Chan-Kyoo Park

A virtual metrology system for semiconductor manufacturing

Nowadays, the semiconductor manufacturing becomes very complex, consisting of hundreds of individual processes. If a faulty wafer is produced in an early stage but detected at the last moment, unnecessary resource consumption is unavoidable. Measuring every wafers quality after each process can save resources, but it is unrealistic and impractical because additional measuring processes put in between each pair of contiguous processes significantly increase the total production time. Metrology, as is employed for product quality monitoring tool today, covers only a small fraction of sampled wafers. Virtual metrology (VM), on the other hand, enables to predict every wafers metrology measurements based on production equipment data and preceding metrology results. A well established VM system, therefore, can help improve product quality and reduce production cost and cycle time. In this paper, we develop a VM system for an etching process in semiconductor manufacturing based on various data mining techniques. The experimental results show that our VM system can not only predict the metrology measurement accurately, but also detect possible faulty wafers with a reasonable confidence.

Prediction of movement direction in crude oil prices based on semi-supervised learning

Oil price prediction has long been an important determinant in the management of most sectors of industry across the world, and has therefore consistently required detailed research. However, existing approaches to oil price prediction have sometimes made it rather difficult to implement the complex interconnected relationship between the price of oil and other global/domestic economic factors. This has been complicated by the influence of the irregular impact caused by the economic factors that affect the oil price. Recently, a machine learning algorithm, known as semi-supervised learning (SSL) has emerged, whose strength is the ease it can bring to the network representation of entities and the explicitness of inference which is expressed through relations between different entities. Since an awareness of the network representation of complicated relations between economic factors including the oil price is natural in SSL, this method allows the effects of the impact of economic factors on the oil price to be assessed with improved accuracy. SSL has so far been exploited in dealing with the non time-series types of entity, but not for the time-series types. Therefore, the proposed study is to exploit the method of representing the network between these time-series entities, and to then employ SSL to forecast the upward and downward movement of oil prices. The proposed SSL approach will be tested using one-month-ahead monthly crude oil price predictions between January 1992 and June 2008.

POSITIVE SENSITIVITY ANALYSIS IN LINEAR PROGRAMMING

Positive sensitivity analysis (PSA) is a sensitivity analysis method for linear programming that finds the range of perturbations within which positive value components of a given optimal solution remain positive. Its main advantage is that it is applicable to both an optimal basic and nonbasic optimal solution. The first purpose of this paper is to present some properties of PSA that are useful for establishing the relationship between PSA and sensitivity analysis using optimal bases, and between PSA and sensitivity analysis using the optimal partition. We examine how the range of PSA varies according to the optimal solution used for PSA, and discuss the relationship between the ranges of PSA using different optimal solutions. The second purpose is to clarify the relationship between PSA and sensitivity analysis using an optimal basis, and the relationship between PSA and sensitivity analysis using the optimal partition. We show that sensitivity analysis using the optimal partition is a special case of PSA, and its properties can be derived from the properties of PSA. The comparison among the three sensitivity analysis methods will lead to a better understanding of the difference among sensitivity analysis methods.

Finding a path in the hierarchical road networks

In a hierarchical road network, all roads can be classified into two groups according to their attributes, such as their speed limit and the number of lanes. By splitting the whole network into high-level and low-level subnetworks, the size of the road network to be searched can be reduced, and more human-oriented paths can be obtained. In this paper, we define a convenient path in the hierarchical road network and propose an algorithm for finding that path. The proposed algorithm produces a convenient and approximately shortest path with little computational effort by reducing the number of nodes to be considered. The difference between the length of the paths produced by the proposed algorithm and by existing algorithms and that of the shortest path of the whole network is discussed. Some experimental results are given to show the efficiency of the proposed algorithm.

An Optimization Approach to Resolving Circular Shareholding in Large Business Groups

Circular shareholding refers to a situation where a series of capital contributions made by companies in a family business group establish a chain of shareholdings. For example, a circular shareholding is formed when company A owns stock in company B, company B owns stock in company C, and company C owns stock in company A. In Korea, the practice of circular shareholding in large family-controlled business groups may give the principal families higher control over member firms and more opportunities to pursue their own interest at the expense of other shareholders. For this reason, the government of Korea has encouraged large conglomerates to gradually eliminate their circular shareholdings. However, there has been no research as to which shareholdings out of the complicated ownership structure should be cleared in order to resolve the issue of circular shareholding. In this paper, we propose optimization models to address the problem. Of the proposed integer programming models that can eliminate circular shareholding, one maximizes the sum of cash-flow rights while another maximizes the sum of voting rights. The proposed models have been applied to Korean family-controlled business groups, and the results are included herein. To the best knowledge of the authors, this research is the first study to apply optimization theory to the problem of resolving circular shareholding.

