Min Jian Yang
In many industrial manufacturing processes, an underlying goal is to manufacture products with high yield while ensuring financially viable time-to-market. This is especially true for the semiconductor manufacturing processes where Product Engineers spend significant portions of their time analyzing production data to address sudden yield issues, and/or discover ways to optimize the production yield. Intelligent Engineering Assistant (IEA) is an AI assistant designed to facilitate a Product Engineer's workflow. In the scope of this project, IEA Linguistics, we are extending and enhancing IEA's human-to-machine translation interface by utilizing modern NLP techniques. Specifically, the enhancement focuses on understanding the Product Engineer's conversational intents. Standard and open-source packages alone are not suitable as they can produce a noisy output for some nontechnical domain. With a more robust NLP interface, IEA can receive language instructions and present data analytic results in an automated and interactive fashion, such as using visual plots based on various data types. This automated presentation feature effectively reduced the amount of time spent on mechanical tasks and leaves the Product Engineer with more bandwidth involving critical-thinking tasks.