Add to ORKGAutomation tools for semiconductor defect data analysis are becoming necessary as device density and wafer sizes continue to increase. These tools are needed to efficiently and robustly process the increasing amounts of data to quickly characterize manufacturing processes and accelerate yield learning. An image-based method is presented for analyzing process signatures from defect data distributions. Applications are presented of enhanced statistical process control, automatic process characterization, and intelligent sub-sampling of event distributions for off-line high-resolution defect review
Images of semiconductor defects are maintained in semiconductor yield-management systems to help dia...
Quality control is one of important process in semiconductor manufacturing. A lot of issues trying t...
Thesis: M. Eng. in Advanced Manufacturing and Design, Massachusetts Institute of Technology, Departm...
Automated inspection of semiconductor defect data has become increasingly important over the past se...
Project (M.S., Mechanical Engineering) -- California State University, Sacramento, 2010.Defect monit...
Automatic detection of defects during the fabrication of semiconductor wafers is largely automated, ...
Image data management in the semiconductor manufacturing environment is becoming more problematic as...
Semiconductor manufacturing test has traditionally been seen as a simple task that segregates good D...
The especially complex and precise nature of semiconductor fabrication often results in low yield ac...
This paper describes Spatial Signature Analysis (SSA), a cooperative research project between SEMATE...
Recurring defect cluster patterns on semiconductor wafers can be linked to imperfectness/faults in s...
It is well known that most of the defect clusters found on the fabricated semiconductor wafers have ...
This paper presents a vision of how a promising new technology called spatial signature analysis (SS...
As semiconductor device density and wafer area continue to increase, the volume of in-line and off-l...
This diploma thesis focuses on detecting defects in semiconductor wafer manufacturing. It explores m...
Images of semiconductor defects are maintained in semiconductor yield-management systems to help dia...
Quality control is one of important process in semiconductor manufacturing. A lot of issues trying t...
Thesis: M. Eng. in Advanced Manufacturing and Design, Massachusetts Institute of Technology, Departm...
Automated inspection of semiconductor defect data has become increasingly important over the past se...
Project (M.S., Mechanical Engineering) -- California State University, Sacramento, 2010.Defect monit...
Automatic detection of defects during the fabrication of semiconductor wafers is largely automated, ...
Image data management in the semiconductor manufacturing environment is becoming more problematic as...
Semiconductor manufacturing test has traditionally been seen as a simple task that segregates good D...
The especially complex and precise nature of semiconductor fabrication often results in low yield ac...
This paper describes Spatial Signature Analysis (SSA), a cooperative research project between SEMATE...
Recurring defect cluster patterns on semiconductor wafers can be linked to imperfectness/faults in s...
It is well known that most of the defect clusters found on the fabricated semiconductor wafers have ...
This paper presents a vision of how a promising new technology called spatial signature analysis (SS...
As semiconductor device density and wafer area continue to increase, the volume of in-line and off-l...
This diploma thesis focuses on detecting defects in semiconductor wafer manufacturing. It explores m...
Images of semiconductor defects are maintained in semiconductor yield-management systems to help dia...
Quality control is one of important process in semiconductor manufacturing. A lot of issues trying t...
Thesis: M. Eng. in Advanced Manufacturing and Design, Massachusetts Institute of Technology, Departm...