LLM-Assisted Analytics in Semiconductor Test (Invited)
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.CCS Concepts• Hardware → Hardware test; • Computing methodologies → Artificial intelligence.
Authors
- Li-C. Wang (ORCID: https://orcid.org/0000-0003-4851-8004)
Institutions
- University of California, Santa Barbara (US)
Publication Details
- Published
- 2024-09-09
- DOI
- https://doi.org/10.1109/mlcad62225.2024.10740163
- Citations
- 2
- Primary Topic
- Integrated Circuits and Semiconductor Failure Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.42
Funders
- National Science Foundation