Citation :

Muthukumaran Vaithianathan, Mahesh Patil, Shunyee Frank Ng, Shiv Udkar, 2024. "Integrating AI and Machine Learning with UVM in Semiconductor Design" ESP International Journal of Advancements in Computational Technology (ESP-IJACT)  Volume 2, Issue 3: 37-51.

Abstract :

The combination of Artificial Intelligence (AI) and Machine Learning (ML) with Universal Verification Methodology (UVM) has been thought to revolutionalize semiconductor design, making the process more efficient and accurate as possible. AI and ML strategies are most effective in applications requiring the automated confirmation of time-taking processes that were previously blended with human errors. These technologies are, therefore, capable of trawling through an incredibly large volume of data in order to get what the algorithm believes is likely to be the design weaknesses that might not be easily discernible to a human eye. It also supports the verification task and improves the quality of the verification result at the same time. In turn, the literature review indicates the extent of present developments, as well as the remaining research voids in this emerging area. These papers demonstrate that AI/ML can decrease time by automating routine verifications and giving time-efficient forecasts. For instance, decision making can be applied to design intelligent test cases that can provide better coverage than manual ones in terms of verification. Also the test coverage data can be processed through the use of ML algorithms to pinpoint areas that require more credentialed work than is required in verification. The generic as well as the specific approach of integrating AI/ML models into UVM environments is elaborated with fanatic meticulousness.

They can be trained on historical verification data to predict the bugs and to generate test cases which helps in optimizing the verification stage. The integration feature also consists of feedback links where AI/ML models continue to learn from the ongoing outcomes of the verification process for improved efficiency. This then gives the means for adaptive learning, which guarantees that the verification environment will grow with the design as new problems emerge. Among the specific examples of successful application of AI/ML in UVM, it is possible to distinguish notable enhancements of practical results of verification processes. Actual examples show that through using these technologies, the time-to-market for semiconductor products was shrunk due to the verification time and the quality of the designed circuits. For instance, the verification approaches based on AI have helped minimize post-silicon bugs, which are relatively expensive and time-consuming when addressed. Such outcomes prove that AI/ML is capable of not only solving contemporary problems in the development of semiconductors but also charting a course for future improvements in validation methods, which are vital to the industry’s growth.

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Keywords :

Internet of Things (IoT), Robotics, Software Architecture, Smart Environments, Communication Protocols, Data Management, Control Strategies, Scalability, Interoperability, Security.