Machine Learning Based Variation Modeling and Optimization for 3D ICs

Samal, Sandeep Kumar; Lim, Sung Kyu

doi:10.6109/jicce.2016.14.4.258

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…Recent developments in the application of machine learning approaches to design research challenges have generated a lot of interest [10,11]. A model is trained or guided by the actual application of a process or phenomena, and then it is used to predict the same metric for new input…”

Section: Machine Learning Techniques In Embedded Memorymentioning

confidence: 99%

Parasitic RC Estimation and Defect Prediction for Embedded Memory using Machine Learning

Maddela,

Sinha,

Parvathi

et al. 2024

Preprint

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In today's rapidly scaling-down technological environment, identifying the best-fit algorithms for evaluating complicated circuits such as SRAMs is a difficult issue. Many fault models have developed, however their flexibility of use is limited by the restrictions and constraints of the provided test environment. The majority of existing fault models have been studied in terms of well-known March algorithms, which simply provide fault detection information. Scaled-down technologies have an impact on parasitic effects as well, resulting in an extra source of defective behavior and making current test algorithms vulnerable to them. Recent work that uses method of parasitic extraction for fault detection have addressed the problem of limitation due to scale down technologies. However, as the circuit complexity increases the estimation of RC would be tedious. Hence in this paper machine learning based parasitic RC extraction is proposed. Also, as an extension to that, proposed ML based fault detection using extracted parasitic RCs as dataset. The proposed machine learning based fault prediction uses extracted parasitic RCs as dataset. The parasitic RC values are extracted for each fault model using technologies of 120nm down to deep submicron 7nm. Regression algorithm is used for modeling the machine for extraction of RCs and observed that 88% of prediction accuracy. Decision tree modeling is used for fault detection and observed 91.7% of accuracy in prediction of fault. 3.1 Effect of open defects in 6T SRAM Cell In this are article we have consider the node to node open/short faults. In Fig 2. We have imposed all possible open defects and then we have analyzed the memory cell for all possible open defects. There are totally 25 open defects are possible as shown in the fig 2. The simulation results and different types faults occurs for all open defects are shown in table 1 Fig2: Fault model for Open Defects in 6T-SRAM Cell Table 1. 6T SRAM Cell open defect list for different technologies

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Section: Machine Learning Techniques In Embedded Memorymentioning

confidence: 99%

Parasitic RC Estimation and Defect Prediction for Embedded Memory using Machine Learning

Maddela,

Sinha,

Parvathi

et al. 2024

Preprint

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AI-Based Hardware Security Methods for Internet-of-Things Applications

Dofe

Danesh

2022

Frontiers of Quality Electronic Design (QED)

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Machine Learning for Electronic Design Automation: A Survey

Huang

et al. 2021

Preprint

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With the down-scaling of CMOS technology, the design complexity of very large-scale integrated (VLSI) is increasing. Although the application of machine learning (ML) techniques in electronic design automation (EDA) can trace its history back to the 90s, the recent breakthrough of ML and the increasing complexity of EDA tasks have aroused more interests in incorporating ML to solve EDA tasks. In this paper, we present a comprehensive review of existing ML for EDA studies, organized following the EDA hierarchy.

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Machine Learning Based Variation Modeling and Optimization for 3D ICs

Cited by 3 publications

References 12 publications

Parasitic RC Estimation and Defect Prediction for Embedded Memory using Machine Learning

Parasitic RC Estimation and Defect Prediction for Embedded Memory using Machine Learning

AI-Based Hardware Security Methods for Internet-of-Things Applications

Machine Learning for Electronic Design Automation: A Survey

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