Improving a Risk-Averse Price-Fluctuating Inventory Model by Reallocating Initial Inventories

In traditional inventory models, purchase prices of raw materials are assumed to be fixed and have no effect on the optimal choice of inventory policies. However, when purchase prices fluctuate continuously over time, inventory costs are heavily affected by purchasing prices. Risk-averse inventory model decides order quantity and ordering time by considering not just purchase prices but also the risk from the discrepancy between estimated prices and realized prices. In this paper, we propose a myopic inventory policy which incorporates price risk into deciding ordering time and quantities. While the existing risk-averse model has no mechanism to reallocate inventories already purchased for a specific future period, the revised one reallocates initial inventories of each period to other future periods so that it can avoid purchasing raw materials at high prices. Experimental results demonstrate that the revised model outperforms the existing one in respect of total cost and variability.

A Heuristic Method for Resolving Circular Shareholdings of Korean Large Business Groups

Circular shareholding is established when at least three member firms in a business group hold stock in other member firms and form a series of ownership in a circular way. Although there have been many studies which inves-tigated a negative effect of circular shareholding on firm‘s va lue, few studies have discussed how to resolve the problem given complicated ownership structures of large business groups .This paper is based on a mixed integer programming model, which was proposed in the author’s previous research and can identify the ownership share divested in order to resol ve circular shareholding. Since the optimization model becomes too complicated for large business groups and requires a sophisticated software to solve it, we propose a simple heuristic method that can find a good approximate soluti on to the model. Its applications to twelve Korean large business groups show that the heuristic method is not jus t computationally attractive but also provides near-opti-mal solutions in most cases.

ON THE PROPERTIES OF ∊-SENSITIVITY ANALYSIS FOR LINEAR PROGRAMMING

∊-Sensitivity analysis (∊-SA) is a kind of method to perform sensitivity analysis for linear programming. Its main advantage is that it can be directly applied for interior-point methods with a little computation. In this paper, we discuss the property of ∊-SA analysis and its relationship with other sensitivity analysis methods. First, we present a new property of ∊-SA, from which we derive a simplified formula for finding the characteristic region of ∊-SA. Next, based on the simplified formula, we show that the characteristic region of ∊-SA includes the characteristic region of Yildirim and Todds method. Finally, we show that the characteristic region of ∊-SA asymptotically becomes a subset of the characteristic region of sensitivity analysis using optimal partition. Our results imply that ∊-SA can be used as a practical heuristic method for approximating the characteristic region of sensitivity analysis using optimal partition.

A Study on Applying Social Network Centrality Metrics to the Ownership Networks of Large Business Groups

Large business groups typically have central firms through which their controlling families establish (or acquire) new firms and maintain control over other member firms. Research on corporate governance has developed metrics to identify those central firms and investigated an impact of the centrality on ownership structure and firm`s financial performance. This paper introduces centrality metrics used in social network analysis (SNA) to measure how crucial a role each firm plays in the ownership structure of its business group. Then, the SNA centrality metrics are compared with the metrics developed in corporate governance field. Also, we test the relationship between the SNA centrality metrics and firm`s value. Experimental results show that the SNA centrality metrics are closely correlated with the centrality metrics used in corporate governance and are significantly correlated with firm`s value.

A Heuristic Method for Resolving Circular Shareholding with the Objective of Voting Rights Maximization

Circular shareholding refers to a situation where at least three member firms in a business group have stock in other member firms and establish a series of ownership in a circular way. Although many studies have focused on the ultimate effect of circular shareholding on firms value and profitability, there have been few studies which address how to resolve circular shareholding from the perspective of optimization theory. This paper proposes a heuristic method for identifying shareholdings which need to be cleared in order to settle the problem of circular shareholding in a business group. The proposed heuristic tries to maximize the sum of voting rights the controlling family has in its business group firms. The applications results confirm that the heuristic provides near-optimal solutions for most of 16 Korean large business groups involving circular shareholding.