Vol. 3 No. 1 (2023): Issue 3
Articles

Ridge Regression-based Electrical Performance Prediction in Semiconductor Devices

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  • McKenzie Ella,
  • Thompson Liam,
  • Sophie Chen

Published 2023-05-02

Keywords

  • Semiconductor Devices,
  • Electrical Performance,
  • Ridge Regression,
  • Prediction Accuracy,
  • Design Optimization

How to Cite

Ella, M., Liam, T., & Chen, S. (2023). Ridge Regression-based Electrical Performance Prediction in Semiconductor Devices. Optimizations in Applied Machine Learning, 3(1). Retrieved from https://ojs.mri-pub.com/index.php/OAML/article/view/39
Creative Commons License

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

Abstract

In the field of semiconductor devices, accurate prediction of electrical performance is essential for design and optimization. Current research lacks a comprehensive approach to address the challenges of predicting electrical performance with high precision. This paper addresses this gap by proposing a novel Ridge Regression-based method for predicting electrical performance in semiconductor devices. The innovative aspect of this work lies in its utilization of Ridge Regression, which effectively balances model complexity and prediction accuracy. By incorporating this approach, our research not only improves the accuracy of electrical performance prediction but also provides insights into the underlying factors influencing device performance. This study contributes to the advancement of semiconductor device design and optimization by offering a robust and efficient prediction model.

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