To make semiconductor chips, a number of complex semiconductor manufacturing processes are required. Semiconductor chips that have undergone complex processes are subjected to EDS(Electrical Die Sorting) tests to check product quality, and a wafer bin map reflecting the information about the normal and defective chips is created. Defective chips found in the wafer bin map form various patterns, which are called defective patterns, and the defective patterns are a very important clue in determining the cause of defects in the process and design of semiconductors. Therefore, it is desired to automatically and quickly detect defective patterns in the field, and various methods have been proposed to detect defective patterns. Existing methods have considered simple, complex, and new defect patterns, but they had the disadvantage of being unable to provide field engineers the evidence of classification results through deep learning. It is necessary to supplement this and provide detailed information on the size, location, and patterns of the defects. In this paper, we propose an anomaly detection framework that can be explained through FCDD(Fully Convolutional Data Description) trained only with normal data to provide field engineers with details such as detection results of abnormal defect patterns, defect size, and location of defect patterns on wafer bin map. The results are analyzed using open dataset, providing prominent results of the proposed anomaly detection framework.

Background/Aims: The ballooning time in endoscopic papillary large balloon dilation (EPLBD) remains controversial. The aim of this study was to evaluate the significance of the ballooning time comparing an immediate balloon deflation method with a conventional ballooning time of > 45 seconds. Methods: Between January 2010 and December 2010, 126 patients with bile duct stones treated with EPLBD and endoscopic sphincterotomy were divided according to the ballooning time: the immediate deflation group (n=56) and the conventional inflation group (ballooning time 45s to < 60s) (n=70). Results: The overall success rate and the success rate of the first attempt of ERCP (endoscopic retrograde cholangio-pancreatography) were 96.4% (54/56) and 80.4% (45/56) in the immediate group and 97.1% (68/70) and 77.1% (54/70) in the conventional inflation group. There were no statistically significant differences in the overall success and the first attempt of ERCP success rate (p=0.99, p=0.66). The frequency of mechanical lithotripsy was 0% in the immediate deflation group and 7.1% in the conventional inflation group (p=0.065). Complications occurred in 3.6% (2/56) patients in the immediate deflation group and 8.6% (4/70) patients in the conventional inflation group (p=0.298). Conclusions: The ballooning time in EPLBD does not affect the outcomes of the treatment for bile duct stones. And the feasibility of the immediate deflation method in EPLBD is acceptable.