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Karthik Pandaram - IEEE Xplore Author Profile
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Karthik Pandaram

Publications
4
Citations
18
Publications by Year
20192024
Co-Authors:
Show All Co-Authors (8)
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Karthik Pandaram

Affiliation

Institute of Computer Engineering and Computer Architecture, University of Stuttgart

Publication Topics

Biography

Karthik Pandaram received the Bachelor of Engineering degree in electrical and electronics engineering from the Coimbatore Institute of Technology, India, in 2020, and the M.Sc. degree in information technology with a specialization in embedded systems engineering from the University of Stuttgart, Germany, in 2023. His research interests include deep learning, automated driving, intelligent sensor systems, semiconductor test, and reliability.(Based on document published on 10 July 2023).
Publications
4
Citations
18
Publications by Year
20192024
Co-Authors:
Show All Co-Authors (8)
Author's Published Works

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Optimized Detection of Marginal Defects in Standard Cells Using Unsupervised Learning

Karthik Pandaram;Hussam Amrouch;Ilia Polian

2024 IEEE 33rd Asian Test Symposium (ATS)
Year: 2024 | Conference Paper |
Marginal defects, such as high-resistance short or low-resistance open defects, are hard to detect by conventional pass-fail test methods because their manifestations are practically indistinguishable from the effects of regular variations. However, their coverage is essential for circuits with high-quality requirements and/or when early-life failures are a concern. In this paper, we propose an al...Show More

Optimized Detection of Marginal Defects in Standard Cells Using Unsupervised Learning

Karthik Pandaram;Hussam Amrouch;Ilia Polian

Year: 2024 | Conference Paper |

WaSSaBi: Wafer Selection With Self-Supervised Representations and Brain-Inspired Active Learning

Karthik Pandaram;Paul R. Genssler;Hussam Amrouch

IEEE Transactions on Circuits and Systems I: Regular Papers
Year: 2024 | Volume: 71, Issue: 4 | Journal Article |
Cited by: Papers (1)
Large datasets are often available for machine learning tasks. However, only very few contain labels for all the samples because labeling is a very labor-intensive process. Hence, large unlabeled datasets are available but inaccessible to traditional supervised learning methods. In this work, we combine two approaches to reduce the number of required labels. First, self-supervised learning (SSL) t...Show More

WaSSaBi: Wafer Selection With Self-Supervised Representations and Brain-Inspired Active Learning

Karthik Pandaram;Paul R. Genssler;Hussam Amrouch

Year: 2024 | Volume: 71, Issue: 4 | Journal Article |

Modeling and Predicting Transistor Aging Under Workload Dependency Using Machine Learning

Paul R. Genssler;Hamza E. Barkam;Karthik Pandaram;Mohsen Imani;Hussam Amrouch

IEEE Transactions on Circuits and Systems I: Regular Papers
Year: 2023 | Volume: 70, Issue: 9 | Journal Article |
Cited by: Papers (17)
The pivotal issue of reliability is one of the major concerns for circuit designers. The driving force is transistor aging, dependent on operating voltage and workload. At the design time, it is difficult to estimate close-to-the-edge guardbands that keep aging effects during the lifetime at bay. This is because the foundry does not share its calibrated physics-based models, comprised of highly co...Show More

Modeling and Predicting Transistor Aging Under Workload Dependency Using Machine Learning

Paul R. Genssler;Hamza E. Barkam;Karthik Pandaram;Mohsen Imani;Hussam Amrouch

Year: 2023 | Volume: 70, Issue: 9 | Journal Article |

Automated Deformation Detection System for Tubes and Rods in Manufacturing Industries Using Quasi Digital Sensors

Karthik Pandaram;V. Manikandan;S. Ramesh;M. Sivaramakrishna

2019 9th IEEE International Conference on Control System, Computing and Engineering (ICCSCE)
Year: 2019 | Conference Paper |
This paper reports the feasibility study of developing an automatic deformation detection system. This system detects the out of roundness or deformation on the surface of cylindrical tubes or rods during mass production for specified application with better quality control. It has been identified that there is need for an automated system that will help tube or rod manufacturing industries to det...Show More

Automated Deformation Detection System for Tubes and Rods in Manufacturing Industries Using Quasi Digital Sensors

Karthik Pandaram;V. Manikandan;S. Ramesh;M. Sivaramakrishna

Year: 2019 | Conference Paper |

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