design, training, and operation of deep learning models for non-expert users.

Main Methods:

  • Development of a deep learning-based AI prediction platform.
  • Integration of a graphical user interface (GUI) to abstract complex AI processes.
  • Automated feature extraction, data cleansing, and model training for large datasets.

Main Results:

  • The platform enables users to configure and run AI predictions without extensive coding knowledge.
  • Accurate and reliable warpage predictions for FOWLP architectures were achieved through case studies.
  • Demonstrated improvement in design efficiency and reduction of errors in electronic packaging.

Conclusions:

  • The AI platform enhances accessibility of deep learning for electronic packaging design.
  • It provides a valuable tool for optimizing FOWLP designs and improving system performance.
  • The GUI-driven platform facilitates further advancements in electronic packaging technology.