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.

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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

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article

LLM-Assisted Analytics in Semiconductor Test (Invited)

Li-C. Wang
2 citations
Integrated Circuits and Semiconductor Failure Analysis
0.42
article

LLM-Assisted Analytics in Semiconductor Test (Invited)

Li-C. Wang
article en
2 citations

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.CCS Concepts• Hardware → Hardware test; • Computing methodologies → Artificial intelligence.

University of California, Santa Barbara (US)
National Science Foundation
Openalex Percentile: Top 37%
Integrated Circuits and Semiconductor Failure Analysis
0.42
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