Abstract
This paper is a rethinking of the conventional method of moments (MoM) using the modern machine learning (ML) technology. By repositioning the MoM matrix and unknowns in an artificial neural network (ANN), the conventional linear algebra MoM solving is changed into a machine learning training process. The trained result is the solution. As an application, the parasitic capacitance extraction broadly needed by VLSI modeling is solved through the proposed new machine learning based method of moments (ML-MoM). The multiple linear regression (MLR) is employed to train the model. The computations are done on Amazon Web Service (AWS). Benchmarks demonstrated the interesting feasibility and efficiency of the proposed approach. According to our knowledge, this is the first MoM truly powered by machine learning methods. It opens enormous software and hardware resources for MoM and related algorithms that can be applied to signal integrity and power integrity simulations.
Recommended Citation
H. M. Yao et al., "Machine Learning Based MoM (ML-MoM) For Parasitic Capacitance Extractions," 2016 IEEE Electrical Design of Advanced Packaging and Systems Symposium, EDAPS 2016, pp. 171 - 173, article no. 7893155, Institute of Electrical and Electronics Engineers, Apr 2017.
The definitive version is available at https://doi.org/10.1109/EDAPS.2016.7893155
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
Artificial Neural Network; Capacitance Extraction; Machine Learning; Method of Moments
International Standard Book Number (ISBN)
978-150906184-6
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
05 Apr 2017
