• Clean Technology
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• v.29 no.4
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• pp.255-261
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• 2023

Power semiconductors are semiconductors used for power conversion, transformation, distribution, and control. Recently, the global demand for high-voltage power semiconductors is increasing across various industrial fields, and optimization research on high-voltage IGBT components is urgently needed in these industries. For high-voltage IGBT development, setting the resistance value of the wafer and optimizing key unit processes are major variables in the electrical characteristics of the finished chip. Furthermore, the securing process and optimization of the technology to support high breakdown voltage is also important. Etching is a process of transferring the pattern of the mask circuit in the photolithography process to the wafer and removing unnecessary parts at the bottom of the photoresist film. Ion implantation is a process of injecting impurities along with thermal diffusion technology into the wafer substrate during the semiconductor manufacturing process. This process helps achieve a certain conductivity. In this study, dry etching and wet etching were controlled during field ring etching, which is an important process for forming a ring structure that supports the 3.3 kV breakdown voltage of IGBT, in order to analyze four conditions and form a stable body junction depth to secure the breakdown voltage. The field ring ion implantation process was optimized based on the TEG design by dividing it into four conditions. The wet etching 1-step method was advantageous in terms of process and work efficiency, and the ring pattern ion implantation conditions showed a doping concentration of 9.0E13 and an energy of 120 keV. The p-ion implantation conditions were optimized at a doping concentration of 6.5E13 and an energy of 80 keV, and the p+ ion implantation conditions were optimized at a doping concentration of 3.0E15 and an energy of 160 keV.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.85-107
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• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.2 no.2
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• pp.147-154
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• 1969

The larvae of the ark shell Anadare broughtoni(Schrenck) were grown at room temporature (approximately $20.4^{\circ}C$), and fed laboratory-cultured Cyclotella nana. The egg of the ark shell produced in the laboratory measured about $54.9\mu$ in diameter. The embryos gradually developed into larvae up to $110.8\mu$ shell length, $83.9\mu$ shell height and with shell breadth of $58.2\mu$ even in the absence of the algal food. Beyond this sire, however, the growth of the larvae was considerably retarded. The larvae showed better growth rate when they were fed the algal food two days after spawning, i. e., early straight-hinge stage. Daily rate of food consumption varies according to the larval sizes. But the rate increases considerably when the larvae begin to form umbos. In general the rate Is indicated by the following formula: $Y=0.0025161\;X^{2.76459}$. The growth experiments of the larvae indicate that the efficiency of food conversion was higher when fed centrifuged food. Regarding to the difference in the slopes of growth curve, centrifuged food showed better growth rate as compared to those grown with the non-centrifuged food. The smaller the larval size, the greater will be the difference in growth. The larvae began settling when they reathed 261.7 to $289.6\;{\mu}$ in shell length, 199.2 to $221.7\mu$ in shell height and 147.6 to $170.8\mu$ in shell breadth. The time which elapsed from spawning to the larval settlement was about 28 days. The mean growth of the larvae is indicated with regression line and exponential curve equations as follows. Regression line shell length. 94.3 to $133.9\mu$ : Y==85.22857+3.35000X 141.6 to $269.3\mu$: Y=10.83036X-36.05357 296.8 to $373.2\mu$ : Y=19.10000X-279.30000 shell height: 72.7 to $89.7\mu$ : Y=67.11429+2.15714X 108.4 to $206.4\mu$ : Y=8.31607X-27.45357 228.6 to $282.1\mu$: Y=173.46700+13.37500X shell breadth: 45.3 to $77.8\mu$ : Y=38.08510X+2.73570X 87.4 to $157.7\mu$: Y=5.77320X-5.99640 175.4 to $214.0\mu$: Y=19.65000X-114.13300 Exponential curve shell length. 94.3 to $373.2\mu$: Y=72.45 $e^{0.04697x}$ shell height: 72.7 to $282.1\mu$: Y=54,96 $e^{0.04720x}$ shell breadth: 45.3 to $214.0\mu$ : Y=39.82 $e^{0.04927x}$ The relationships between the shell length and shell height and between the shell length and shell breadth are indicated as follows- shell height: 72.7 to $98.7\mu$ : Y=12.87780+0.63817X 108.4 to $206.4\mu$ : Y=0.90220+0.76456X 228.6 to $282.1\mu$ : Y=25.02630+0.69156X shell breadth: 45.3 to $77.8\mu$:Y=0.81373Xx-31.18914 87.4 to $157.7\mu$ : Y=13.37549+0.53230X 175.4 to $214.0\mu$: Y=30.24328+0.49545X