Query: wafer bin map

Candidate papers:

abstract: Wafer bin maps (WBM) provides crucial information regarding process abnormalities and facilitate the diagnosis of low yield problems in semiconductor manufacturing. Most studies of WBM classification and analysis apply a statistical based method or machine learning method operating on raw wafer data and extracted features. With increasing WBM pattern diversity and complexity, the useful features for effective WBM recognition are highly dependent on domain knowledge. This study proposes an ensemble convolutional neural network (ECNN) framework for WBM pattern classification, in which a weighted majority function is adopted to select higher weights for the base classifiers that have higher predictive performance. An industrial WBM dataset (namely, WM 811K) from a wafer fabrication process was used to demonstrate the effectiveness of the proposed ECNN framework. The proposed ECNN has superior performance in terms of precision, recall, F1 and other conventional machine learning classifiers such as linear regression, random forest, gradient boosting machine, and artificial neural network. The experimental results show that the proposed ECNN framework is able to identify common WBM defect patterns effectively.
author_names: Chia-Yu Hsu

Ju-Chien Chien
title: Ensemble convolutional neural networks with weighted majority for wafer bin map pattern classification
year: 2020

abstract: Abstract The semiconductor manufacturing process involves long and complex activities, with intensive use of resources. Producers compete through the introduction of new technologies for increasing yield and reducing costs. So, yield improvement is becoming increasingly important since advanced production technologies are complex and interrelated. In particular, Wafer Bin Maps (WBMs) presenting specific fault models provide crucial information to keep track of process problems in semiconductor manufacturing. Production control is often based on the "judgement" of expert engineers who, however, carry out the analysis of map templates through simple visual exploration. In this way, existing studies are subjective, time consuming, and are also limited by the capacity of human recognition. This study proposes a network based data mining approach, which integrates correlation graphs with clustering analysis to quickly extract patterns from WBMs and then bind them to manufacturing defects. An empirical study has been conducted on real production data for validating the proposed clustering algorithm, which showed a perfect correspondence between the malfunction patterns found by the algorithm and those discovered by human experts, so confirming the validity of our approach in its ability of correctly identifying actual defective patterns to help improving production yield.
author_names: Crescenzio Gallo

Vito Capozzi
title: A Wafer Bin Map "Relaxed" Clustering Algorithm for Improving Semiconductor Production Yield
venue: Open Comput. Sci.
year: 2020

abstract: Wafer bin map (WBM) represents specific defect patterns that provide information for diagnosing root causes of low yield in semiconductor manufacturing. In practice, most semiconductor engineers use subjective and time consuming eyeball analysis to assess defect patterns. Given shrinking feature sizes, various types of WBMs with different defect patterns occur; therefore, relying on human vision to judge defect patterns become more complicated, inconsistent, and unreliable. To bridge the gap, a system is proposed to facilitating WBM patterns extraction and assisting engineer to recognize defect patterns efficiently. We propose an individual classification model which is trained by Deep Belief Network (DBN) to diagnose the patterns on wafer bin maps. By setting up six single classifiers with different thresholds, the individual classifiers were combined for both single and mixed type patterns recognition. The numerical results showed that the single classifiers outperform MLP method both on single and mix type patterns. Simultaneously, the results have shown the validity and practical viability of the proposed combined classifiers.
author_names: Junjun Zhuang

Guiyun Mao

Xu Chen

Zhengying Wei
title: A Neural Network Approach to Better Diagnosis of Defect Pattern in Wafer Bin Map
venue: 2020 China Semiconductor Technology International Conference (CSTIC)
year: 2020

abstract: Defective die on a wafer map tend to cluster in distinguishable patterns, and such defect patterns can provide crucial information to identify equipment problems or process failures in the semiconductor manufacturing. Therefore, it is important to accurately and efficiently classify the defect patterns. In this paper, we propose a novel clustering based defect pattern detection and classification framework for wafer bin map (WBM) The proposed framework has many advantages. Outlier detection and defect cluster pattern extraction can be done at the same time; arbitrarily shaped cluster patterns can be detected; and there is no need to specify the number of clusters in advance. Based on WBM property, the parameters used in the clustering algorithm are fixed so that parameter sensitivity can be avoided. Since the defect patterns are classified based on extracted features, no labeled data and no supervised classification training are needed and single type patterns as well as mixed type patterns can be found. Extensive experiments conducted on a real world WM 811K dataset has shown the superiority of proposed framework. The proposed framework can also be used in a big data environment to accelerate performance.
author_names: Cheng Hao Jin

