DocumentCode :
2714096
Title :
A Hybrid Flow for Memory Failure Bitmap Classification
Author :
Li, Jianbo ; Huang, Yu ; Cheng, Wu-Tung ; Schuermyer, Chris ; Xiang, Dong ; Faehn, Eric ; Farrugia, Ruth
fYear :
2012
fDate :
19-22 Nov. 2012
Firstpage :
314
Lastpage :
319
Abstract :
Failure bitmaps of manufactured memory arrays may contain the information of some systematic defects and have hence been used to monitor the process and to improve the memory yield. It is important to have an accurate flow to classify the memory failure bitmap signatures. The memory bitmap signature classification can be either dictionary based or machine learning based. This paper introduces a hybrid flow that can combine dictionary based and machine learning based methods. The proposed method can enhance the accuracy of signature classification, and more importantly, it has the capability of learning new memory bitmap signatures unseen before.
Keywords :
learning (artificial intelligence); storage management; hybrid flow; machine learning; memory arrays; memory bitmap signature classification; memory failure bitmap classification; memory failure bitmap signatures; memory yield; Artificial neural networks; Dictionaries; Neurons; Shape; Support vector machine classification; Training; Vectors; Artificial Neural Network (ANN); Dictionary Based Pattern Matching; Machine Learning; Memory Failure Bitmaps; Memory Test;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Test Symposium (ATS), 2012 IEEE 21st Asian
Conference_Location :
Niigata
ISSN :
1081-7735
Print_ISBN :
978-1-4673-4555-2
Electronic_ISBN :
1081-7735
Type :
conf
DOI :
10.1109/ATS.2012.16
Filename :
6394222
Link To Document :
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