Senior Data Scientist Advantest America, Inc. San Jose, CA, United States
The rapid evolution of Artificial Intelligence (AI), particularly in the realms of Generative AI and the prominence of Large Language Models (LLMs), has already begun to reshape numerous engineering disciplines. In software development, for instance, AI-driven tools are accelerating coding, enhancing debugging processes, and streamlining workflows, leading to unprecedented gains in productivity and innovation. The semiconductor industry, a cornerstone of technological advancement, is similarly poised for a revolutionary shift. This presentation will explore how these powerful AI technologies can be strategically leveraged to upskill test engineers, transforming their roles and dramatically increasing efficiency.
We will delve into the practical applications of AI in the daily workflow of a test engineer. A primary focus will be on the use of LLMs for automated test program generation. By translating high-level test requirements into syntactically correct and efficient code for various Automatic Test Equipment (ATE) platforms, generative AI can significantly reduce development time and eliminate repetitive tasks. This allows engineers to shift their focus from routine coding to the more complex challenges of test strategy and measurement integrity. Furthermore, the presentation will address how AI-powered chat assistants can function as intelligent knowledge bases. Instead of spending valuable time sifting through dense and extensive equipment documentation, engineers can interact with these assistants to receive instant, context-aware answers to complex queries. This capability not only accelerates problem-solving but also lowers the barrier to entry for engineers working with new or unfamiliar test platforms.
Beyond these applications, we will explore other emergent uses of AI in the testing domain, including: - Advanced Data Analytics: Utilizing AI to analyze vast datasets from test results to rapidly identify subtle anomalies, predict yield excursions, and perform insightful root cause analysis. - Intelligent Debugging: Assisting engineers in diagnosing complex hardware and software issues by identifying patterns and suggesting potential solutions. - Enhanced Defect Localization: AI models can analyze failure or log data aiding in yield improvement efforts.
Ultimately, this presentation will make the case that integrating AI into semiconductor testing is not about replacing the engineer but about augmenting their expertise. By embracing these tools, we can upskill our engineering talent, empowering them to tackle higher-value challenges and drive the next wave of innovation in the industry.
