AI-driven verification: Augmenting engineers in semiconductor EDA workflows

The semiconductor industry faces mounting verification challenges as chip designs grow increasingly complex, with process nodes shrinking and design elements multiplying. AI-driven verification emerges as a transformative solution, creating a symbiosis between human expertise and machine intelligence rather than replacing engineers. This article explores how AI technologies augment verification workflows through predictive models for testbench generation, advanced anomaly detection systems, and collaborative human-machine partnerships. These innovations enable verification teams to navigate complexity with greater precision while reducing time-to-market pressures. Despite significant technical challenges and organizational hurdles, the trajectory toward AI-augmented verification is clear, requiring not just technological adaptation but a cultural shift in how organizations approach verification processes and engineer roles in the semiconductor ecosystem.

Authors

Publication Details

Journal
World Journal of Advanced Engineering Technology and Sciences
Published
2025-05-06
DOI
https://doi.org/10.30574/wjaets.2025.15.2.0424
Primary Topic
Semiconductor materials and devices
Type
article
Field-Weighted Citation Impact
0.00
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article

AI-driven verification: Augmenting engineers in semiconductor EDA workflows

S Ravikumar
World Journal of Advanced Engineering Technology and Sciences
Semiconductor materials and devices
article

AI-driven verification: Augmenting engineers in semiconductor EDA workflows

S Ravikumar
article en

Abstract

The semiconductor industry faces mounting verification challenges as chip designs grow increasingly complex, with process nodes shrinking and design elements multiplying. AI-driven verification emerges as a transformative solution, creating a symbiosis between human expertise and machine intelligence rather than replacing engineers. This article explores how AI technologies augment verification workflows through predictive models for testbench generation, advanced anomaly detection systems, and collaborative human-machine partnerships. These innovations enable verification teams to navigate complexity with greater precision while reducing time-to-market pressures. Despite significant technical challenges and organizational hurdles, the trajectory toward AI-augmented verification is clear, requiring not just technological adaptation but a cultural shift in how organizations approach verification processes and engineer roles in the semiconductor ecosystem.

World Journal of Advanced Engineering Technology and SciencesVol. 15(2)
Openalex Percentile: Top 92%
Semiconductor materials and devices
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