Physics-informed machine learning model for bias temperature instability
Author:
Affiliation:
1. Department of System Semiconductor Engineering, Sangmyung University, 31 Sangmyungdae-gil, Dongnam-gu, Cheonan, Chungnam 31066, South Korea
Funder
National Research Foundation of Korea
Publisher
AIP Publishing
Subject
General Physics and Astronomy
Link
https://aip.scitation.org/doi/pdf/10.1063/5.0040100
Reference42 articles.
1. Stochastic charge trapping in oxides: From random telegraph noise to bias temperature instabilities
2. The Paradigm Shift in Understanding the Bias Temperature Instability: From Reaction–Diffusion to Switching Oxide Traps
3. H. Reisinger, T. Grasser, W. Gustin, and C. Schlünder, in 2010 IEEE International Reliability Physics Symposium (IEEE, 2010), p. 7.
4. A Comparison of Very Fast to Very Slow Components in Degradation and Recovery Due to NBTI and Bulk Hole Trapping to Existing Physical Models
5. Voltage-Dependent Activation Energy Maps for Analytic Lifetime Modeling of NBTI Without Time Extrapolation
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1. Rapidly Exploring Random Trees with Physics-Informed Neural Networks for Constrained Energy-Optimal Rendezvous Problems;The Journal of the Astronautical Sciences;2024-02-01
2. Bias Temperature Instability of MOSFETs: Physical Processes, Models, and Prediction;Electronics;2022-04-28
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