Reliable Power Delivery and Analysis of Power-Supply Noise During Testing in Monolithic 3D ICs

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Koneru:2019:VTS,

• author = "Abhishek Koneru and Aida Todri-Sanial and Krishnendu Chakrabarty",
• booktitle = "2019 IEEE 37th VLSI Test Symposium (VTS)",
• title = "Reliable Power Delivery and Analysis of Power-Supply Noise During Testing in Monolithic {3D ICs}",
• year = "2019",
• abstract = "Monolithic 3D (M3D) integration offers significant performance, power, and area benefits. However, the design of a reliable M3D power-delivery network (PDN) is challenging due to high power density and current demand per unit area. We propose a framework to design a reliable PDN for M3D ICs using accurate electrical and reliability models. We leverage genetic programming to explore the design space to optimize the PDN for M3D. We also analyze power-supply noise (PSN) during scan-based testing and compare it with that observed during functional operation. We quantify the impact of PSN during scan-based testing on yield loss. Our results show that the PDN obtained using the proposed approach significantly increases the reliability of at least 4percent of the wire segments in the PDN. In addition, the proposed PDN design reduces the worst-case power-supply droop by 50.percent compared to a baseline PDN. The yield loss due to power-supply droop for the proposed design is also significantly lower compared to the baseline.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/VTS.2019.8758650",
• ISSN = "2375-1053",
• month = apr,
• notes = "Also known as \cite{8758650}",

}

Genetic Programming entries for Abhishek Koneru Aida Todri-Sanial Krishnendu Chakrabarty

Citations