Developing the new ADC algorithm that enables to identify the defect source

2007 ◽  
Author(s):  
Po-Yueh Tsai ◽  
Wen-Feng Chiu ◽  
To-Yu Chen ◽  
Fumiaki Endo ◽  
Yuko Kariya ◽  
...  
Keyword(s):  
Defect Source

A Study to Remove Heavy Polymer Remain on Thick Metal (> 3 micron) Sidewall Profile after Metal Etch Solvent Clean Step

2008 ◽  
Author(s):  
S. F. Liew ◽  
K. A. Mohammad ◽  
L. J. Liu ◽  
S.F. Chong ◽  
D. G. Lee ◽  
...  
Keyword(s):  
Electron Microscope ◽  
Corrosion Test ◽  
Profile Analysis ◽  
Metal Corrosion ◽  
Source Analysis ◽  
Metal Line ◽  
Success Criteria ◽  
Aluminum Metal ◽  
Defect Source ◽  
Scanning Electron

Abstract Heavy polymer residue has been observed on the sidewall of thick metal during the process release. The thickness of metal line is more than 3 micron. This thick polymer residue on the aluminum metal sidewall is seen from tilt Scanning Electron Microscope (SEM) profile analysis. This polymer residue on the metal sidewall with chlorine (Cl2) trapped will result in metal corrosion. The focus on this paper is on the removal of this polymer residue on the thick metal sidewall. The experiments were run with splits of varying the chemical dispensing time and the rinsing time in the process. The success criteria are determined by passing the Defect Source Analysis (DSA) and tilt SEM profile analysis. These wafers are sent for electrical test, wet box test (corrosion test) and electrical sort test to ensure the reliability of the post metal cleaning condition.

Identification of defect source to control reticle defect density for CAR and dry etching in the photomask process

2003 ◽  
Author(s):  
Sung-Yong Cho ◽  
Won-Suk Ahn ◽  
Won-Il Cho ◽  
Moon-Gyu Sung ◽  
Yong-Hoon Kim ◽  
...  
Keyword(s):  
Defect Density ◽  
Dry Etching ◽  
Defect Source

Reactive Sampling for Efficient Defect Source Identification

2016 ◽  
Vol 29 (2) ◽  
pp. 104-115
Author(s):  
Mohamad Chakaroun ◽  
Mustapha Ouladsine ◽  
Mohand Djeziri ◽  
Jacques Pinaton
Keyword(s):  
Source Identification ◽  
Defect Source

Atomistic Monte Carlo Simulations of Homogeneous and Heterogeneous Precipitation on Grain Boundaries of NbC in Steels

2005 ◽  
Vol 237-240 ◽  
pp. 721-726 ◽  
Author(s):  
Céline Hin ◽  
Frédéric Soisson ◽  
Philippe Maugis
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Grain Boundaries ◽  
Vacancy Concentration ◽  
Recrystallization Process ◽  
Thermal Treatments ◽  
Heterogeneous Precipitation ◽  
Defect Source ◽  
Equilibrium Segregation ◽  
Diffusion Properties

The precipitation of niobium carbides in industrial steels is commonly used to control the recrystallization process or the amount of interstitial atoms in solid solution. It is then important to understand the precipitation kinetics and especially the competition between homogeneous and heterogeneous precipitation, since both of them have been observed experimentally, depending on the alloy composition, microstructure and thermal treatments. We propose Monte Carlo simulations of NbC precipitation in α-iron, based on a simple atomic description of the main parameters which control the kinetic pathway : - realistic diffusion properties, with a rapid diffusion of C atoms by interstitial jumps and a slower diffusion of Fe and Nb atoms by vacancy jumps - a simple model of grain boundaries which reproduces the equilibrium segregation properties of Nb and C - a point defect source which drives the vacancy concentration towards its equilibrium value. Depending on the precipitation conditions, MC simulations predict different kinetic behaviours, including homogeneous and heterogeneous NbC precipitation, early segregation of C atoms and its importance as a first stage for NbC precipitation, wetting phenomena on grain boundaries and transient precipitation of metastable carbides.

Defect source analysis of directed self-assembly process (DSA of DSA)

2013 ◽  
Author(s):  
Paulina Rincon Delgadillo ◽  
Ryota Harukawa ◽  
Mayur Suri ◽  
Stephane Durant ◽  
Andrew Cross ◽  
...  
Keyword(s):  
Self Assembly ◽  
Source Analysis ◽  
Assembly Process ◽  
Defect Source

scholarly journals Fractal Hilbert sensor to detect partial discharge on transformer

2020 ◽  
Vol 1 (2) ◽  
pp. 94
Author(s):  
Herman Halomoan Sinaga
Keyword(s):  
High Frequency ◽  
Partial Discharge ◽  
Single Layer ◽  
Defect Model ◽  
Tem Cell ◽  
Sensor Sensitivity ◽  
Closed Type ◽  
Defect Source ◽  
Transformer Insulation ◽  
Frequency Pulse

The design of fractal Hilbert sensor is presented in this paper. The sensor is intended to detect partial discharge (PD) in transformer insulation. The fractal Hilbert sensor designed using 4 order fractal and etching on single layer PCB board. The Hilbert fractal type is chosen as this type of sensor can be built on smaller area compare to other types. The sensor overall dimension is limited to 5x5 cm as it is proposed to be use to detect the PD presence in transformer. The sensor sensitivity is tested using a closed type TEM cell. After the sensitivity of the sensor is tested the sensor then applied to detect the PD signals emitted by void PD defect model. The results show the sensor has sensitivity as high as 10 dB. The sensor also has capability to detect the PD signals generated by the PD defect source. The waveforms captured by the sensor show the sensors can capture high frequency pulse generated by the PD source.  Keywords: Partial Discharge (PD), Fractal Hilbert Sensor UHF, Sensor Sensitivity

Identification of defect source to control reticle defect density for CAR and dry etching in the photomask process

2002 ◽  
Author(s):  
Sung-Yong Cho ◽  
Won-Suk Ahn ◽  
Won-Il Cho ◽  
Moon-Gyu Sung ◽  
Seong-Yong Moon ◽  
...  
Keyword(s):  
Defect Density ◽  
Dry Etching ◽  
Defect Source

The AE Identification Methods to Welding Defect by Wavelet Analysis

2011 ◽  
Vol 63-64 ◽  
pp. 355-360 ◽  
Author(s):  
Yan Tao Dou ◽  
Xiao Li Xu ◽  
Xiao Jun Cai ◽  
Guo Xin Wu ◽  
Zhi Xiang Sun
Keyword(s):  
Acoustic Emission ◽  
Failure Process ◽  
Emission Source ◽  
Engineering Practice ◽  
Identification Methods ◽  
Welding Defects ◽  
Acoustic Emission Source ◽  
Welding Defect ◽  
Defect Source ◽  
Bending Failure

In the engineering applications area, welding defect is a major hidden danger of structure security. the bending failure process of welded specimens is detected by using AE technique and the data samples of typical welding defect source are collected, and by using wavelet technique the typical AE datas acquired through experiment are analyzed, characteristic information of the typical acoustic emission source such as electromagnetic noise, plastic deformation, micro-crack initiation, crack unsteady expansion and fracture, etc are extracted. A serial acoustic emission source identification methods based on the energy spectrum coefficients of wavelet are established and which can realize accurately distinguishing of different acoustic emission sources, so as to provide a theoretical basis to detect equipment welding defects by acoustic emission technology dynamic in engineering practice.

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