Query: defect type classification

Candidate papers:

abstract: Semiconductor wafer map data provides valuable information for semiconductor engineers. Correctly classified defect patterns in wafer maps can increase semiconductor productivity. Convolutional Neural Networks (CNN) achieved excellent performance on computer vision and were frequently used method in wafer map classification. The CNN based classifier of the wafer map defect pattern requires a sufficiently large training set to ensure high performance. However, for the real semiconductor production environment, it is challenging to collect various defect patterns enough. In this paper, we propose a method to supplement the lack of training set using Generative Adversarial Networks (GAN) to improve the performance of the classifier. We measure our performance on the 'WM 811k' dataset, which consists of 811K real world wafer maps. We compare the performance of our classifiers with commonly used augmentation techniques. As a result, we achieved remarkable performance enhancement from 97.0% to 98.3%
author_names: YongSung Ji

Jee-Hyong Lee
title: Using GAN to Improve CNN Performance of Wafer Map Defect Type Classification Yield Enhancement
venue: 2020 31st Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)
year: 2020

abstract: In this paper, we study a new estimation system for the prediction of steam generator tube defects. In the previous research works, defect size estimators were independently designed for each defect types in order to estimate the defect size. As a result, the structure of estimation system is rather complex and the estimation performance gets worse if the classification performance is degraded for some reason. This paper studies a new estimation system that does not require the classification of defect types. Although the previous works are expected to achieve much better estimation performance than the proposed system since it uses the estimator specialized in each defect, the performance difference is not so large. Therefore, it is expected that the proposed estimator can be effectively used for the case where the defect type classification is imperfect.
author_names: Nam Hoon Jo
title: Performance Evaluation of SG Tube Defect Size Estimation System in the Absence of Defect Type Classification
year: 2010

abstract: In semiconductor manufacturing, a wafer bin map (WBM) represents the results of wafer testing for dies using a binary pass or fail value. For WBMs, defective dies are often clustered into groups of local systematic defects. Determining their specific patterns is important, because different patterns are related to different root causes of failure. Recently, because wafer sizes have increased and the process technology has become more complicated, the probability of observing mixed type defect patterns, i.e. two or more defect patterns in a single wafer, has increased. In this paper, we propose the use of convolutional neural networks (CNNs) to classify mixed type defect patterns in WBMs in the framework of an individual classification model for each defect pattern. Through simulated and real data examples, we show that the CNN is robust to random noise and performs effectively, even if there are many random defects in WBMs.
author_names: Kiryong Kyeong

Heeyoung Kim
title: Classification of Mixed Type Defect Patterns in Wafer Bin Maps Using Convolutional Neural Networks
venue: IEEE Transactions on Semiconductor Manufacturing
year: 2018

abstract: In this paper, we propose a solder joint defect type classification method for automatic optical inspection machines in the manufacturing system of printed circuit boards. The inspection procedure for the solder joint defect type classification consists of an offline stage, which sets the optimal feature extraction region, and an online stage which classifies a defect type. In the offline stage, we use an optimization technique, namely, the genetic algorithm, to optimize the feature extraction region. In this stage, the optimal feature extraction region for defect type classification is constructed automatically. In the online stage, feature extraction regions are used to segment the solder joint image after component image acquirement. We then extract various color features from the segmented feature extraction regions. Next, we use support vector machine, which is one of the machine learning model's method to classify the solder joint defect type. To evaluate the performance of the proposed method, ten types of solder joint defects were used in an experiment. The experimental results verified the effectiveness of the method in terms of the recognition rate, and its convenience.
author_names: Ji-Deok Song

Young-Gyu Kim

Tae-Hyoung Park
title: SMT defect classification by feature extraction region optimization and machine learning
year: 2019

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

abstract: INTRODUCTION Since the first description of autologous chondrocyte implantation (ACI) in 1994 different methods and improvements were established for this regenerative treatment option of large chondral defects. This study analyzes safety and short term clinical results from characterized ACI using a collagen based biphasic scaffold and evaluates prognostic factors. METHODS 433 patients with a mean age of 33.4 years and localized grade III to IV cartilage defects (ICRS classification) in the knee or ankle were included. Mean defect size was 5.9 cm(2) Prior seeding of the scaffold, expanded chondrocytes were characterized by RT PCR on 6 different marker genes (type I and II collagen, aggrecan, interleukin 1 b (IL 1b) vascular endothelial growth factor receptor 1 (FLT 1) and bone sialoprotein 2 (BSP 2) Clinical outcome was evaluated using a questionnaire for defect history, basic demographics, time elapsed from surgery, 10 point outcome assessments of pain, function and swelling. Moreover, adverse events (AEs) or subsequent treatments were recorded and analysed. RESULTS Patients improved significantly over baseline (p 0.0001) in pain, function and swelling. Subjects with later than 12 months follow up reported nominally greater mean changes. Graft failure incidence was 6% for patients with greater than one year follow up. Graft related complications were significantly higher for patellar (p 0.0001) and degenerative defects (p 0.005) Elevated expression of FLT 1 (p 0.02) or IL 1 b mRNA (p 0.03) was associated with graft related AEs. A borderline association was found for low collagen type II expression (p 0.08) CONCLUSION Early graft related AEs after ACI with a biphasic collagen scaffold are related to defect type, location and marker gene expression. The levels of significance observed for gene expression with respect to graft related AEs were subordinate to those identified in the analysis of lesion history and location.
author_names: Peter Angele

