Internal Defect Identification of Arc Magnets Based on a Deep Residual Network Combined with GRU and SqueezeNet

2021 ◽  
Author(s):  
Ying Zhou ◽  
Qinyuan Huang ◽  
Tian Yang ◽  
Qiang Li
Keyword(s):  
Residual Network ◽  
Internal Defect ◽  
Defect Identification

Systematic Defect Identification through Layout Snippet Clustering

2010 ◽  
Author(s):  
Wing Chiu Tam ◽  
Osei Poku ◽  
R. D. (Shawn) Blanton
Keyword(s):  
Design Process ◽  
Integrated Circuit ◽  
Yield Loss ◽  
Defect Identification ◽  
Clustering Techniques ◽  
Dominant Component

Abstract Systematic defects due to design-process interactions are a dominant component of integrated circuit (IC) yield loss in nano-scaled technologies. Test structures do not adequately represent the product in terms of feature diversity and feature volume, and therefore are unable to identify all the systematic defects that affect the product. This paper describes a method that uses diagnosis to identify layout features that do not yield as expected. Specifically, clustering techniques are applied to layout snippets of diagnosis-implicated regions from (ideally) a statistically-significant number of IC failures for identifying feature commonalties. Experiments involving an industrial chip demonstrate the identification of possible systematic yield loss due to lithographic hotspots.

Application of Fast Laser Deprocessing Techniques in Physical Failure Analysis on SRAM Memory of Advance Technology

2014 ◽  
Author(s):  
H.H. Yap ◽  
P.K. Tan ◽  
G.R. Low ◽  
M.K. Dawood ◽  
H. Feng ◽  
...  
Keyword(s):  
Failure Analysis ◽  
Integrated Circuit ◽  
Cost Effective ◽  
Tetraethyl Orthosilicate ◽  
Ic Design ◽  
Advance Technology ◽  
Defect Identification ◽  
Technology Scaling ◽  
And Function ◽  
Metal Layers

Abstract With technology scaling of semiconductor devices and further growth of the integrated circuit (IC) design and function complexity, it is necessary to increase the number of transistors in IC’s chip, layer stacks, and process steps. The last few metal layers of Back End Of Line (BEOL) are usually very thick metal lines (>4μm thickness) and protected with hard Silicon Dioxide (SiO2) material that is formed from (TetraEthyl OrthoSilicate) TEOS as Inter-Metal Dielectric (IMD). In order to perform physical failure analysis (PFA) on the logic or memory, the top thick metal layers must be removed. It is time-consuming to deprocess those thick metal and IMD layers using conventional PFA workflows. In this paper, the Fast Laser Deprocessing Technique (FLDT) is proposed to remove the BEOL thick and stubborn metal layers for memory PFA. The proposed FLDT is a cost-effective and quick way to deprocess a sample for defect identification in PFA.

scholarly journals A SYMMETRIC FULLY CONVOLUTIONAL RESIDUAL NETWORK WITH DCRF FOR ACCURATE TOOTH SEGMENTATION

IEEE Access ◽  
2020 ◽  
pp. 1-1
Author(s):  
Yunbo Rao ◽  
Yilin Wang ◽  
Fanman Meng ◽  
Jiansu Pu ◽  
Jihong Sun ◽  
...  
Keyword(s):  
Residual Network ◽  
Tooth Segmentation
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