Deep learning driven silicon wafer defect segmentation and classification
Integrated Circuits are made of various transistors that are embedded on a silicon wafer, these wafers are difficult to process and hence are prone to defects. Defecting these defects manually is a time consuming and labour-intensive task and hence automation is necessary. Deep Learning approach is...
Saved in:
Main Authors: | Rohan Ingle, Aniket K. Shahade, Mayur Gaikwad, Shruti Patil |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2025-06-01
|
Series: | MethodsX |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016125000068 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Wafer Defect Classification Algorithm With Label Embedding Using Contrastive Learning
by: Jeongjoon Hwang, et al.
Published: (2025-01-01) -
Semi-Supervised Learning With Wafer-Specific Augmentations for Wafer Defect Classification
by: Uk Jo, et al.
Published: (2025-01-01) -
YOLOSeg with applications to wafer die particle defect segmentation
by: Yen-Ting Li, et al.
Published: (2025-01-01) -
Investigation of the Effects of Wafer-Baking Plates on Thermal Distribution, Wafer Thickness, and Wafer Color Distribution
by: Uğur Köklü, et al.
Published: (2025-01-01) -
STUDY OF SILICON-INSULATOR STRUCTURE DEFECTS BASED ON ANALYSIS OF A SPATIAL DISTRIBUTION OF A SEMICONDUCTOR WAFERS’ SURFACE POTENTIAL
by: R. I. Vorobey, et al.
Published: (2015-03-01)