Modeling and Predicting Transistor Aging Under Workload Dependency Using Machine Learning

Author:

Genssler Paul R.1ORCID,Barkam Hamza E.2ORCID,Pandaram Karthik1ORCID,Imani Mohsen2ORCID,Amrouch Hussam1ORCID

Affiliation:

1. Chair of Semiconductor Test and Reliability (STAR), University of Stuttgart, Stuttgart, Germany

2. Bio-Inspired Architecture and Systems (BIASLab), UC Irvine, Irvine, CA, USA

Funder

German Research Foundation (DFG) “ACCROSS: Approximate Computing aCROss the System Stack”

Advantest as part of the Graduate School “Intelligent Methods for Test and Reliability” (GS-IMTR), University of Stuttgart

Defense Advanced Research Projects Agency (DARPA) Young Faculty Award

National Science Foundation

Semiconductor Research Corporation

Office of Naval Research

Air Force Office of Scientific Research

Xilinx, Advanced Micro Devices (AMD), and Cisco

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Hardware and Architecture

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1. Machine Learning Methodologies to Predict the Results of Simulation-Based Fault Injection;IEEE Transactions on Circuits and Systems I: Regular Papers;2024-05

2. Machine Learning Unleashes Aging and Self-Heating Effects: From Transistors to Full Processor (Invited Paper);2024 IEEE International Reliability Physics Symposium (IRPS);2024-04-14

3. WaSSaBi: Wafer Selection With Self-Supervised Representations and Brain-Inspired Active Learning;IEEE Transactions on Circuits and Systems I: Regular Papers;2024-04

4. HDCircuit: Brain-Inspired HyperDimensional Computing for Circuit Recognition;2024 Design, Automation & Test in Europe Conference & Exhibition (DATE);2024-03-25

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