Evaluating the Performance of Different Classification Algorithms for Fabricated Semiconductor Wafers

Jian, Cheng; Ooi, Melanie Po-Leen; Chan, Chris; Kuang, Ye Chow; Demidenko, Serge

doi:10.1109/delta.2010.69

Cited by 14 publications

(7 citation statements)

References 12 publications

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“…The recent development of the machine learning and GIS has introduced many new machine learning techniques that have been recognized as having better overall performance (Witten et al, 2011). Some state-of-the art advanced machine learning methods such as the KLR and ADT have been used in other fields with high accuracy (Cheng et al, 2010;Guy et al, 2012;Liu et al, 2005;, however, the exploration of these methods for landslide susceptibility mapping has seldom been carried out. We addressed this issue in this paper with the investigation and comparison of the KLR and ADT methods for landslide susceptibility modeling.…”

Section: Discussionmentioning

confidence: 99%

Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines

Hong

Pradhan

et al. 2015

CATENA

386

147

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Preparation of landslide susceptibility map is the first step for landslide hazard mitigation and risk assessment. The main aim of this study is to explore potential applications of two new models such as two-class Kernel Logistic Regression (KLR) and Alternating Decision Tree (ADT) for landslide susceptibility mapping at the Yihuang area (China). The ADT has not been used in landslide susceptibility modeling and this paper attempts a novel application of this technique. For the purpose of comparison, a conventional method of Support Vector Machines (SVM) which has been widely used in the literature was included and their results were assessed. At first, a landslide inventory map with 187 landslide locations for the study area was constructed from various sources. Landslide locations were then spatially randomly split in a ratio of 70/30 for building landslide models and for the model validation. Then a spatial database with a total of fourteen landslide conditioning factors was prepared, including slope, aspect, altitude, topographic wetness index (TWI), stream power index (SPI), sediment transport index (STI), plan curvature, landuse, normalized difference vegetation index (NDVI), lithology, distance to faults, distance to rivers, distance to roads, and rainfall. Using the KLR, the SVM, and the ADT, three landslide susceptibility models were constructed using the training dataset. The three resulting models were validated and compared using the receive operating characteristic (ROC), Kappa index, and five statistical evaluation measures. In addition, pairwise comparisons of the area under the ROC curve were carried out to assess if there are significant differences on the overall performance of the three models. The goodness-of-fits are 92.5% (the KLR model), 88.8% (the SVM model), and 95.7% (the ADT model). The prediction capabilities are 81.1%, 84.2%, and 93.3% for the KLR, the SVM, and the ADT models, respectively. The result shows that the ADT model yielded better overall performance and accurate results than the KLR and SVM models. The KLR model considered slightly better than SVM model in terms of the positive prediction values. The ADT and KLR are the two promising data mining techniques which might be considered to use in landslide susceptibility mapping. The results from this study may be useful for landuse planning and decision making in landslide prone areas.

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Section: Discussionmentioning

confidence: 99%

Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines

Hong

Pradhan

et al. 2015

CATENA

386

147

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“…The simulated wafer defect maps also serve as a training set for the defect-cluster classifier [4]. Apart from the problems just mentioned, it is also generally inadvisable to train the classifier using raw production test data, because such data normally contains random failures, which appear as a noise to the classifier.…”

Section: Device Type Customization: Configuration Stagementioning

confidence: 99%

“…When defect clusters are found, the SDC algorithm produces a wafer map for each defect cluster on each wafer in a given wafer lot. This is shown in Figure 4 as internal file 1, which is then sent to the defect-cluster classifier [4].…”

Section: Production Test Data Monitoring: Cluster Identification Stagementioning

confidence: 99%

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Identifying Systematic Failures on Semiconductor Wafers Using ADCAS

et al. 2013

Self Cite

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“…Since hundreds of individual equipment are involved in the semiconductor manufacturing process, more than three months of production time is required. The overall yield of th semiconductor manufacturing industry is relatively lower than that of other manufacturing industries [4] because of the highly complex manufacturing processes involved; hence, yield management is one of the key factors to gain a competitive advantage in the market. It is obvious that developing a new product faster than competitors is necessary for market leadership, but a company can also dominate the market when their mass production system is running with a high and stable yield.…”

Section: Introductionmentioning

confidence: 99%

Bin2Vec: A Better Wafer Bin Map Coloring Scheme for Comprehensible Visualization and Effective Bad Wafer Classification

Kim

Park

et al. 2019

Applied Sciences

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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.

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Evaluating the Performance of Different Classification Algorithms for Fabricated Semiconductor Wafers

Cited by 14 publications

References 12 publications

Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines

Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines

Identifying Systematic Failures on Semiconductor Wafers Using ADCAS

Bin2Vec: A Better Wafer Bin Map Coloring Scheme for Comprehensible Visualization and Effective Bad Wafer Classification

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