• Journal of the Korean Society for Precision Engineering
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• v.31 no.10
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• pp.881-887
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• 2014

Micro-sized Peltier coolers are generally employed for uniformly distributing heat generated in the multi-chip packages. These coolers are commonly classified into vertical and planar devices, depending on the heat flow direction and the arrangement of thermoelectric materials on the used substrate. Owing to the strong need for evaluation of performance of thermoelectric modules, at present an establishment of proper theoretical model has been highly required. The design theory for micro-sized thermoelectric cooler should be considered with contact resistance. Cooling performance of these modules was significantly affected by their contact resistance such as electrical and thermal junction. In this paper, we introduce the useful and optimal design model of small dimension thermoelectric module.

• Journal of the Microelectronics and Packaging Society
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• v.22 no.2
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• pp.47-53
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• 2015

The effects of electroless nickel immersion gold (ENIG) and organic solderability preservative (OSP) surface finishes on the in-situ intermetallics reaction and the electromigration (EM) reliability of Sn-3.0Ag-0.5Cu (SAC305) solder bump were systematically investigated. After as-bonded, $(Cu,Ni)_6Sn_5$ intermetallic compound (IMC) was formed at the interface of the ENIG surface finish at solder top side, while at the OSP surface finish at solder bottom side,$ Cu_6Sn_5$ and $Cu_3Sn$ IMCs were formed. Mean time to failure on SAC305 solder bump at $130^{\circ}C$ with a current density of $5.0{\times}10^3A/cm^2$ was 78.7 hrs. EM open failure was observed at bottom OSP surface finish by fast consumption of Cu atoms when electrons flow from bottom Cu substrate to solder. In-situ scanning electron microscope analysis showed that IMC growth rate of ENIG surface finish was much lower than that of the OSP surface finish. Therefore, EM reliability of ENIG surface finish was higher than that of OSP surface finish due to its superior barrier stability to IMC reaction.

• KSBB Journal
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• v.16 no.6
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• pp.603-608
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• 2001

In this study, we studied on the remeval of toluene vapors in a lab-scale biofilter. Biofiltration was performed in a column fed downflow with contaminated air at ambient conditions. The column was packed with mixture of Peat and Calstene(5:3 vol. Ratio), Synthesized media, Bark and Wood chip, which were inoculated with microbial population of selected stains(Pseudomonas. putida, KCCM 11343, ATCC 12633). The microorganisms were immobilized on the bed medium and then biofilm were formed. The biofilter was operated under the conditions of various inlet toluene concentrations for 180 days and treated up to the elimination capacity of maximum 40 g/㎥hr at the inlet load of 30 g/㎥ hr with percentage removals of 20∼90% and gas retention times between 1 and 2 min. The pressure drop was very negligible through the biofilter columps because its value of 0.054 cmH$_2$O/m was much less than others. The effect of operating conditions such as flow rate, inlet toluene concentration and moisture contents on the performance of the biofilter was sequentially investigated in this study.

• 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 Institute of Electronics Engineers of Korea SD
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• v.41 no.10
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• pp.95-104
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• 2004

This paper propose a motion estimator architecture to reduce the power consumption of the most-power-consuming motion estimation method when designing multimedia SoC with deep submicron technologies below 0.13${\mu}{\textrm}{m}$. The proposed architecture considers both dynamic and static power consumption so that it is suitable for large leakage process technologies, while conventional architectures consider only dynamic power consumption. Consequently, it is suitable for mobile information terminals such as mobile videophone where efficient power management is essential. It exploits full search method for simple hardware implementation. It also exploits early break-off method to reduce dynamic power consumption. To reduce static power consumption, megablock shutdown method considering power line noise is also employed. To evaluate the proposed architecture when applied multimedia SoC, system-level control flow and low-power control algorithm are developed and the power consumption was calculated based on thor From the simulation results, power consumption was reduced to about 60%. Considering the line width reduction and increased leakage current due to heat dissipation in chip core, the proposed architecture shows steady power reduction while it goes worse in conventional architectures.