Development of Knowledge-Based Artificial Neural Networks for Analysis of PSIJ in CMOS Inverter Circuits
Author:
Affiliation:
1. Department of Electronics, Carleton University, Ottawa, Canada
2. IIT Jodhpur, Jodhpur, India
Funder
Natural Sciences and Engineering Research Council (NSERC) of Canada
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Condensed Matter Physics,Radiation
Link
http://xplorestaging.ieee.org/ielx7/22/10091956/09968046.pdf?arnumber=9968046
Reference34 articles.
1. Efficient Modeling of Power Supply Induced Jitter in Voltage-Mode Drivers (EMPSIJ)
2. Analytical Probability Density Calculation for Step Pulse Response of a Single-Ended Buffer With Arbitrary Power-Supply Voltage Fluctuations
3. Modeling of power supply induced jitter (PSIJ) transfer function at inverter chains
4. Device Parameter-Based Analytical Modeling of Power Supply Induced Jitter in CMOS Inverters
5. Efficient Jitter Analysis for a Chain of CMOS Inverters
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1. Prediction of Power Supply Induced Jitter via Deep Belief and Knowledge-Based Neural Networks;2024 IEEE 28th Workshop on Signal and Power Integrity (SPI);2024-05-12
2. Investigation of Temperature and Channel Dimension Effects on CMOS Circuit Performance;East European Journal of Physics;2024-03-05
3. Efficient Estimation of Power Supply Induced Jitter via Machine Learning;2023 IEEE 32nd Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS);2023-10-15
4. Data and domain knowledge dual‐driven artificial intelligence: Survey, applications, and challenges;Expert Systems;2023-08-14
5. PSIJ Transfer Function Response Prediction via NARNET and KBNNs;2023 21st IEEE Interregional NEWCAS Conference (NEWCAS);2023-06-26
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