Wafer Map Defect Patterns Semi-Supervised Classification Using Latent Vector Representation
As the globalization of semiconductor design and manufacturing processes continues, the demand for defect detection during integrated circuit fabrication stages is becoming increasingly critical, playing a significant role in enhancing the yield of semiconductor products. Traditional wafer map defect pattern detection methods involve manual inspection using electron microscopes to collect sample images, which are then assessed by experts for defects. This approach is labor-intensive and inefficient. Consequently, there is a pressing need to develop a model capable of automatically detecting defects as an alternative to manual operations. In this paper, we propose a method that initially employs a pre-trained VAE model to obtain the fault distribution information of the wafer map. This information serves as guidance, combined with the original image set for semi-supervised model training. During the semi-supervised training, we utilize a teacher-student network for iterative learning. The model presented in this paper is validated on the benchmark dataset WM-811K wafer dataset. The experimental results demonstrate superior classification accuracy and detection performance compared to state-of-the-art models, fulfilling the requirements for industrial applications. Compared to the original architecture, we have achieved significant performance improvement.
Code (0)
등록된 구현이 없습니다.
Tasks
Defect DetectionSimilar Papers 제목 키워드 기반
One-Class Classification for Wafer Map using Adversarial Autoencoder with DSVDD Prior
Recently, semiconductors' demand has exploded in virtual reality, smartphones, wearable devices, the internet of things, robotics, and automobiles. Semiconductor manufacturers want to make semiconductors with high yields…
ClassificationOne-Class ClassificationSePArate: Segmenting Patterns from Defects in Wafer Manufacturing Using Weak Supervision
In semiconductor manufacturing, defect analysis is essential, but manual inspection cannot scale. However, existing automated inspection methods remain insufficient for root-cause analysis and process optimization. To th…
Semiconductor Wafer Map Defect Classification with Tiny Vision Transformers
Semiconductor wafer defect classification is critical for ensuring high precision and yield in manufacturing. Traditional CNN-based models often struggle with class imbalances and recognition of the multiple overlapping …
ClassificationDefect DetectionWafer Map Defect Classification Using Autoencoder-Based Data Augmentation and Convolutional Neural Network
In semiconductor manufacturing, wafer defect maps (WDMs) play a crucial role in diagnosing issues and enhancing process yields by revealing critical defect patterns. However, accurately categorizing WDM defects presents …
ClassificationData AugmentationDefect DetectionDiversityA novel approach for wafer defect pattern classification based on topological data analysis
In semiconductor manufacturing, wafer map defect pattern provides critical information for facility maintenance and yield management, so the classification of defect patterns is one of the most important tasks in the man…
ClassificationManagementTopological Data Analysis