A Deep Transfer Learning Design Rule Checker With Synthetic Training

被引：1

作者：

Francisco, Luis ^{[1
]}

Davis, W. Rhett ^{[1
]}

Franzon, Paul ^{[1
]}

机构：

[1] North Carolina State Univ, Dept Elect & Comp Engn, Raleigh, NC 27606 USA

来源：

IEEE DESIGN & TEST | 2023年 / 40卷 / 01期

基金：

美国国家科学基金会;

关键词：

Layout; Design methodology; Convolutional neural networks; Transfer learning; Generators; Deep learning; Manuals; Training data; Design Rule Checking; Machine Learning; IC Verification; Physical Verification; Convolutional Neural Network; Deep Learning; Synthetic Data Training; Transfer Learning;

D O I：

10.1109/MDAT.2022.3162786

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Deep transfer learning is applied to the task of design rule checking (DRC). A parameterized synthetic data set generator is used to train the model. -Ulf Schlichtmann, Technical University of Munich

引用

页码：77 / 84

页数：8

共 12 条

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