TCAD Device Simulation With Graph Neural Network
Author:
Affiliation:
1. Innovation Center, Computational Science and Engineering Team, Samsung Electronics, Hwaseong-si, South Korea
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Electronic, Optical and Magnetic Materials
Link
http://xplorestaging.ieee.org/ielx7/55/10199118/10168926.pdf?arnumber=10168926
Reference19 articles.
1. MixHop: Higher-order graph convolutional architectures via sparsified neighborhood mixing;abu-el-haija;Proc 36th Int Conf Mach Learn (ICML),2019
2. Improving breadth-wise backpropagation in graph neural networks helps learning long-range dependencies;lukovnikov;Proc Int Conf Mach Learn,2021
3. Scattered data interpolation for computer graphics
4. Observations on the behavior of radial basis function approximations near boundaries
5. Deeper Insights Into Graph Convolutional Networks for Semi-Supervised Learning
Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. ML-TCAD: Perspectives and Challenges on Accelerating Transistor Modeling using ML;2023 ACM/IEEE 5th Workshop on Machine Learning for CAD (MLCAD);2023-09-10
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