• Journal of the Semiconductor & Display Technology
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• v.21 no.1
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• pp.148-154
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• 2022

As the 4th industrial age approaches, the demand for semiconductors is increasing enough to be used in all electronic devices. At the same time, semiconductor technology is also developing day by day, leading to ultraprecision and low power consumption. Semiconductors that keep getting smaller generate heat because the energy density increases, and the generated heat changes the shape of the semiconductor package, so it is important to manage. The temperature change is not only self-heating of the semiconductor package, but also heat generated by external damage. If the package is deformed, it is necessary to manage it because functional problems and performance degradation such as damage occur. The package burn in test in the post-process of semiconductor production is a process that tests the durability and function of the package in a high-temperature environment, and heat dissipation performance can be evaluated. In this paper, we intend to review a new material formulation that can improve the performance of the adapter, which is one of the parts of the test socket used in the burn-in test. It was confirmed what characteristics the basic base showed when polyamide, a high-molecular material, and alumina, which had high thermal conductivity, were mixed for each magnification. In this study, functional evaluation was also carried out by injecting an adapter, a part of the test socket, at the same time as the specimen was manufactured. Verification of stiffness such as tensile strength and flexural strength by mixing ratio, performance evaluation such as thermal conductivity, and manufacturing of a dummy device also confirmed warpage. As a result, it was confirmed that the thermal stability was excellent. Through this study, it is thought that it can be used as basic data for the development of materials for burn-in sockets in the future.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.611-612
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• 2006

Many data mining techniques have been proved useful in revealing important patterns from large data sets. Especially, data mining techniques play an important role in a customer data analysis in a financial industry and an electronic commerce. Also, there are many data mining related research papers in a semiconductor industry and an automotive industry. In addition, data mining techniques are applied to the bioinformatics area. To satisfy customers' various requirements, each industry should develop new processes with more accurate production criteria. Also, they spend more money to guarantee their products' quality. In this manner, we apply data mining techniques to the production-related data such as a test data, a field claim data, and POP (point of production) data in the automotive parts industry. Data collection and transformation techniques should be applied to enhance the analysis results. Also, we classify various types of manufacturing processes and proposed an analysis scheme according to the type of manufacturing process. As a result, we could find inter- or intra-process relationships and critical features to monitor the current status of the each process. Finally, it helps an industry to raise their profit and reduce their failure cost.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.664-667
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• 1997

For higher component density per chip, it is necessary to identify and control the semiconductor manufacturing process more stringently. Recently, neural networks have been identified as one of the most promising techniques for modeling and control of complicated processes such as plasma etching process. Since wafer states after each run using identical recipe may differ from each other, conventional neural network models utilizing input factors only cannot represent the actual state of process and equipment. In this paper, in addition to the input factors of the recipe, real-time tool data are utilized for modeling of 64M DRAM s-poly plasma etching process to reflect the actual state of process and equipment. For real-time tool data, we collect optical emission spectroscopy (OES) data. Through principal component analysis (PCA), we extract principal components from entire OES data. And then these principal components are included to input parameters of neural network model. Finally neural network model is trained using feed forward error back propagation (FFEBP) algorithm. As a results, simulation results exhibit good wafer state prediction capability after plasma etching process.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.239-240
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• 2012

With advancements in semiconductor device technologies, manufacturing processes are getting more complex and it became more difficult to maintain tighter process control. As the number of processing step increased for fabricating complex chip structure, potential fault inducing factors are prevail and their allowable margins are continuously reduced. Therefore, one of the key to success in semiconductor manufacturing is highly accurate and fast fault detection and classification at each stage to reduce any undesired variation and identify the cause of the fault. Sensors in the equipment are used to monitor the state of the process. The idea is that whenever there is a fault in the process, it appears as some variation in the output from any of the sensors monitoring the process. These sensors may refer to information about pressure, RF power or gas flow and etc. in the equipment. By relating the data from these sensors to the process condition, any abnormality in the process can be identified, but it still holds some degree of certainty. Our hypothesis in this research is to capture the features of equipment condition data from healthy process library. We can use the health data as a reference for upcoming processes and this is made possible by mathematically modeling of the acquired data. In this work we demonstrate the use of recurrent neural network (RNN) has been used. RNN is a dynamic neural network that makes the output as a function of previous inputs. In our case we have etch equipment tool set data, consisting of 22 parameters and 9 runs. This data was first synchronized using the Dynamic Time Warping (DTW) algorithm. The synchronized data from the sensors in the form of time series is then provided to RNN which trains and restructures itself according to the input and then predicts a value, one step ahead in time, which depends on the past values of data. Eight runs of process data were used to train the network, while in order to check the performance of the network, one run was used as a test input. Next, a mean squared error based probability generating function was used to assign probability of fault in each parameter by comparing the predicted and actual values of the data. In the future we will make use of the Bayesian Networks to classify the detected faults. Bayesian Networks use directed acyclic graphs that relate different parameters through their conditional dependencies in order to find inference among them. The relationships between parameters from the data will be used to generate the structure of Bayesian Network and then posterior probability of different faults will be calculated using inference algorithms.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.6 no.4
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• pp.28-35
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• 2007

