• Transactions of Materials Processing
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• v.5 no.1
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• pp.37-46
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• 1996

Superplastic forming/diffusion bonding (SPF/DB) processes were analyzed using a rigid visco-plastic finite element method. The optimum pressure-time relationship for a target strain rate and thickness distributions were predicted by two-node line elements based on the membrane approximation for plane strain. Material behavior during SPF/DB of the integral structures having complicated shapes was investigated. The tying condition is employed for the analysis of inter-sheet contact problems. A movement of rib structure is successfully predicted during the forming.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1995.03a
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• pp.155-161
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• 1995

Superplastic forming/diffusion bonding(SPF/DB) processes are analyzed using a rigid visco-plastic finite element method. The optimum pressure-time relationship for a target strain rate and thickness distributions were predicted using two-node line element based on membrane approximation for plane strain shapes. Material behavior during SPF/DB of the integral structures with complicated shapes are investigated. The tying condition is employed for the analysis inter-sheet contact problems. A movement of rib structure is successfully prodicted during the forming.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2007.11a
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• pp.508-509
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• 2007

For the safty driving of an automobile which is become individual requisites, a new Neural Network algorithm which recognized the load vehicles in real time is proposed. The proposed neural network technique is the real time computation method through the inter-node diffusion. The most reliable algorithm derived for real time recognition of vehicles, is a dynamic programming based algorithm based on sequence matching techniques that would process the data as it arrives and could therefore provide continuously updated neighbor information estimates.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2003.05b
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• pp.90-95
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• 2003

For the safety driving of an automobile which is become individual requisites, a new Neural Network algorithm which recognized the load vehicles in real time is proposed. The proposed neural network technique is the real time computation method through the inter-node diffusion. In the network, a node corresponds to a state in the quantized input space. Each node is composed of a processing unit and fixed weights from its neighbor nodes as well as its input terminal. The most reliable algorithm derived for real time recognition of vehicles, is a dynamic programming based algorithm based on sequence matching techniques that would process the data as it arrives and could therefore provide continuously updated neighbor information estimates. Through several simulation experiments, real time reconstruction of the nonlinear image information is processed. 1-D LIPN hardware has been composed and various experiments with static and dynamic signals have been implemented.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.546-549
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• 1999

For the safty driving of an automobile which is become individual requisites, a new Neural Network algorithm which recognized the load vehicles in real time is proposed. The proposed neural network technique is the real time computation method through the inter-node diffusion. In the network, a node corresponds to a state in the quantized input space. Each node is composed of a processing unit and fixed weights from its neighbor nodes as well as its input terminal. The most reliable algorithm derived for real time recognition of vehicles, is a dynamic programming based algorithm based on sequence matching techniques that would process the data as it arrives and could therefore provide continuously updated neighbor information estimates. Through several simulation experiments, real time reconstruction of the nonlinear image information is processed 1-D LIPN hardware has been composed and various experiments with static and dynamic signals have been implmented.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2130-2133
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• 2003

For real-time recognizing of the load vehicles a new Neural Network algorithm is proposed. The proposed neural network technique is the real time computation method through the inter-node diffusion. In the network, a node corresponds to a state in the quantized input space. Each node is composed of a Processing unit and fixed weights from its neighbor nodes as well as its input terminal. The most reliable algorithm derived for real time recognition of vehicles, is a dynamic programming based algorithm based on sequence matching techniques that would process the data as it arrives and could therefore provide continuously updated neighbor information estimates. Through severa simulation experiments, real time reconstruction nonlinear image information is Processed. 1-D hardware has been composed and various experi with static and dynamic signals have implemented.

• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.229-229
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• 2010

With the scaling down of ultra large integrated circuits (ULSI) to the sub-50 nm technology node, the need for an ultra-thin, continuous and conformal diffusion barrier and Cu seed layer is increasing. However, diffusion barrier and Cu seed layer formation with a physical vapor deposition (PVD) method has become difficult as the technology node is reduced to 30 nm and beyond. Recent work on self-forming barrier processes using PVD Cu alloys have attracted great attention due to the capability of conformal ultra-thin barrier formation using a simple technique. However, as in the case of the conventional barrier and Cu seed layer, PVD of the Cu alloy seed layer will eventually encounter the difficulty in conformal deposition in narrow line trenches and via holes. Atomic layer deposition (ALD) has been known for its good step coverage and precise thickness control, and is a candidate technique for the formation of a thin conformal barrier layer and Cu seed layer. Conformal Cu-Mn seed layers were deposited by plasma enhanced atomic layer deposition (PEALD) at low temperature ($120^{\circ}C$), and the Mn content in the Cu-Mn alloys were controlled form 0 to approximately 10 atomic percent with various Mn precursor feeding times. Resistivity of the Cu-Mn alloy films decreased by annealing due to out-diffusion of Mn atoms. Out-diffused Mn atoms were segregated to the surface of the film and interface between a Cu-Mn alloy and $SiO_2$, resulting in self-formed $MnO_x$ and $MnSi_xO_y$, respectively. No inter-diffusion was observed between Cu and $SiO_2$ after annealing at $500^{\circ}C$ for 12 h, indicating an excellent diffusion barrier property of the $MnSi_xO_y$. The adhesion between Cu and $SiO_2$ was enhanced by the formation of $MnSi_xO_y$. Continuous and conductive Cu-Mn seed layers were deposited with PEALD into 32 nm $SiO_2$ trench, enabling a low temperature process, and the trench was perfectly filled using electrochemical plating (ECD) under conventional conditions. Thus, it is the resultant self-forming barrier process with PEALD Cu-Mn alloy film as a seed layer for plating Cu that has further potential to meet the requirement of the smaller than 30 nm node.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2531-2533
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• 2005

The proposed neural network technique is the real time computation method based theory of inter-node diffusion for searching the safety distances from the sudden appearance-objects during the work driving. The main steps of the distance computation using the theory of stereo vision like the eyes of man is following steps. One is the processing for finding the corresponding points of stereo images and the other is the interpolation processing of full image data from nonlinear image data of objects. All of therm request much memory space and time. Therefore the most reliable neural-network algorithm is drived for real-time matching of obejects, which is composed of a dynamic programming algorithm based on sequence matching techniques in moving objects.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2003.05c
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• pp.194-199
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• 2003

Depth error correction effect for maladjusted stereo cameras with calibrated pixel distance parameter is presented. The proposed neural network technique is the real time computation method based theory of inter-node diffusion for searching the safety distances from the sudden appearance-objects during the work driving. The main steps of the distance computation using the theory of stereo vision like the eyes of man is following steps. One is the processing for finding the corresponding points of stereo images and the other is the interpolation processing of full image data from nonlinear image data of objects. All of them request much memory space and time. Therefore the most reliable neural-network algorithm is derived for real-time matching of objects, which is composed of a dynamic programming algorithm based on sequence matching techniques.

• The Journal of the Korea Contents Association
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• v.7 no.6
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• pp.44-51
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• 2007

For real-time recognizing of model objects in remote position a new Neural Networks algorithm is proposed. The proposed neural networks technique is the real time computation methods through the inter-node diffusion. In the networks, a node corresponds to a state in the quantized input space. Each node is composed of a processing unit and fixed weights from its neighbor nodes as well as its input terminal. The most reliable algorithm derived for real time recognition of objects, is a dynamic programming based algorithm based on sequence matching techniques that would process the data as it arrives and could therefore provide continuously updated neighbor information estimates. Through several simulation experiments, real time reconstruction of the nonlinear image information is processed. 1-D LIPN hardware has been composed and various experiments with static and dynamic signals have been implemented.