Hyuk Jun Na

Minghao Piao

Gouchol Pok

Keun Ho Ryu
title: A Novel DBSCAN Based Defect Pattern Detection and Classification Framework for Wafer Bin Map
venue: IEEE Transactions on Semiconductor Manufacturing
year: 2019

author_names: Seungjae Baek

Chang Hyun Lee

Seoung Bum Kim
title: New Pattern Detection of Wafer Bin Map Using Deep Learning
year: 2020

abstract: A wafer bin map (WBM) which is the result of an electrical die sorting test, provides information on which bins failed what tests, and plays an important role in finding defective wafer patterns in semiconductor manufacturing. Current wafer inspection based on WBM has two problems: good/bad WBM classification is performed by engineers and the bin code coloring scheme does not reflect the relationship between bin codes. To solve these problems, we propose a neural network based bin coloring method called Bin2Vec to make similar bin codes are represented by similar colors. We also build a convolutional neural network based WBM classification model to reduce the variations in the decisions made by engineers with different expertise by learning the company wide historical WBM classification results. Based on a real dataset with a total of 27,701 WBMs, our WBM classification model significantly outperformed benchmarked machine learning models. In addition, the visualization results of the proposed Bin2Vec method makes it easier to discover meaningful WBM patterns compared with the random RGB coloring scheme. We expect the proposed framework to improve both efficiencies by automating the bad wafer classification process and effectiveness by assigning similar bin codes and their corresponding colors on the WBM.
author_names: Junhong Kim

Hyungseok Kim

Jaesun Park

Kyounghyun Mo

Pilsung Kang
title: Bin2Vec: A Better Wafer Bin Map Coloring Scheme for Comprehensible Visualization and Effective Bad Wafer Classification
venue: Applied Sciences
year: 2019

abstract: Many studies have been conducted to improve wafer bin map (WBM) defect classification performance because accurate WBM classification can provide information about abnormal processes causing a decrease in yield. However, in the actual manufacturing field, the manual labeling performed by engineers leads to a high level of uncertainty. Label uncertainty has been a major cause of the reduction in WBM classification system performance. In this paper, we propose a class label reconstruction method for subdividing a defect class with various patterns into several groups, creating a new class for defect samples that cannot be categorized into known classes and detecting unknown defects. The proposed method performs discriminative feature learning of the Siamese network and repeated cross learning of the class label reconstruction based on Gaussian means clustering in a learned feature space. We verified the proposed method using a real world WBM dataset. In a situation where there the class labels of the training dataset were corrupted, the proposed method could increase the classification accuracy of the test dataset by enabling the corrupted sample to find its original class label. As a result, the accuracy of the proposed method was up to 7.8% higher than that of the convolutional neural network (CNN) Furthermore, through the proposed class label reconstruction, we found a new mixed type defect class that had not been found until now, and we detected new types of unknown defects that were not used for learning with an average accuracy of over 73%
author_names: Seyoung Park

Jaeyeon Jang

Chang Ouk Kim
title: Discriminative feature learning and cluster based defect label reconstruction for reducing uncertainty in wafer bin map labels
venue: J. Intell. Manuf.
year: 2021

abstract: Abstract The wafer map is obtained by testing each die in the wafer during semiconductor production for defects and marking the defective die. The classification of wafer maps can provide evidence for problems occurring in the production process, so as to solve the problems and reduce the costs. Before classifying the wafer map, the most important thing is feature extraction. In addition to a certain spatial pattern, the wafer map also has a lot of noise, which affects the process of feature extraction. When the traditional DBSCAN algorithm is used for filtering, it is necessary to manually determine the value of Eps and MinPts parameters, and the selection of the parameters directly affects the accuracy of the clustering. Therefore, this paper proposes an automatic parameter filtering method based on DBSCAN, which can solve the traditional drawbacks of manually parameters setting, the algorithm is a Self Adaptive DBSCAN based method for wafer bin map, we call it SA DBSCANWBM. This method selects a comprehensive index of cluster intra cluster density and inter cluster density to evaluate the optimal parameters. The experimental results show that the algorithm proposed in this paper can automatically and reasonably select better parameters and has a good clustering effect, which helpful for subsequent feature extraction and classification.
author_names: Shouhong Chen

Mulan Yi

Yuling Shang
title: A self adaptive DBSCAN based method for wafer bin map defect pattern classification
year: 2021

abstract: Automatic identification of defect patterns in wafer bin maps (WBMs) stands as a challenging problem for the semiconductor manufacturing industry. Deep convolutional neural networks have recently shown decent progress in learning spatial patterns in WBMs, but only at the expense of explicit manual supervision. Unfortunately, a clean set of labeled WBM samples is often limited in both size and quality, especially during rapid process development or early production stages. In this study, we propose a self supervised learning framework that makes the most out of unlabeled data to learn beforehand rich visual representations for data efficient WBM defect pattern classification. After self supervised pre training based on noise contrastive estimation, the network is fine tuned on the available labeled data to classify WBM defect patterns. We argue that self supervised pre training with a vast amount of unlabeled data substantially improves classification performance when labels are scarce. We demonstrate the effectiveness of our work on a real world public WBM dataset, WM 811K. The code is available at https:/github.com/hgkahng/WaPIRL.
author_names: Hyungu Kahng

Seoung Bum Kim
title: Self Supervised Representation Learning for Wafer Bin Map Defect Pattern Classification
venue: IEEE Transactions on Semiconductor Manufacturing
year: 2021

author_names: Kwang-Jae Kim

Chang Ho Lee

Seunghwan Choi

Dong-Hee Lee
title: Discovering Sequences of Machines causing Wafer Bin Map Failure Patterns in Semiconductor Manufacturing Process
year: 2019