• Korean Journal of Hazardous Materials
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• v.3 no.1
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• pp.59-64
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• 2015

Semiconductor industry has large number of chemical inventory and is easily exposed to chemical release incidents. Toxic release is one of the most interested area in evaluating consequence to the vicinity of industry facilities handling hazardous materials. Hydrofluoric acid is one of the typical chemical used in semiconductor facility and is selected and toxic release is evaluated to assess the risk impacted to its off-site. Accident scenarios were listed using process safety information. The scenarios having effect to the off-site were selected and assessed further according to guideline provided by Korea government. Worst case and alternative scenarios including other interested scenarios were evaluated using ALOHA. Each evaluated scenario was assessed further considering countermeasures. The results showed that the facility handling hydroflooric acid is safe enough and needed no further protections at the moment.

• Journal of Information Technology Applications and Management
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• v.31 no.1
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• pp.139-161
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• 2024

The Fourth Industrial Revolution has spurred widespread adoption of AI-based services, driving global interest in AI semiconductors for efficient large-scale computation. Text mining research, historically using LDA, has evolved with machine learning integration, exemplified by the 2021 BERTopic technology. This study employs BERTopic to analyze AI semiconductor-related patents and research data, generating 48 topics from 2,256 patents and 40 topics from 1,112 publications. While providing valuable insights into technology trends, the study acknowledges limitations in taking a macro approach to the entire AI semiconductor industry. Future research may explore specific technologies for more nuanced insights as the industry matures.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.11
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• pp.5178-5187
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• 2011

National Competency Standards(NCS) development related researches on the semiconductor industry were carried out partially, but the field training demand survey and analysis using that NCS were not done. The past demand survey for the job skill training had focused on personnel shortage and oversupply so it has the problems called skill mismatch. This study has the purpose to provide an alternative analysis of qualitative evaluation using the relative importance and gap of the job skill elements in the semiconductor industry. As research methods, we carried out related literature and report review, and a job skill demand survey on the semiconductor industry. We analyzed about the industry related jobs and job tasks, the qualitative demand for each job skill elements, and procurement methods for each job skills and manpower. We illustrated some related training courses to find out a relevant way for supplying the training programs.

• 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.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.18 no.1
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• pp.1-12
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• 2009

Presently, the semiconductor industry has the chronic problem. In the semiconductor industry, it has the semiconductor wafer, a package, the optical filter cut by using the saw blade, the mold, a laser etc. The cutting technique has the difficulty due to the rising of the production cost by the wearing of mold, the poor quality problem due to generated heat at the moment of cutting procedure and curve cutting etc. The goal of this time of national research and development project is develop the apparatus for solving the problem that the existing cutting technique has. The technology is so called waterjet abrasive method. This technology will be mainly applied to cut a semiconductor package and a wafer. Two important things to be considered are ripple effect(in other words, the scale of a market) and simplicity of an application.

• Journal of the Semiconductor & Display Technology
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• v.9 no.4
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• pp.45-50
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• 2010

This paper explores a new approach for modeling of decision-making problems that involve uncertain, time-dependent and sequence-dependent processes which can be applied to semiconductor industry. In the proposed approach, which is based on probability theory, approximate sample paths are required to be specified by probability and statistic characteristics. Completely specified sample paths are seen to be elementary and fundamental outcomes of the related experiment. The proposed approach is suitable for modeling real processes more accurately. A case study is applied to a single item production planning problem with continuous and uncertain demand and the solution obtained by the approximate path specification method shows less computational efforts and practically desirable features. The application possibility and general plan of the proposed approach in semiconductor manufacturing process is also described in the paper.

• Electronics and Telecommunications Trends
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• v.39 no.2
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• pp.66-76
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• 2024

Artificial-intelligence (AI) semiconductors are crucial for securing national core competitiveness, including dominating the AI and data ecosystem and succeeding in the Digital New Deal. When examining the macroenvironment, the global division of labor in the semiconductor industry has weakened owing to the technological competition between the United States and China. Major countries are aiming to build the entire semiconductor ecosystem around their territories. As a result, these countries are formulating policy goals tailored to their realities and actively pursuing key policies such as research and development, securing manufacturing bases, workforce development, and financial support. These policies also focus on intercountry cooperation and bold government policy support, which is deemed essential. To secure core competitiveness in AI semiconductors, South Korea needs to examine the policy directions of major countries and actively formulate and implement policies for this semiconductor industry.

• The Journal of the Korea Contents Association
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• v.20 no.2
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• pp.27-35
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• 2020

The Korean semiconductor industry is the top export item, and its technological prowess is also higher than that of its competitors. However, the technology gap with rivals will narrow. And the semiconductor industry is facing difficulties due to trade friction. Therefore, semiconductor firms should be more efficient in their production. we study analyzes the efficiency semiconductor firms using DEA model. We evaluate the CCR, BBC efficiency and RTS(return to scale) of 30 Korean semiconductor firms. There are a total of 13 efficient DMUs with a BCC of 1. There are a total of 6 efficient DMUs with a CCR of 1. A total of 10 DMUs were IRS in Scale Efficiency and a total of 9 DMUs were CRS in Scale Efficiency and a total of 11 DMUs were DRS in Scale Efficiency. We also suggest the semiconductor firms which can be benchmarked based on analyzed information.

• Korean Management Science Review
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• v.32 no.4
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• pp.69-80
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• 2015

The semiconductor industry is one of those in which the most intricate processes are involved and there are many critical factors that are controlled with precision in those processes. Naturally production scheduling in the semiconductor industry is also very complex and studied by the industry and academia for many years; however, still there are many issues left unclear in the problem. This paper proposes an multi-objective optimization-based scheduling method for semiconductor fabrication(fab). Two main objectives are throughput maximization and meeting target production quantities. The first objective aims to reduce production cost, especially the fixed cost incurred by a large investment constructing a new fab facility. The other is meeting customer orders on time and also helps a fab maintain stable throughput through controlled WIP balancing in the long run. The paper shows a trade-off structure between the two objectives through experimental studies, which provides industrial practitioners with useful references.

• Journal of the Semiconductor & Display Technology
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• v.23 no.2
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• pp.129-134
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• 2024

This study aims to talk about the necessity of solving the PFC gas emission problem raised by the recent development of the semiconductor industry and the remote plasma source method monitoring system used in the semiconductor industry. The 'monitoring system' means that the researchers applied machine learning to the existing monitoring technology and modeled it. In the process of this study, Residual Gas Analyzer monitoring technology and linear regression model were used. Through this model, the researchers identified emissions of at least 12700mg CO2 to 75800mg CO2 with values ranging from ion current 0.6A to 1.7A, and expect that the 'monitoring system' will contribute to the effective calculation of greenhouse gas emissions in the semiconductor industry in the future.