As semiconductor products become increasingly complex, the corresponding test development process faces significant challenges. The number of required test cases rises dramatically, and test environments grow more intricate—leading to longer development cycles, delayed product releases, and constrained test coverage. Manual creation of test cases from specifications, protocols, drivers, and automation frameworks is not only time-consuming but also highly dependent on engineering expertise, often taking months for a single product.
We propose a novel application of Generative AI to transform product specifications directly into test cases and functional test scripts. By automating large portions of the test development lifecycle, Generative AI can reduce timelines from months to days or even hours. This acceleration improves both the accuracy and reliability of tests, allowing engineers to focus more on strategic tasks like expanding test coverage and enhancing product quality.
How is technology enabling this today?
Domain-specific agents powered by Generative AI are demonstrating remarkable capabilities in specialized tasks. These agents can efficiently parse large volumes of domain knowledge, generate comprehensive test cases, and produce standard configurations and test programs at a pace far beyond human capacity. Meanwhile, engineers excel at interpreting product intent, validating outputs, and exercising judgment about when to guide or constrain AI behavior. This emerging collaborative model—often referred to as “AI-in-the-loop,” “human-in-the-loop,” or simply “Copilots”—is proving to be a powerful productivity amplifier.
We are bringing this paradigm to Semiconductor Test and Measurement by developing tailored user experiences and domain-specific AI agents. These tools enable engineers to seamlessly interact with AI systems, dramatically accelerating test generation and validation processes—unlocking faster time-to-market and higher-quality outcomes.
In one case study, this approach reduced test sequence generation time from several weeks to just a single day using AI-powered agents. While the technology and its adoption are still in the early stages, ongoing research continues to improve agent accuracy and enhance the overall user experience. Despite being nascent, we are already seeing strong potential for this paradigm to fundamentally transform the future of test and measurement workflows.
