000			03378ntm a2200325 i 4500
999			_c100697 _d100703
003			MY-KuUP
005			20251125110850.0
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008			240424t20232023my a|||fr|||| 000 0 eng d
020			_aTHE0009838 (Local) _qHardback
040			_aUMPSA _beng _cUMP _erda
090			_aFTKPM .S55 2023 r Thesis
100	1		_aLim Shi Xuen, _eauthor.
245	1	0	_aThe formulation of a transfer learning pipeline for the classification of the wafer defects / _cLim Shi Xuen
264		1	_aKuantan, Pahang : _bUMPSA, _c2023
264		4	_c©2023
300			_axii, 102 pages : _billustrations (some color) ; _c30 cm. + _e1 CD-ROM
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
347			_2rda _atext file _bPDF
500			_aFaculty of Manufacturing and Mechatronic Engineering Technology
502			_aThesis (Master of Science) -- Universiti Malaysia Pahang – 2023
504			_aIncludes bibliographical references
520	3		_aIn a semiconductor manufacturing process, a semiconductor wafer may have defects which are unacceptable due to its complexity in manufacturing process. Defect detection in wafer is vital in avoiding yield loss in end product, which is often achieved by visual judgement using an optical microscope. This often causes misjudgment and inconsistency result between different personnel. Automated processes have been used commonly in recent years, with the judgement done by using conventional image processing algorithm. However, limitations such as robustness and difficulty in setting up the parameters required for image processing algorithm encourages the investigation in using Deep learning classification in detecting the wafer defects. Deep learning classification produces excellent and robust results in classifying defects in wafer images, but challenges in data scarcity and hyperparameter tuning hampers its implementation in the industry. To combat this challenge, Transfer learning (TL) is investigated to utilize pretrained weights from other models, which in turns produces similar excellent results while improving the efficiency of implementation. Thus far, there are still limited studies that investigate the classification of wafer defects using TL combined with a classical Machine learning (ML) pipeline. Thus, this study will aim to explore 17 types of TL models, and classify the features extracted using 3 different ML algorithms, namely Support Vector Machine (SVM), k-Nearest Neighbor (kNN) and Random Forest (RF). The ML classifiers were tuned via a 5-fold cross-validation technique through grid search approach. The input images were split into a stratified ratio of 60:20:20 ratio as training, validation and testing set respectively. Different combinations of TL-ML pipeline were evaluated and compared to obtain the best performing pipeline by various performance measures such as the classification accuracy, confusion matrix, precision, sensitivity and F1 score. It is observed that the ResNet101v2 model pairing up with an optimized SVM pipeline is able to achieve the best classification accuracy of 95% for training, validation and testing data
610	2	0	_aFaculty of Manufacturing and Mechatronic Engineering Technology _xDissertations
650		0	_aUniversities and colleges _xDissertations
942			_2lcc _cTHESIS