Generative AI for Intelligent Manufacturing Virtual Assistants in the Semiconductor Industry

As semiconductor manufacturing complexity escalates, the intricacy of corresponding manufacturing systems intensifies. These extensive systems necessitate diverse engineering expertise for effective operation and analysis. For instance, yield engineers analyze yield systems, process engineers interpret FDC parameters, and equipment engineers monitor device equipment health. Traditional manufacturing systems, reliant on manual data analysis and fixed algorithms, suffer from slow decision-making and limited adaptability. They are susceptible to human error, reactive maintenance, and restricted user interaction confined to technical interfaces and business hours. Additionally, scalability and integration pose significant challenges, inflating operational costs and hampering resource efficiency. This letter introduces an Intelligent Manufacturing Virtual Assistant (IMVA) specifically designed for the semiconductor industry. By harnessing the power of Large Language Models (LLMs) and AI Agents, IMVA enhances yield analysis and seamlessly integrates with existing systems and tools. It exhibits high accuracy in defect detection through advanced data analysis and report generation. Furthermore, IMVA facilitates natural language interaction, rendering it user-friendly and accessible to non-technical personnel. Consequently, IMVA markedly improve operational efficiency and cost-effectiveness compared to traditional manufacturing systems. The efficacy of IMVA is demonstrated through the Wide-bandgap (WBG) process, showcasing its capability to simplify root cause analysis and provide comprehensive yield reports.