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Physics-Prior Bayesian Neural Networks in Semiconductor Processing
Chun Han Chen
, Parag Parashar
, Chandni Akbar
, Sze Ming Fu
, Ming Ying Syu
,
Albert Lin
*
*
此作品的通信作者
電子研究所
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11
引文 斯高帕斯（Scopus）
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Keyphrases
Technology Computer-aided Design
100%
Semiconductor Processing
100%
Bayesian Neural Network
100%
Physical Model
66%
Integrated Circuit Manufacturing
33%
Mean Square Error
33%
Fabrication Methods
33%
Etching Depth
33%
Semiconductors
33%
Prior Distribution
33%
Downscaling
33%
Etching Process
33%
Manufacturing Technology
33%
Statistical Model
33%
Bayesian Framework
33%
Posterior Distribution
33%
Zero Mean
33%
Hidden Layer
33%
Variational Inference
33%
Anomalous Effects
33%
Machine Learning Models
33%
Process Inputs
33%
Aspect Ratio Dependent Etching
33%
Experimental Cost
33%
Kullback-Leibler Divergence Minimization
33%
Parameter Depth
33%
Design Prior
33%
Mathematics
Neural Network
100%
Aided Design
100%
Bayesian Prior
100%
Bayesian
66%
Experimental Data
66%
Physical Model
66%
Standard Deviation
33%
Gaussian Distribution
33%
Mean Square Error
33%
Aspect-Ratio
33%
Kullback-Leibler Divergence
33%
Input Parameter
33%
Posterior Distribution
33%
Test Set
33%
Engineering
Computer Aided Design
100%
Physical Model
66%
Input Parameter
33%
Mean Square Error
33%
Normal Distribution
33%
Etching Process
33%
Manufacturing Engineering
33%
Bayesian Framework
33%
Posterior Distribution
33%
Limited Amount
33%
Hidden Layer
33%
Kullback-Leibler Divergence
33%
Learning System
33%
Integrated Circuit
33%
Mathematical Model
33%
Aspect Ratio
33%
Computer Science
Neural Network
100%
Computer Aided Design
100%
Physical Model
66%
Normal Distribution
33%
Approximation (Algorithm)
33%
Bayesian Framework
33%
Leibler Divergence
33%
Posterior Distribution
33%
Input Parameter
33%
Machine Learning
33%
Learning System
33%
Integrated Circuit
33%
Chemical Engineering
Neural Network
100%
Learning System
50%