Year: 2024
Type: conference-paper
Abstract: The emergence of Large Language Models (LLMs) has impacted our perspective on applying Machine Learning (ML) in semiconductor test. This paper shares our experience in leveraging the power of LLMs to build an AI agent for test data analytics. We advocate for an end-to-end approach where the Knowledge Graph (KG) plays a central role. Using wafermap analytics as an example, we highlight the key ideas behind developing the LLM-assisted AI agent named IEA-Plot, and discuss its practical applications.
Author: Li-C. Wang
Institution: University of California, Santa Barbara
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4402193872