Juergen Fritz

Dirk Albrecht

Jason L Koh

Johannes Zellner
title: Defect type, localization and marker gene expression determines early adverse events of matrix associated autologous chondrocyte implantation.
venue: Injury
year: 2015

abstract: Inspired by famous results of Drozd (1974) [13] Nazarova Roiter (1975) [42] and Bondarenko Stepochkina (2005) [8] on the importance of finite posets I with the non negative Tits quadratic form q^I:ZI Z in the representation theory, we study the finite non negative posets I of corank two, that is, such posets I that the symmetric Gram matrix GI:=12[CI+CItr]MI(Q) of I is positive semi definite of corank two, where CIMI(Z) is the incidence matrix of I. A structure of such posets is described in the paper. It is shown that every such a poset I can be obtained in a natural way from a corank zero poset J and a pair of roots of J. Following main ideas of the Coxeter spectral analysis of posets, we associate with any connected corank two poset I the incidence quadratic form qI:ZI Z, the Coxeter matrix CoxI: CICI tr, the Coxeter polynomial coxI(t)=det(tE CoxI)Z[t] its complex Coxeter spectrum speccI, the Coxeter number cIN{ the reduced Coxeter number cVIN, the incidence defect homomorphism ~I:ZI KerqIZI, and a simply laced Dynkin diagram DynI. In particular, we study the question when the Coxeter spectrum speccI of I, together with DynI, determines the incidence matrix CI of I (hence the poset I) uniquely, up to a Z congruence. By applying linear algebra techniques and a combinatorial computer aided analysis of posets, we prove that the question has an affirmative answer for connected corank two posets I, with at most 15 elements. A complete list of such posets is presented and their Coxeter type invariants are computed. It follows that |I|=6, there is only one such a poset I of cardinality 6, and 8 such posets of cardinality 7, up to the poset duality I Iop. The existence of a PhI mesh translation quiver G(RIKerqI,PhI) on the set RIKerqI is discussed, where RI={vZI;qI(v)=1} is the set of roots of qI.
author_names: Marcin Gasiorek

Daniel Simson

Katarzyna Zajac
title: Structure and a Coxeter Dynkin type classification of corank two non negative posets
year: 2015

abstract: Abstract A novel multiple hyper spheres support vector machine with additional information (MHSVM+ is proposed for multi class steel surface defects classification. Originated from binary twin hyper spheres support vector machine, MHSVM+ uses hyper sphere to solve classification decision problem. Differently, MHSVM+ is a multi class classifier, where it builds a corresponding hyper sphere for each type of defect dataset. Moreover, MHSVM+ introduces learning paradigm using additional information, which means it can learn additional information hidden in defect dataset. Two types of additional information are provided: local neighbor information and local density information. Local neighbor information contains local classification results for defect samples. And local density information is used to capture label noise, isolated samples and important samples in defect dataset. The above two types of additional information are introduced into MHSVM+ model. Finally, MHSVM+ classifier is used to classify six types of steel surface defects. Experimental results show that the novel multi class classifier has perfect classification accuracy for defect dataset, especially corrupted defect dataset.
author_names: Rong-fen Gong

Chengdong Wu

Maoxiang Chu
title: Steel surface defect classification using multiple hyper spheres support vector machine with additional information
year: 2018

abstract: In Timor Leste, the coffee is one of the most important product for the acquisition of foreign currency. However, there are almost no rationalizations, therefore, enhancing the value of the coffee efficiently at the local of production is desired. The final objective of our study is to develop the automatic coffee beans sorting system for the producers of coffee beans in Timor Leste. As the first step, we developed an image processing system which classifies the images of green coffee beans into each type of defect. We employed deep convolutional neural networks, the state of the art machine learning technique, for the image processing. As the results, we succeeded to sort defect beans from 72.4% to 98.7% of accuracies based on the types of defects.
author_names: Carlito Pinto

Junya Furukawa

Hidekazu Fukai

Satoshi Tamura
title: Classification of Green coffee bean images basec on defect types using convolutional neural network (CNN)
venue: 2017 International Conference on Advanced Informatics, Concepts, Theory, and Applications (ICAICTA)
year: 2017

abstract: ABSTRACT In semiconductor manufacturing, a wafer bin map (WBM) is a map that consists of assigned bin values for dies based on wafer test results (e.g. value 1 for good dies and value 0 for defective dies) The bin values of adjacent dies are often spatially correlated, forming some systematic defect patterns. These non random defect patterns occur due to assignable causes; therefore, it is important to identify these systematic defect patterns in order to know the root causes of failure and to take actions for quality management and yield enhancement. In particular, as wafer fabrication processes have become more complicated, mixed type defect patterns (two or more different types of defect patterns occur simultaneously in a single wafer) occur more frequently than in the past. For more effective classification of wafers based on their defect patterns, mixed type defect patterns need to be detected and separated into several clusters of different patterns; subsequently, each cluster of a single pattern can be matched to a well known defect type (e.g. scratch, ring) or it may indicate the emergence of a new defect pattern. There are several challenges to be overcome in the detection and clustering of mixed type defect patterns. These include (i) the separation of random defects from systematic defect patterns; (ii) determining the number of clusters; and (iii) the clustering of defect patterns of complex shapes. To address these challenges, in this article, we propose a new framework for detecting and clustering mixed type defect patterns. First, we propose a new filtering method, called the connected path filtering method, to denoise WBMs. Subsequently, we adopt the infinite warped mixture model for the clustering of mixed type defect patterns; this model is flexible in its ability to deal with complex shapes of defect patterns; furthermore, the number of clusters does not need to be specified in advance but is automatically determined simultaneously during the clustering procedure. We validate the proposed method using real data from a semiconductor company. The experimental results demonstrate the effectiveness of the proposed method in estimating the number of underlying clusters as well as in the clustering of mixed type defect patterns.
author_names: Jinho Kim

Youngmin Lee

Heeyoung Kim
title: Detection and clustering of mixed type defect patterns in wafer bin maps
year: 2016