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Applications and challenges of hybrid artificial intelligence in chip age testing: a comprehensive review

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DOI: 10.23977/jaip.2023.060309 | Downloads: 16 | Views: 481

Author(s)

Cong Xu 1, Wensheng Chen 1, Mingkuan Lin 1, Jianli Lu 1, Yunghsiao Chung 1, Jiahui Zou 1, Ciliang Yang 1

Affiliation(s)

1 Global ETS. LLC, 2631 Success Dr, Odessa, Florida, 33556, USA

Corresponding Author

Cong Xu

ABSTRACT

As technology rapidly advances, semiconductor devices play a crucial role in various fields. However, these devices experience aging over time, leading to performance degradation, failure, or system crashes. Real-time aging detection of semiconductor devices is essential. This paper presents a real-time aging detection technique for semiconductor devices, combining deep learning and evolutionary algorithms, effectively assessing and predicting device aging states using Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). These features are then input into evolutionary algorithm frameworks, such as Genetic Algorithms (GA) and Genetic Algorithms (PSO), to identify and predict aging trends. The adaptation of evolutionary algorithms ensures good generalization for various semiconductor devices. Through extensive experimental data analysis, the proposed technique demonstrates excellent accuracy and real-time performance compared to traditional aging detection methods. In addition, it also monitors their operation in real-time, providing valuable support for maintenance and management personnel. The findings contribute to improving semiconductor device reliability and stability, providing a robust foundation for intelligent and automated maintenance.

KEYWORDS

Artificial intelligence, Convolutional Neural Networks, Recurrent Neural Networks, Genetic Algorithms, Genetic Algorithms

CITE THIS PAPER

Cong Xu, Wensheng Chen, Mingkuan Lin, Jianli Lu, Yunghsiao Chung, Jiahui Zou, Ciliang Yang, Applications and challenges of hybrid artificial intelligence in chip age testing: a comprehensive review. Journal of Artificial Intelligence Practice (2023) Vol. 6: 70-75. DOI: http://dx.doi.org/10.23977/jaip.2023.060309.

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