Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/78800
Title: Classification of defects in semiconductor wafer using artificial intelligence
Authors: Lit, Yek Kit
Keywords: Engineering::Mechanical engineering
Issue Date: 2019
Abstract: Machine learning, a subset of artificial intelligence is an emerging technology that enabled the classification of objects without the need of being explicitly programmed. Due to the popularity of artificial intelligence, many frameworks were invented. ANN, CNN, Faster RCNN will be explained to understand the fundamentals of machine learning. However, the focus of this project is on the framework Mask-RCNN, developed by Facebook, it uses region-based convolutional neural network that simultaneously perform object detection and instance segmentation. This project comprises of two important part. The first step is to obtain the datasets in the form of images in large numbers of a 1000. The images are annotated by drawing polygons on the region of interest and a json file is obtained. The Mask R-CNN is downloaded on the computer and a virtual environment is created, dependencies are installed for training to take place. The second part includes running the training to obtain the h5 files. Detection is run to determine the success of the training. The whole process will be repeated if the detection is unable to produce the results needed.
URI: http://hdl.handle.net/10356/78800
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:MAE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
FYP REPORT LIT YEK KIT.pdf
  Restricted Access
Main article1.99 MBAdobe PDFView/Open

Page view(s)

146
Updated on Jun 19, 2021

Download(s) 50

18
Updated on Jun 19, 2021

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.