Currently, most of all valves using at the semiconductor & LCD manufacturing process are packless type. Such valves are needed to have the functions to protect perfectly from the leakage of fluid by using bellows and diaphragm instead of the grand packing. But not only those valves made in Korea are not available at this point, but also advanced foreign manufacturers do not open enough their data on the basic technology. Otherwise although they open the data a little, they are almost data about stainless steel for pneumatic valves. In this paper, it was focused on the fluid valves for chemical & DI water based on the data of steel valves which are already using commercially. And also this study concentrated to collect the basic developing data of Eco-On/Off diaphragm valve by ourselves.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.26 no.4
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• pp.278-283
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• 2013

This paper presents a design and fabrication of 0.5 V two stage operational amplifier. The proposed operational amplifier utilizes body-driven differential input stage and self-cascode current mirror structure. Cadence Virtuoso is used for layout and the layout data is verified by LVS through Mentor Calibre. The proposed two stage operational amplifier is fabricated using $0.13{\mu}m$ CMOS process and operation at 0.5 V is confirmed. Measured low frequency small signal gain of operational amplifier is 50 dB, power consumption is $29{\mu}W$ and chip area is $75{\mu}m{\times}90{\mu}m$.

• JSTS:Journal of Semiconductor Technology and Science
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• v.15 no.4
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• pp.454-461
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• 2015

Light enhancement of GaN based light emitting diodes (LEDs) have been investigated by texturing the top p-GaN surface. Nano-textured LEDs have been fabricated using self-assembled Ni nano mask during dry etching process. Experimental results were further compared with simulation data. Three types of LEDs were fabricated: Conventional (planar LED), Surface nano-porous (porous LED) and Surface nano-cluster (cluster LED). Compared to planar LED there were about 100% and 54% enhancement of light output power for porous and cluster LED respectively at an injection current of 20 mA. Moreover, simulation result showed consistency with experimental result. The increased probability of light scattering at the nano-textured GaN-air interface is the major reason for increasing the light extraction efficiency.

• IE interfaces
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• v.20 no.3
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• pp.288-297
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• 2007

Success of semiconductor/LCD industry depends on its yield and quality of product. For the purpose, FDC (Fault Detection and Classification) system is used to diagnose fault state in main manufacturing processes by monitoring time series data collected by equipment sensors which represent various conditions of the equipment. The data set is segmented at the start and end of each product lot processing by a trigger event module. However, in practice, segmented sensor data usually have the features of data asynchronization such as different start points, end points, and data lengths. Due to the asynchronization problem, false alarm (type I error) and missed alarm (type II error) occur frequently. In this paper, we propose a robust process fault detection system by integrating a process event detection method and a similarity measuring method based on dynamic time warping algorithm. An experiment shows that the proposed system is able to recognize abnormal condition correctly under the asynchronous data situation.

• Journal of the Semiconductor & Display Technology
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• v.19 no.3
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• pp.73-76
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• 2020

The physics-based compact gate leakage current noise models in nanoscale MOSFETs are developed in such a way that the models incorporate important physical effects and are suitable for circuit simulators, including QM (quantum-mechanical) effects. An emphasis on the trap-related parameters of noise models is laid to make the models adaptable to the variations in different process technologies and to make its parameters easily extractable from measured data. With the help of an accurate and generally applicable compact noise models, the compact noise models are successfully implemented into BSIM (Berkeley Short-channel IGFET Model) format. It is shown that the noise models have good agreement with measurements over the frequency, gate-source and drain-source bias ranges.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.4
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• pp.164-169
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• 2007

The estimation of mortality characteristics of industrial property is an important adjunct to engineering valuation and depreciation estimation. Once the important of depreciation estimation is determined, it is desirable to understand the processes upon which these estimates are based. The Iowa type survivor curves are a set of generalized retirement dispersion models. These curves were based on analysis of actual retirement experience and represent typical retirement behavior patterns likely to be encountered. The retirement rate of Iowa type survivor curves on the semiconductor equipments in Korea industry was estimated by the life estimation process. In this paper, estimates of service lives based on directly observed data of the domestic semiconductor equipments are presented.