• Journal of the Institute of Electronics Engineers of Korea SD
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• v.43 no.3 s.345
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• pp.9-14
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• 2006

The dependence of NBTI lifetime on the BEOL processes such as sintering gas type and passivation layer has been characterized in depth. Then, optimized BEOL process scheme is proposed to improve NBTI lifetime. NBTI showed degradation due to the plasma enhanced nitride (PE-SiN) passivation film and $H_2$ sintering anneal. Then, new process scheme of $N_2$ annealing instead of $H_2$ annealing prior to PE-SiN deposition is proposed. The proposed BEOL process flow showed that NBTI lifetime can be improved a lot without degradation of device performance and NMOS hot carrier reliability.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1505-1509
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• 2005

This paper introduces a new architecture of genetically optimized self-organizing fuzzy polynomial neural networks by means of information granulation. The conventional SOFPNNs developed so far are based on mechanisms of self-organization and evolutionary optimization. The augmented genetically optimized SOFPNN using Information Granulation (namely IG_gSOFPNN) results in a structurally and parametrically optimized model and comes with a higher level of flexibility in comparison to the one we encounter in the conventional FPNN. With the aid of the information granulation, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. The GA-based design procedure being applied at each layer of genetically optimized self-organizing fuzzy polynomial neural networks leads to the selection of preferred nodes with specific local characteristics (such as the number of input variables, the order of the polynomial, a collection of the specific subset of input variables, and the number of membership function) available within the network. To evaluate the performance of the IG_gSOFPNN, the model is experimented with using gas furnace process data. A comparative analysis shows that the proposed IG_gSOFPNN is model with higher accuracy as well as more superb predictive capability than intelligent models presented previously.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.20-20
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• 2000

Back-propagation neural network (BPNN) is the most prevalently used paradigm in modeling semiconductor manufacturing processes, which as a neuron activation function typically employs a bipolar or unipolar sigmoid function in either hidden and output layers. In this study, applicability of another linear function as a neuron activation function is investigated. The linear function was operated in combination with other sigmoid functions. Comparison revealed that a particular combination, the bipolar sigmoid function in hidden layer and the linear function in output layer, is found to be the best combination that yields the highest prediction accuracy. For BPNN with this combination, predictive performance once again optimized by incrementally adjusting the gradients respective to each function. A total of 121 combinations of gradients were examined and out of them one optimal set was determined. Predictive performance of the corresponding model were compared to non-optimized, revealing that optimized models are more accurate over non-optimized counterparts by an improvement of more than 30%. This demonstrates that the proposed gradient-optimized teaming for BPNN with a linear function in output layer is an effective means to construct plasma models. The plasma modeled is a hemispherical inductively coupled plasma, which was characterized by a 24 full factorial design. To validate models, another eight experiments were conducted. process variables that were varied in the design include source polver, pressure, position of chuck holder and chroline flow rate. Plasma attributes measured using Langmuir probe are electron density, electron temperature, and plasma potential.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.8
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• pp.551-560
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• 2004

In this paper, we propose a new architecture of Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) that is based on a genetically optimized multilayer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms (GAs). The proposed SOFPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional SOFPNNs. The design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics (such as the number of input variables, the order of the polynomial of the consequent part of fuzzy rules, and a collection of the specific subset of input variables) and addresses specific aspects of parametric optimization. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. To evaluate the performance of the genetically optimized SOFPNN, the model is experimented with using two time series data(gas furnace and chaotic time series), A comparative analysis reveals that the proposed SOFPNN exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literatures.

• Proceedings of the KIEE Conference
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• 2005.11a
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• pp.151-153
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• 2005

Indium tin oxide (ITO) thin film was polished by chemical mechanical polishing (CMP) by the change of process parameters for the improvement of electrical and optical properties of ITO thin film. Light transparent efficiency of ITO thin film was improved after CMP process at the optimized process parameters compared to that before CMP process.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2005.11a
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• pp.354-355
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• 2005

Indium tin oxide (ITO) thin film was polished by chemical mechanical polishing (CMP) by the change of process parameters for the improvement of electrical and optical properties of ITO thin film. Light transparent efficiency of ITO thin film was improved after CMP process at the optimized process parameters compared to that before CMP process.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.3
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• pp.61-67
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• 2020

The fourth industrial revolution encourages manufacturing industry to pursue a new paradigm shift to meet customers' diverse demands by managing the production process efficiently. However, it is not easy to manage efficiently a variety of tasks of all the processes including materials management, production management, process control, sales management, and inventory management. Especially, to set up an efficient production schedule and maintain appropriate inventory is crucial for tailored response to customers' needs. This paper deals with the optimized inventory policy in a steel company that produces granule products under supply contracts of three targeted on-time delivery rates. For efficient inventory management, products are classified into three groups A, B and C, and three differentiated production cycles and safety factors are assumed for the targeted on-time delivery rates of the groups. To derive the optimized inventory policy, we experimented eight cases of combined safety stock and data analysis methods in terms of key performance metrics such as mean inventory level and sold-out rate. Through simulation experiments based on real data we find that the proposed optimized inventory policy reduces inventory level by about 9%, and increases surplus production capacity rate, which is usually used for the production of products in Group C, from 43.4% to 46.3%, compared with the existing inventory policy.

• Journal of the Korea Institute of Building Construction
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• v.14 no.5
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• pp.467-476
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• 2014

According to the expanding of construction machinery works, the number of civil complaints demanding compensations are increasing continuously from surrounding residents due to the noise from construction fields. However, the noise is usually managed restrictedly during the construction phase rather than prevented in advance. So, the efforts to solve the noise problem are occurring only after complaints have been made. Also, excessive cost and time consuming in order to solve the complaints negatively affects to construction companies. Therefore, the purpose of this study is to develop an optimized scheduling process model for controlling the noise in construction field by considering the planned time, cost, and the number of equipment before construction. In addition, this process model is expected to provide a useful information about the cost comparison between the original planned cost plus compensation and the optimized cost considering noise limitation so that the site managers can manage their projects effectively.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.209-213
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• 1987

This paper presents decentralized discrete-time system which is optimized by hierarchical control for process automation via the extended interaction balance method. This proposed method can control general matrix which input matrix is not block diagonalization. Also, this paper shows convergence condition of proposed method.

• 한국전기화학회:학술대회논문집
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• 2004.06a
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• pp.219-224
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• 2004