Silicon Whisperers: Improving Test Quality and Cost in the Age of Generative AI
The relentless scaling of process nodes, emergence of post-silicon device technologies, and stringent quality demands of automotive and medical markets have pushed integrated-circuit (IC) testing to an economic inflection point: manufacturing tests for advanced SoCs already account for nearly one-third of total silicon cost, yet latent-defect escapes remain an industry concern. In this survey, we analyze how large language models (LLMs) and broader generative AI techniques can be leveraged to break the long-standing quality-versus-cost trade-off in test. We discuss how generative AI is transitioning from pilot studies to factory-scale impact, positioning the semiconductor industry to deliver higher-quality products at lower test cost over the coming decade.
Authors
- Jonti Talukdar (ORCID: https://orcid.org/0000-0001-7079-5281)
- Farshad Firouzi (ORCID: https://orcid.org/0000-0002-8359-4304)
- Sanmitra Banerjee (ORCID: https://orcid.org/0000-0002-1136-9220)
Institutions
- Nvidia (United States) (US)
- Arizona State University (US)
Publication Details
- Published
- 2025-08-04
- DOI
- https://doi.org/10.1109/coins65080.2025.11125741
- Citations
- 2
- Primary Topic
- VLSI and Analog Circuit Testing
- Type
- article
- Field-Weighted Citation Impact
- 4.54