A Robustand Dynamic Fault Detectionand Classficationof Semi-Conductorsusing Logistic Regression
Main Article Content
Abstract
In the semiconductor industry, the singular goal for automatic graphical assessment is to detect and categorize fabrication defects exploiting advanced image processing practices. The manufacturer main aim to detect the patterns at the quickest feasible recognition of defect models accepts quality management and computerization of production supply chains, companies benefit from a boosted yield and lowered production costs. Because conventional image processing systems are constrained in their capability to detect innovative defect models, and artificial intelligence methodology every so often involve a incredible amount of computational determination, this research presents a innovative deep neural network-based hybrid method.
Downloads
Metrics
Article Details
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
