• Fire Science and Engineering
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• v.14 no.4
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• pp.23-28
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• 2000

A performance-based design of smoke management systems for semiconductor fabrication facilities is described in this paper. The example of one such facility is discussed. Performance criteria for smoke control systems were established, effective smoke removal system features were identified and optimal system exhaust capacity requirements were developed by applying engineering tools including Fire Dynamic Simulator model. Considering the fact that the absence of relevant design guide, codes for consensus standards for semiconductor smoke design in Korea and United States this performance based approach can and should be applied to other fabrication facilities designs.

• IE interfaces
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• v.16 no.spc
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• pp.93-98
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• 2003

This paper addresses an AGV dispatching problem in semiconductor clean-room bays where AGVs move cassettes of wafers between machines or machines and a central buffer. Since each machine in a bay has a local buffer of limited capacity, material flow should be controlled in a careful way to maintain high system performance. It is regarded that two most important performance measures in a semiconductor bay are throughput rate and lead-time. The throughput rate is determined by a bottleneck resource and the lead-time depends on smooth material flow in the system. This paper presents an AGV dispatching procedure based on the concept of theory of constraints (TOC), by which dispatching decisions are made to utilize the bottleneck resource at the maximum level and to smooth the flow of material. The new dispatching procedure is compared with existing dispatching rules through simulation experiments.

• Transactions on Electrical and Electronic Materials
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• v.12 no.1
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• pp.16-19
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• 2011

The oxide-nitride-oxide (ONO) deposition process was added to the beginning of a 0.25 ${\mu}m$ embedded polysiliconoxide-nitride-oxide-silicon (SONOS) process before all of the logic well implantation processes in order to maintain the characteristics of basic CMOS(complementary metal-oxide semiconductor) logic technology. The system subsequently suffered severe ONO rupture failure. The damage was caused by the ONO implantation and was responsible for the ONO rupture failure in the embedded SONOS process. Furthermore, based on the experimental results as well as an implanted ion's energy loss model, processes primarily producing permanent displacement damages responsible for the ONO rupture failure were investigated for the embedded SONOS process.

• Journal of the Semiconductor & Display Technology
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• v.20 no.3
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• pp.161-166
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• 2021

To prevent accidents caused by changes in the surrounding environment or other factors, various protection facilities are installed at the photovoltaic connection module. The main causes of fire are sparks due to foreign substances inside the photovoltaic connection module through high temperature rise and dew condensation in the photovoltaic connection module, and fire due to heat from the power diode. The proposed method can predict the fire by measuring flame, carbon dioxide, carbon monoxide, temperature, humidity, input voltage, and current on the photovoltaic connection module, and when the fire conditions are reached, fire alarm and power off can be sent to managers and users in real time to prevent fire in advance.

• Journal of the Semiconductor & Display Technology
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• v.21 no.3
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• pp.130-134
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• 2022

An anti-reflective coating is used to improve the performance of the solar cell. The anti-reflective coating changes the value of the short-circuit current about the thickness. However, the current-voltage characteristics about the anti-reflective coating are difficult to calculate without simulation tool. In this paper, a modeling technique to determine the short-circuit current value and the current-voltage characteristics in accordance with the thickness is proposed. In addition, artificial neural network is used to predict the short-circuit current with the dependence of temperature and thickness. Simulation results incorporating the artificial neural network model are obtained using MATLAB/Simulink and show the current-voltage characteristic according to the thickness of the anti-reflective coating.

• Journal of the Semiconductor & Display Technology
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• v.21 no.3
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• pp.135-139
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• 2022

The current-voltage modeling plays an important role in characterizing photovoltaic systems. A solar cell has a nonlinear characteristic with various parameters influenced by the external environments such as the irradiance and the temperature. In order to accurately predict current-voltage characteristics at low irradiance, the artificial neural networks are applied to effectively quantify nonlinear behaviors. In this paper, a multi-layer perceptron scheme that can make accurate predictions is employed to learn complex formulas for large amounts of continuous data. The simulated results of artificial neural networks model show the accuracy improvement by using MATLAB/Simulink.

