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2012 IEEE 21st Asian Test Symposium (2012)
Niigata, Japan Japan
Nov. 19, 2012 to Nov. 22, 2012
ISSN: 1081-7735
ISBN: 978-1-4673-4555-2
pp: 314-319
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.
Artificial neural networks, Shape, Vectors, Dictionaries, Neurons, Training, Support vector machine classification, Artificial Neural Network (ANN), Memory Test, Memory Failure Bitmaps, Dictionary Based Pattern Matching, Machine Learning

J. Li et al., "A Hybrid Flow for Memory Failure Bitmap Classification," 2012 IEEE 21st Asian Test Symposium(ATS), Niigata, Japan Japan, 2012, pp. 314-319.
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