APA
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Kushwaha, S., Guglani, S., Soleimani, N., Pathania, S., Kumar, S., Trinchero, R., … Sharma, R. (2023). Space Mapped Neuromodeling for Fast & Accurate Signal Integrity Analysis of Rough On-chip Copper Interconnects. Electrical Design of Advanced Packaging and Systems Symposium.
Chicago/Turabian
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Kushwaha, Suyash, Surila Guglani, N. Soleimani, Sunil Pathania, Somesh Kumar, Riccardo Trinchero, Sourajeet Roy, and Rohit Sharma. “Space Mapped Neuromodeling for Fast &Amp; Accurate Signal Integrity Analysis of Rough On-Chip Copper Interconnects.” Electrical Design of Advanced Packaging and Systems Symposium (2023).
MLA
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Kushwaha, Suyash, et al. “Space Mapped Neuromodeling for Fast &Amp; Accurate Signal Integrity Analysis of Rough On-Chip Copper Interconnects.” Electrical Design of Advanced Packaging and Systems Symposium, 2023.
BibTeX Click to copy
@article{suyash2023a,
title = {Space Mapped Neuromodeling for Fast & Accurate Signal Integrity Analysis of Rough On-chip Copper Interconnects},
year = {2023},
journal = {Electrical Design of Advanced Packaging and Systems Symposium},
author = {Kushwaha, Suyash and Guglani, Surila and Soleimani, N. and Pathania, Sunil and Kumar, Somesh and Trinchero, Riccardo and Roy, Sourajeet and Sharma, Rohit}
}
In this paper, an accurate modeling of on-chip copper interconnects with surface roughness is performed considering the parametric variability. This modeling is highly accurate as per-unit-length parameters of the on-chip rough copper interconnects are extracted via full wave EM solver. Further, a space-mapped artificial neural network (ANN) is developed for accurate prediction of eye height and eye width from the geometrical and material parameters of the rough copper interconnects. The novel space-mapping ANN developed in this work is more efficient in terms of accuracy and requires fewer training samples when compared to conventional ANNs.