Machine vision algorithms for semiconductor wafer inspection: a project with Inspex

1998 ◽  
Author(s):  
Chi Hau Chen ◽  
Tzu-Hung Cheng ◽  
Wo-Tak Wu ◽  
Shawn Driscoll
Keyword(s):  
Machine Vision ◽  
Semiconductor Wafer ◽  
Wafer Inspection

Machine Vision for Semiconductor Wafer Inspection

1989 ◽  
pp. 283-314 ◽  
Author(s):  
Ramesh Jain ◽  
A. Ravishankar Rao ◽  
Ali Kayaalp ◽  
Charles Cole
Keyword(s):  
Machine Vision ◽  
Semiconductor Wafer ◽  
Wafer Inspection

Defect Segmentation of Semiconductor Wafer Image Using k-Means Clustering

2015 ◽  
Vol 815 ◽  
pp. 374-379 ◽  
Author(s):  
N.H. Saad ◽  
N. Mohamad Sabri ◽  
A.F. Hasan ◽  
Azuwa Ali ◽  
Hariyanti Mohd Saleh
Keyword(s):  
Machine Vision ◽  
Manufacturing Industry ◽  
Clustering Algorithm ◽  
Vision System ◽  
Semiconductor Wafer ◽  
Spatial Features ◽  
Two Stages ◽  
Wafer Manufacturing ◽  
Specific Number ◽  
Human Inspection

Nowadays, quality control becomes an important issue in semiconductor manufacturing industry. The rate of production with respect to time gives a lot of issues in the industry. In most semiconductor assemblies, a lot of defects generated from various processes in semiconductor wafer manufacturing need to be inspected manually using human experts and this process required full concentration of the operators. This human inspection procedure, however, is time consuming and highly subjective. In order to overcome this problem, implementation of machine vision will be the best solution. This paper presents defect segmentation of semiconductor wafer image based on colour features with k-Means clustering algorithm which can be adopted in machine vision system. In this work, the segmentation process is carried out in two stages. The first stage comprised of clustering the pixels in the image based on their colour and spatial features. Then the clustered pixels are merged to a specific number of regions. The proposed approach is being evaluated using defected wafer image. The experimental results show that it can be used to segment the defect correctly. By using this method, it is possible to increase the computational efficiency since it will avoid feature extraction for every pixel in the image.

scholarly journals Tracking and Synchronization with Inversion-Based ILC for a Multi-Actuator-Driven Wafer Inspection Cartridge Transport Robot System

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2904
Author(s):  
Dong Jun Oh ◽  
Seung Guk Baek ◽  
Kyung-Tae Nam ◽  
Ja Choon Koo
Keyword(s):  
Iterative Learning Control ◽  
Control Method ◽  
Iterative Learning ◽  
Synchronization Control ◽  
System Control ◽  
Input Output ◽  
Semiconductor Wafer ◽  
Robot System ◽  
Output Data ◽  
Wafer Inspection

This paper proposes a simple tracking and synchronization control of a dual-drive system using inversion-based iterative learning control (IILC), which reformulates the model at each iteration based on input/output data. By the power of the IILC, this work simplifies the dual-actuator-driven dynamic system control problem that is normally addressed with a MIMO method. This work also shows the potential of the IILC for nonlinear system applications by reformulating the model at each iteration based on the input/output data. An analytical representation of the iteration-varying IILC followed by simulations is provided. A set of physical system testings with a dual-motor gantry and a semiconductor wafer inspection robotic system are carried out to verify the control method.

scholarly journals Improving Semiconductor Wafer Inspection

2020 ◽  
Vol 17 (6) ◽  
pp. 57-59
Author(s):  
Siva Hemanth Valluru
Keyword(s):  
Semiconductor Wafer ◽  
Wafer Inspection

Machine Vision

2009 ◽  
Author(s):  
Wesley E. Snyder ◽  
Hairong Qi
Keyword(s):  
Machine Vision
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