Vol. 1 No. 1 (2021): Issue 1
Articles

Semiconductor Reliability Analysis via Adaptive Kriging

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  • O'Sullivan Liam,
  • Zoe Thompson,
  • Jack Roberts
DOI: https://doi.org/10.71070/oaml.v1i1.40

Published 2021-05-02

Keywords

  • Semiconductor Devices,
  • Reliability Analysis,
  • Adaptive Kriging,
  • Model Parameters,
  • Prediction Accuracy

How to Cite

Liam, O., Thompson, Z., & Roberts, J. (2021). Semiconductor Reliability Analysis via Adaptive Kriging. Optimizations in Applied Machine Learning, 1(1). https://doi.org/10.71070/oaml.v1i1.40
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Abstract

Semiconductor devices play a critical role in modern electronic systems, necessitating a comprehensive understanding of their reliability. Despite the extensive research in this field, current methodologies encounter challenges in accurately predicting semiconductor reliability due to the complex interactions among various factors. This paper addresses the limitations of existing approaches by proposing a novel methodology for Semiconductor Reliability Analysis via adaptive Kriging. The key innovation lies in the adaptive Kriging technique, which dynamically adjusts model parameters based on the characteristics of the semiconductor device under analysis. Our work not only enhances the accuracy of reliability predictions but also provides a more efficient and robust framework for assessing semiconductor reliability.

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