• IE interfaces
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• v.20 no.3
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• pp.277-287
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• 2007

Semiconductor manufacturing is one of the most complex and capital-intensive processes composed of several hundreds of operations. In today’s competitive business environments, it is more important than ever before to manage manufacturing process effectively to achieve better performances in terms of customer satisfaction and productivity than those of competitors. So, many semiconductor manufacturing companies implement advanced planning and scheduling (APS) system as a management tool for the complex semiconductor manufacturing process. In this study, we explain roles of production planning and scheduling in semiconductor manufacturing and principal factors that make the production planning and scheduling more difficult. We describe the APS system implementation project at Korean semiconductor manufacturing company in terms of key issues with realistic samples.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.26 no.3
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• pp.67-73
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• 2003

Manufacturing technologies of compound semiconductor are similar to the process of memory device, but management technology of manufacturing process for compound semiconductor is not enough developed. Semiconductor manufacturing environment also has been emerged as mass customization and open foundry service so integrated manufacturing system is needed. In this study we design the integrated manufacturing system for compound semiconductor fabrication t hat has monitoring of process, reduction of lead-time, obedience of due-dates and so on. This study presents integrated manufacturing system having database system that based on web and data acquisition system. And we will implement them in the actual compound semiconductor fabrication.

• IE interfaces
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• v.23 no.3
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• pp.205-212
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• 2010

The learning curve model is a mathematical form which represents the relationship between the manufacturing experience and its effectiveness. The semiconductor manufacturing is widely known as an appropriate example for the learning effect due to its complicated manufacturing processes. In this paper, I propose a new compound learning curve model for semiconductor products in which the general learning curve model and the growth curve are composed. The dependent variable and the effective independent variables of the model were abstracted from the existing learning curve models and selected according to multiple regression processes. The simulation results using the historical DRAM data show that the proposed compound learning curve model is one of adequate models for describing learning effect of semiconductor products.

• Proceedings of the IEEK Conference
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• 2002.06b
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• pp.41-44
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• 2002

The Semiconductor Industrial are developed after 1940. It was called “Rice of Industrial”. It needs great effect in Electronics. It was developed highly in recent several years with semiconductor manufacturing equipments. Semiconductor manufacturing devices are developed “In-line” type in the first stage. But It was non-effective in modem many type process. Because this reason, Cluster type manufacturing equipments are proposed. Cluster have ability of many-type-process and effective-scheduling by circular type process chamber In this paper. we propose a real-time 3D monitoring and simulation of this semiconductor manufacturing equipments. By proposed monitoring method, we have capability real visual maintanance & virtual simulation. This effective visual 3D monitoring could apply another dangerous environment in entire industrial.

• International Journal of Internet, Broadcasting and Communication
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• v.9 no.2
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• pp.20-26
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• 2017

Process control is crucial in many industries, especially in semiconductor manufacturing. In such large-volume multistage manufacturing systems, a product has to go through a very large number of processing steps with reentrant) before being completed. This manufacturing system has many machines of different types for processing a high mix of products. Each process step has specific quality standards and most of them have nonlinear dynamics due to physical and/or chemical reactions. Moreover, many of the processing steps suffer from drift or disturbance. To assure high stability and yield, on-line quality monitoring of the wafers is required. In this paper we develop a real-time fault detection system on semiconductor manufacturing process. Proposed system is superior to other incremental fault detection system and shows similar performance compared to batch way.

• Journal of Information Technology Applications and Management
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• v.15 no.1
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• pp.243-260
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• 2008

To prevent low yields in the semiconductor industry is crucial to the success of that industry. However, to prevent low yields is difficult because of too many factors to affect yield variation and their complex relation in the semiconductor manufacturing process. This study presents a new efficient detection methodology for detecting abnormal yields including high and low yields, which can forecast the yield level of a production unit (namely a lot) based on yield-related feature variables' behaviors. In the methodology, we use C5.0 to identify the yield-related feature variables that are the combination of correlated process variables associated with yield, use SOM (Self-Organizing Map) neural networks to extract and classify significant patterns of past abnormal yield lots and finally use C5.0 to generate classification rules for detecting abnormal yield lot. We illustrate the effectiveness of our methodology using a semiconductor manufacturing company's field data.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.4
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• pp.415-424
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• 2002

Semiconductor manufacturing has been emerged as a highly competitive but profitable business. Accordingly it becomes very important for semiconductor manufacturing companies to meet customer demands at the right time, in order to keep the leading edge in the world market. However, due-date oriented production is very difficult task because of the complex job flows with highly resource conflicts in fabrication shop called FAB. Due to its cyclic manufacturing feature of products, to be completed, a semiconductor product is processed repeatedly as many times as the number of the product manufacturing cycles in FAB, and FAB processes of individual manufacturing cycles are composed with similar but not identical unit processes. In this paper, we propose a production scheduling and control scheme that is designed specifically for semiconductor scheduling environment (FAB). The proposed scheme consists of three modules: simulation module, cycle due-date estimation module, and dispatching module. The fundamental idea of the scheduler is to introduce the due-date for each cycle of job, with which the complex job flows in FAB can be controlled through a simple scheduling rule such as the minimum slack rule, such that the customer due-dates are maximally satisfied. Through detailed simulation, the performance of a cycle due-date based scheduler has been verified.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.79-82
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• 1995

The problem of temperature control for rapid thermal processing (RTP) in semiconductor manufacturing is discussed in this paper. Among sub=micron technologies for VLSI devices, reducing the junction depth of doped region is of great importance. This paper investigates existing methods for manufacturing wafers, focusing on the RPT which is considered to be good for formation of shallow junctions and performs the wafer fabrication operation in a single chamber of annealing, oxidation, chemical vapor deposition, etc., within a few minutes. In RTP for semiconductor manufacturing, accurate and uniform control of the wafer temperature is essential. In this paper, a robustr controller is designed using a recently developed optimization technique. The controller designed is then tested via computer simulation and compared with the other results.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.22 no.1
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• pp.52-59
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• 2012

Objectives: The purpose of this study was to evaluate the exposure possibility of by-products during the semiconductor manufacturing processes. Methods: The authors investigated types of chemicals generated during semiconductor manufacturing processes by the qualitative experiment on generation of by-products at the laboratory and a literature survey. Results: By-products due to decomposition of photoresist by UV-light during the photo-lithography process, ionization of arsine during the ion implant process, and inter-reactions of chemicals used at diffusion and deposition processes can be generated in wafer fabrication line. Volatile organic compounds (VOCs) such as benzene and formaldehyde can be generated during the mold process due to decomposition of epoxy molding compound and mold cleaner in semiconductor chip assembly line. Conclusions: Various types of by-products can be generated during the semiconductor manufacturing processes. Therefore, by-products carcinogen such as benzene, formaldehyde, and arsenic as well as chemical substances used during the semiconductor manufacturing processes should be controlled carefully.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.6
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• pp.572-578
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• 2015

Yield prediction is one of the most important issues in semiconductor manufacturing. Especially, for a fast-changing environment of the semiconductor industry, accurate and reliable prediction techniques are required. In this study, we propose a prediction model to predict wafer yield based on virtual metrology process parameters in semiconductor manufacturing. The proposed prediction model addresses imbalance problems frequently encountered in semiconductor processes so as to construct reliable prediction model. The effectiveness and applicability of the proposed procedure was demonstrated through a real data from a leading semiconductor industry in South Korea.