# Wafer Map Defect Classification Using Autoencoder-Based Data Augmentation and Convolutional Neural Network

arXiv: https://arxiv.org/abs/2411.11029 · HTML: https://mlsanity.com/paper/2411.11029/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 36 references that are in the map.
- First posted on arXiv 17 Nov 2024.

## Builds on (most cited first, 3 of 3)

- [An Evaluation of Large Language Models in Bioinformatics Research](https://mlsanity.com/paper/2402.13714/) (2024)
- [Wafer Map Defect Patterns Semi-Supervised Classification Using Latent Vector Representation](https://mlsanity.com/paper/2311.12840/) (2023)
- [SEMI-CenterNet: A Machine Learning Facilitated Approach for Semiconductor Defect Inspection](https://mlsanity.com/paper/2308.07180/) (2023)

## Built on by (most cited first, 2 of 2)

- [WaferSAGE: Large Language Model-Powered Wafer Defect Analysis via Synthetic Data Generation and Rubric-Guided Reinforcement Learning](https://mlsanity.com/paper/2604.27629/) (2026)
- [Advanced Clustering Framework for Semiconductor Image Analytics Integrating Deep TDA with Self-Supervised and Transfer Learning Techniques](https://mlsanity.com/paper/2505.03848/) (2025)

## Abstract (arXiv)

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 significant challenges due to noisy data, unbalanced defect classes, and the complexity of failure modes. To address these challenges, this study proposes a novel method combining a self-encoder-based data augmentation technique with a convolutional neural network (CNN). By introducing noise into the latent space, the self-encoder enhances data diversity and mitigates class imbalance, thereby improving the model's generalization capabilities. The augmented dataset is subsequently used to train the CNN, enabling it to deliver precise classification of both common and rare defect patterns. Experimental results on the WM-811K dataset demonstrate that the proposed method achieves a classification accuracy of 98.56%, surpassing Random Forest, SVM, and Logistic Regression by 19%, 21%, and 27%, respectively. These findings highlight the robustness and effectiveness of the proposed approach, offering a reliable solution for wafer defect detection and classification.