Machine Learning for Automated Etch Pit Counting on As-sliced Surface of Multicrystalline Silicon

Author(s):  
Takuto Kojima ◽  
Kohei Onishi ◽  
Atsushi Ogura ◽  
Kenji Fukui ◽  
Manabu Komoda ◽  
...  
Author(s):  
Sergio Castellanos ◽  
Jasmin Hofstetter ◽  
Maulid Kivambe ◽  
Markus Rinio ◽  
Barry Lai ◽  
...  

2014 ◽  
Vol 115 (18) ◽  
pp. 183511 ◽  
Author(s):  
S. Castellanos ◽  
M. Kivambe ◽  
J. Hofstetter ◽  
M. Rinio ◽  
B. Lai ◽  
...  

2013 ◽  
Vol 205-206 ◽  
pp. 65-70
Author(s):  
Ali Ghaderi ◽  
Semih Senkader

A major performance limiting factor of multicrystalline silicon wafers is structural defects, mainly dislocations, reducing solar cell efficiency. Dislocations are formed during crystallisation process. Characterization of dislocation-content is necessary both to optimise the crystallisation and to eliminate bad wafers before cell processing. We developed two techniques to characterise dislocations: conventional etch-pit counting modified for full size wafers using a new etch-recipe and a novel etch-pit counting algorithm. Secondly we developed a technique to estimate the dislocation content directly from photoluminescence images of as-cut wafers.


2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.


Author(s):  
Y. Pan

The D defect, which causes the degradation of gate oxide integrities (GOI), can be revealed by Secco etching as flow pattern defect (FPD) in both float zone (FZ) and Czochralski (Cz) silicon crystal or as crystal originated particles (COP) by a multiple-step SC-1 cleaning process. By decreasing the crystal growth rate or high temperature annealing, the FPD density can be reduced, while the D defectsize increased. During the etching, the FPD surface density and etch pit size (FPD #1) increased withthe etch depth, while the wedge shaped contours do not change their positions and curvatures (FIG.l).In this paper, with atomic force microscopy (AFM), a simple model for FPD morphology by non-crystallographic preferential etching, such as Secco etching, was established.One sample wafer (FPD #2) was Secco etched with surface removed by 4 μm (FIG.2). The cross section view shows the FPD has a circular saucer pit and the wedge contours are actually the side surfaces of a terrace structure with very small slopes. Note that the scale in z direction is purposely enhanced in the AFM images. The pit dimensions are listed in TABLE 1.


2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  

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