Classification of IC Process Deformation Characteristics Using Memory Fail Bitmaps

2004 ◽  
Author(s):  
T. Zanon ◽  
W. Maly
Keyword(s):  
Geometric Characterization ◽  
Deformation Characteristics ◽  
Analysis Study ◽  
Preliminary Results ◽  
Failure Patterns ◽  
Yield Learning ◽  
Sram Memory ◽  
Viable Approach ◽  
Ic Technology

Abstract Building a portfolio of deformations is the key step for building better defect models for the test and yield learning domain. A viable approach to achieve this goal is through geometric characterization and classification of failure patterns found on memory fail bitmaps. In this paper, we present preliminary results on how to build such a portfolio of deformations for an IC technology of interest based on a fail bitmap analysis study conducted on large, modern SRAM memory products.

A Study on Multi Class Classification from Breast Cancer Images using Ensemble Network and Transfer Learning

2020 ◽  
Vol 14 ◽  
Author(s):  
Lahari Tipirneni ◽  
Rizwan Patan
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Convolutional Neural Network ◽  
Binary Classification ◽  
Disease Diagnosis ◽  
Feature Descriptors ◽  
Histopathological Images ◽  
Viable Approach ◽  
Multi Class Classification

Abstract:: Millions of deaths all over the world are caused by breast cancer every year. It has become the most common type of cancer in women. Early detection will help in better prognosis and increases the chance of survival. Automating the classification using Computer-Aided Diagnosis (CAD) systems can make the diagnosis less prone to errors. Multi class classification and Binary classification of breast cancer is a challenging problem. Convolutional neural network architectures extract specific feature descriptors from images, which cannot represent different types of breast cancer. This leads to false positives in classification, which is undesirable in disease diagnosis. The current paper presents an ensemble Convolutional neural network for multi class classification and Binary classification of breast cancer. The feature descriptors from each network are combined to produce the final classification. In this paper, histopathological images are taken from publicly available BreakHis dataset and classified between 8 classes. The proposed ensemble model can perform better when compared to the methods proposed in the literature. The results showed that the proposed model could be a viable approach for breast cancer classification.

scholarly journals MKJ and MSS Classification of Solar-Type Stars Within 100 Parsecs of the Sun: Preliminary Results

1985 ◽  
Vol 111 ◽  
pp. 407-410
Author(s):  
M. Fracassini ◽  
L. E. Pasinetti ◽  
M. Borella ◽  
A. Pasinetti
Keyword(s):  
Spectral Type ◽  
Selection Criteria ◽  
The Sun ◽  
Preliminary Results ◽  
Good Selection ◽  
Color Indices ◽  
Solar Type

A study of the distribution of spectral types of Solar Type Stars (STS) in the revised MKJ and MSS classifications is made on 3919 F8-K3 HD spectral-type stars brighter than mv=10. By means of the solar color indices U-B and B-V 697 STS were selected. The spectral types G3V and G5V have the highest percentages in MSS and MKJ, respectively, confirming statistically the results published by Keenan and Pitts (1980) and by Hardorp (1982). The distribution of the color indices U-B and B-V in the revised G2V spectral type shows that these are good selection criteria for STS and are in the range 0.06 ≤ U-B ≤ 0.10 and 0.58 ≤ B-V ≤ 0.65.

scholarly journals SIMPLE JORDAN CONFORMAL SUPERALGEBRAS

2008 ◽  
Vol 07 (04) ◽  
pp. 517-533 ◽  
Author(s):  
VICTOR G. KAC ◽  
ALEXANDER RETAKH
Keyword(s):  
Preliminary Results ◽  
Conformal Superalgebras

We classify simple finite Jordan conformal superalgebras and also establish preliminary results for the classification of simple finite Jordan pseudoalgebras.

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