Effect of nitridation on the orientation of GaN layer grown on m-sapphire substrates using hydride vapor phase epitaxy

원영종,소병찬,우서휘,이동헌,김민호,남기범,임수진,심광보,남옥현 한양대학교 세라믹연구소 2014 Journal of Ceramic Processing Research Vol.15 No.2

This study reports the effect of nitridation on the orientation of GaN layers grown on m-plane sapphire (Al2O3) substrates by hydride vapor phase epitaxy (HVPE). Non-polar (10-10) GaN layers were grown on m-sapphire without nitridation. With increasing nitridation time, the crystallographic phases of the GaN layer changed from non-polar to semi-polar (11-22) through mixed phases of (10-1-3) and (11-22). The phase change with nitridation was attributed to the formation of the nanosized AlN protrusions with slanted facets. This was confirmed by X-ray photoelectron spectroscopy and atomic force microscopy.

單一施設에 의한 多品種少量生産의 生産計劃에 관한 硏究

郭秀一,李珖洙,元榮鍾 한국경영과학회 1976 韓國經營科學會誌 Vol.1 No.1

There are many cases of production processes which intermittently produce several different kinds of products for stock through one set of physical facility. In this case, an important question is what size of production run should be produced once we do set-up for a product in order to minimize the total cost, that is, the sum of the set-up, carrying, and stock-out costs. This problem is used to be called scheduling of multiple products through a single facility in the production management field. Despite the very common occurrence of this type of production process, no one has yet devised a method for determining the optimal production schedule. The purpose of this study is to develop quantitative analytical models which can be used practically and give us rational production schedules. The study is to show improved models with application to a can-manufacturing plant. In this thesis the economic production quantity (EPQ) model was used as a basic model to develop quantitative analytical models for this scheduling problem and two cases, one with stock-out cost, the other without stock-out cost, were taken into consideration. The first analytical model was developed for the scheduling of m products production through a single facility. In this model we calculate No, the optimal number of production runs per year, minimizing the total annual cost above all. Next we calculate No_i the optimal number of production runs per year, for each product as if it were an independent product without the facility-sharing constraint. Then, for products in which No_i is significantly different from, No, some manipulation of the schedule can be made by trial and error in order to try to ft the product into the basic (No schedule either more or less frequently as s dictated by) No_i. But this trial and error schedule is thought of inefficient. The second analytical model was developed by reinterpretation of the calculating process of the economic production quantity model. In this model we obtained two relationships, one of which is the relationship between optimal number of set-ups for the ith item and optimal total number of set-ups, the other is the relationship between optimal average inventory investment for the ith item and optimal total average inventory investment. Form these relationships we can determine how much average inventory investment per year would be required if a rational policy based on m No set-ups per year for m products were followed and, alternatively, how many set-ups per year would be required if a rational policy were followed which required an established total average inventory investment. We also learned the relationship between the number of set-ups and the average inventory investment takes the form of a hyperbola. But, there is no reason to way that the first analytical model is superior to the second analytical mode. It can be said that the first model is useful for a basic production schedule. On the other hand, the second model is efficient to get an improved production schedule, in a sense of reducing the total cost. Another merit of the second model is that, unlike the first model where we have to know all the inventory costs for each product, we can obtain an improved production schedule with unknown inventory costs. The application of these quantitative analytical models to PoHang can-manufacturing plant shows this point.

단일시설에 의한 다품종소량생산의 생산계획에 관한 연구

곽수일,이광수,원영종,Kwak, Soo-Il,Lee, Kwang-Soo,Won, Young-Jong 한국국방경영분석학회 1976 한국국방경영분석학회지 Vol.2 No.2

There are many cases of production processes which intermittently produce several different kinds of products for stock through one set of physical facility. In this case, an important question is what size of production run should be produced once we do set-up for a product in order to minimize the total cost, that is, the sum of the set-up, carrying, and stock-out costs. This problem is used to be called scheduling of multiple products through a single facility in the production management field. Despite the very common occurrence of this type of production process, no one has yet devised a method for determining the optimal production schedule. The purpose of this study is to develop quantitative analytical models which can be used practically and give us rational production schedules. The study is to show improved models with application to a can-manufacturing plant. In this thesis the economic production quantity (EPQ) model was used as a basic model to develop quantitative analytical models for this scheduling problem and two cases, one with stock-out cost, the other without stock-out cost, were taken into consideration. The first analytical model was developed for the scheduling of m products production through a single facility. In this model we calculate No, the optimal number of production runs per year, minimizing the total annual cost above all. Next we calculate $No_i$, the optimal number of production runs per year, for each product as if it were an independent product without the facility-sharing constraint. Then, for products in which $No_i$ is significantly different from No, some manipulation of the schedule can be made by trial and error in order to try to fit the product into the basic (No schedule either more or less frequently as dictated by) $No_i$. But this trial and error schedule is thought of inefficient. The second analytical model was developed by reinterpretation of the calculating process of the economic production quantity model. In this model we obtained two relationships, one of which is the relationship between optimal number of set-ups for the ith item and optimal total number of set-ups, the other is the relationship between optimal average inventory investment for the ith item and optimal total average inventory investment. From these relationships we can determine how much average inventory investment per year would be required if a rational policy based on m No set-ups per year for m products were followed and, alternatively, how many set-ups per year would be required if a rational policy were followed which required an established total average inventory investment. We also learned the relationship between the number of set-ups and the average inventory investment takes the form of a hyperbola. But, there is no reason to say that the first analytical model is superior to the second analytical model. It can be said that the first model is useful for a basic production schedule. On the other hand, the second model is efficient to get an improved production schedule, in a sense of reducing the total cost. Another merit of the second model is that, unlike the first model where we have to know all the inventory costs for each product, we can obtain an improved production schedule with unknown inventory costs. The application of these quantitative analytical models to PoHang can-manufacturing plant shows this point.