The expanding role of AI in semiconductor verification signifies a world in which machine learning becomes the key asset of engineers. Deep learning models will progress, with reinforcement learning perfecting testing strategy, and anomaly detection improving the detection of errors. AI-driven methodologies will thus speed up verification. But it all depends on a balance being maintained—automaton can make things faster, yet domain expertise continues to play an essential role in interpreting outputs and fine-tuning models. By combining AI with human intelligence, semiconductor firms can enhance productivity, eliminate design errors, and advance innovation boundaries, making verification smarter, faster, and more accurate in a rapidly growing chip design world.
In conclusion, Anubhav Mangla highlights how machine learning is redefining the landscape of semiconductor design verification, enabling more efficient and accurate functional coverage closure. While challenges such as data quality, model interpretability, and ongoing maintenance remain, the advantages of AI-driven verification outweigh these concerns. As technological advancements continue, the integration of machine learning in verification will become even more seamless, paving the way for innovation in semiconductor development and beyond.
