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Related Concept Videos

Design Consideration01:22

Design Consideration

374
Designing a structure involves a series of considerations, primarily the material's ultimate strength, calculated through tests that measure changes under increased force until the material reaches its breaking point or limit. The ultimate load, where the material breaks, is divided by its original cross-sectional area, resulting in the ultimate normal stress or strength. The ultimate shearing stress is another significant factor taken into account.
The factor of safety is another key...
374

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An Overview of AI-Assisted Design-on-Simulation Technology for Reliability Life Prediction of Advanced Packaging.

Sunil Kumar Panigrahy1, Yi-Chieh Tseng1, Bo-Ruei Lai1

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Summary

This study combines artificial intelligence (AI) with design-on-simulation (DoS) to predict wafer level package (WLP) reliability. AI-assisted DoS reduces the time and cost of optimizing packaging designs, improving reliability assessment.

Keywords:
AIANNFEM simulationKNNKRRRFRNNSVRWLPmachine learningregression model

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Area of Science:

  • Materials Science
  • Electrical Engineering
  • Computer Science

Background:

  • Wafer-level package (WLP) reliability is crucial and influenced by design parameters like pad size and solder volume.
  • Traditional accelerated thermal cycling tests (ATCT) for reliability assessment are time-consuming and costly.
  • Finite-element-based design-on-simulation (DoS) offers faster assessment but results vary due to human factors.

Purpose of the Study:

  • To demonstrate AI-assisted DoS technology for predicting WLP reliability.
  • To overcome the inconsistencies in simulation results caused by human factors.
  • To evaluate the efficiency and accuracy of various machine learning models for reliability prediction.

Main Methods:

  • Developed an AI-assisted DoS framework by integrating artificial intelligence with simulation.
  • Validated simulation procedures experimentally to build a reliable AI training database.
  • Investigated and compared multiple machine learning models: ANN, RNN, SVR, KRR, KNN, and RF.

Main Results:

  • AI-assisted DoS effectively predicts WLP reliability, reducing experimental iterations.
  • The study provides a comparative analysis of machine learning models based on prediction accuracy and computational time.
  • Experimental validation ensured the accuracy of the simulation-based AI training data.

Conclusions:

  • AI-assisted DoS is a viable approach for efficient and accurate WLP reliability assessment.
  • Machine learning models offer a promising alternative to traditional testing methods for electronic packaging.
  • This integrated approach accelerates the design cycle and optimizes packaging structures effectively.