• Journal of the Semiconductor & Display Technology
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• v.21 no.1
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• pp.85-90
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• 2022

In this paper, an asynchronous nonvolatile memory module using a self-diagnosis function was designed. For the system to work, a lot of data must be input/output, and memory that can be stored is required. The volatile memory is fast, but data is erased without power, and the nonvolatile memory is slow, but data can be stored semi-permanently without power. The non-volatile static random-access memory is designed to solve these memory problems. However, the non-volatile static random-access memory is weak external noise or electrical shock, data can be some error. To solve these data errors, self-diagnosis algorithms were applied to non-volatile static random-access memory using error correction code, cyclic redundancy check 32 and data check sum to increase the reliability and accuracy of data retention. In addition, the possibility of application to an asynchronous non-volatile storage system requiring reliability was suggested.

• Journal of Korea Trade
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• v.25 no.6
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• pp.78-104
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• 2021

Purpose - Semiconductors are a significant export item for Korea that is expected to continue to contribute significantly to the Korean economy in the future. Thus, the semiconductor industry is a critical component in the 4th Industrial Revolution and is expected to continue growing as the non-face-to-face economy expands as a result of the COVID-19 pandemic. In this context, this paper aims to empirically investigate how semiconductors are imported and exported in Korea from a global supply chain perspective by analysing import and export data at the micro-level. Design/methodology - This study conducts a multifaceted analysis of the global supply chain for semiconductors and related equipment in Korea by examining semiconductor imports and exports by semiconductor type, year, target country, mode of transportation, airport/port, and domestic region, using import/export micro-data. The visualisation, flow analysis, and Bayesian Network methodologies were used to compensate for the limitations of each method. Findings - Korea is a major exporter of semiconductor memory and has the world's highest competitiveness but is relatively weak in the field of system semiconductors. The trade deficit in 'semiconductor equipment and parts' is clearly growing. As a result, continued investment in 'system semiconductors' and 'semiconductor equipment and parts' technology development is necessary to boost exports and ensure a stable supply chain. Originality/value - Few papers on semiconductor trade in Korea have been published from the perspective of the global supply chain or value chain. This study contributes to the literature in this area by focusing on import and export data for the global supply chain of the Korean semiconductor industry using a variety of approaches. It is our hope that the insights gained from this study will aid in the advancement of SCM research.

• JSTS:Journal of Semiconductor Technology and Science
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• v.16 no.5
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• pp.657-663
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• 2016

In this work, a novel silicon (Si) based floating body synaptic transistor (SFST) is studied to mimic the transition from short-term memory to long-term one in the biological system. The structure of the proposed SFST is based on an n-type metal-oxide-semiconductor field-effect transistor (MOSFET) with floating body and charge storage layer which provide the functions of short- and long-term memories, respectively. It has very similar characteristics with those of the biological memory system in the sense that the transition between short- and long-term memories is performed by the repetitive learning. Spike timing-dependent plasticity (STDP) characteristics are closely investigated for the SFST device. It has been found from the simulation results that the connectivity between pre- and post-synaptic neurons has strong dependence on the relative spike timing among electrical signals. In addition, the neuromorphic system having direct connection between the SFST devices and neuron circuits are designed.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.10
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• pp.837-842
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• 2001

In this paper, we design and implement the DC characteristic measurement system for semiconductor devices. The proposed system is composed of 4 SMU(Source and Measure Unit) channels. Various efforts in hardware and software have been made to reduce the measurement errors. Internal and external sources of errors in measurement system especially in pA range measurement have been identified and removed. Also, various digital signal processing techniques are developed. Calibration is executed under the control of microprocessor periodically. Experimental results show that the implemented system can measure the DC characteristic of semiconductor devices with less than 0.2% error in various voltage and current source/measurement range.