• JSTS:Journal of Semiconductor Technology and Science
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• 제11권3호
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• pp.135-145
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• 2011

Accurate process characterization and optimization are the first step for a successful advanced process control (APC), and they should be followed by continuous monitoring and control in order to run manufacturing processes most efficiently. In this paper, process characterization and recipe optimization methods with multiple outputs are presented in high density plasma-chemical vapor deposition (HDP-CVD) silicon dioxide deposition process. Five controllable process variables of Top $SiH_4$, Bottom $SiH_4$, $O_2$, Top RF Power, and Bottom RF Power, and two responses of interest, such as deposition rate and uniformity, are simultaneously considered employing both statistical response surface methodology (RSM) and neural networks (NNs) based genetic algorithm (GA). Statistically, two phases of experimental design was performed, and the established statistical models were optimized using performance index (PI). Artificial intelligently, NN process model with two outputs were established, and recipe synthesis was performed employing GA. Statistical RSM offers minimum numbers of experiment to build regression models and response surface models, but the analysis of the data need to satisfy underlying assumption and statistical data analysis capability. NN based-GA does not require any underlying assumption for data modeling; however, the selection of the input data for the model establishment is important for accurate model construction. Both statistical and artificial intelligent methods suggest competitive characterization and optimization results in HDP-CVD $SiO_2$ deposition process, and the NN based-GA method showed 26% uniformity improvement with 36% less $SiH_4$ gas usage yielding 20.8 ${\AA}/sec$ deposition rate.

• 제어로봇시스템학회논문지
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• 제8권7호
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• pp.589-598
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• 2002

The color measuring/matching expert system is implemented with a new color measuring method that combines intelligent algorithms with image processing techniques. Color measuring part of the proposed system preprocesses the scanned original color input images to eliminate their distorted components by means of the image histogram technique of image pixels, and then extracts RGB(Red, Green, Blue)data among color information from preprocessed color input images. If the extracted RGB color data does not exist on the matching recipe databases, we can measure the colors for the user who want to implement the model that can search the rules for the color mixing information, using the intelligent modeling techniques such as fuzzy inference system and adaptive neuro-fuzzy inference system. Color matching part can easily choose images close to the original color for the user by comparing information of preprocessed color real input images with data-based measuring recipe information of the expert, from the viewpoint of the delta Eformula used in practical process.

• 전자공학회논문지CI
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• 제42권5호
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• pp.31-40
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• 2005

본 연구에서는 웨이퍼의 식각, 세정, 연마 공정에 사용되는 매엽식 세정장비의 동작순서의 시뮬레이션과 단위 시간당 처리량 측정 방법에 대해 연구한다. 유한상태기계를 바탕으로 스케쥴링 알고리즘에 따른 로봇의 상태를 정의하여 시뮬레이션 모델을 구축하였으며, 이에 따른 시뮬레이션 수행을 통해 세정장비의 시간당 처리량을 측정하였다. 본 연구에서 제시한 시뮬레이션 기법을 통해 레시피와 로봇의 동작속도에 따라 세정장비의 단위시간당 처리량을 측정하고, 처리량을 극대화 할 수 있는 레시피와 로봇의 동작순서를 찾아낼 수 있다.

• 동아시아식생활학회지
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• 제19권3호
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• pp.421-429
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• 2009

This study was conducted to develop a recipe for a nutritional cookie containing Danggue powder, and to achieve an optimal ingredient composition and texture that would appeal to consumers of all ages. To reduce its content, wheat flour was partially substituted with Danggue in the formulation. Response surface methodology was used to analyze the measured results and showed 16 experimental points, including 2 replicates for the Danggue powder, brown sugar, and butter ingredients. The compositional and functional properties were measured, and these values were applied to a mathematical model. A canonical form and perturbation plot showed the influence of each ingredient on the final product. The sensory evaluation results indicated significant differences between samples for color (p<0.01), flavor (p<0.01), texture (p<0.05), and overall quality (p<0.05). As a result, the optimal ingredient levels for sensory quality were determined as 4.83 g of Danggue powder, 70.46 g of brown sugar, and 86.08 g of butter.

• 식품보건융합연구
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• 제9권4호
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• pp.19-23
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• 2023

The integration of ChatGPT, an AI-powered language model, is causing a profound transformation within the food industry, impacting various domains. It offers novel capabilities in recipe creation, personalized dining, menu development, food safety, customer service, and culinary education. ChatGPT's vast culinary dataset analysis aids chefs in pushing flavor boundaries through innovative ingredient combinations. Its personalization potential caters to dietary preferences and cultural nuances, democratizing culinary knowledge. It functions as a virtual mentor, empowering enthusiasts to experiment creatively. For personalized dining, ChatGPT's language understanding enables customer interaction, dish recommendations based on preferences. In menu development, data-driven insights identify culinary trends, guiding chefs in crafting menus aligned with evolving tastes. It suggests inventive ingredient pairings, fostering innovation and inclusivity. AI-driven data analysis contributes to quality control, ensuring consistent taste and texture. Food writing and marketing benefit from ChatGPT's content generation, adapting to diverse strategies and consumer preferences. AI-powered chatbots revolutionize customer service, improving ordering experiences, and post-purchase engagement. In culinary education, ChatGPT acts as a virtual mentor, guiding learners through techniques and history. In food safety, data analysis prevents contamination and ensures compliance. Overall, ChatGPT reshapes the industry by uniting AI's analytics with culinary expertise, enhancing innovation, inclusivity, and efficiency in gastronomy.

• 한국의류학회지
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• 제38권3호
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• pp.347-354
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• 2014

The color appearance of a yarn-dyed woven fabric depends on the color of the yarn as well as on the weave structure. Predicting the final color appearance or formulating the recipe is a difficult task, considering the interference of colored yarns and structure variations. In a modern fabric design process, the intended color appearance is attained through a digital color methodology based on numerous color data and color mixing recipes (i.e., color prediction models, accumulated in CAD systems). For successful color reproduction, accurate color prediction models should be devised and equipped for the systems. In this study, the final colors of yarn-dyed woven fabrics were predicted using six geometric-color mixing models (i.e., simple K/S model, log K/S model, D-G model, S-N model, modified S-N model, and W-O model). The color differences between the measured and the predicted colors were calculated to evaluate the accuracy of various color models used for different weave structures. The log K/S model, D-G model, and W-O model were found to be more accurate in color prediction of the woven fabrics used. Among these three models, the W-O model was found to be the best one as it gave the least color difference between the measured and the predicted colors.

• Industrial Engineering and Management Systems
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• 제4권1호
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• pp.54-67
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• 2005

This paper provides an evaluation of an optimization-based, multiple-input double-output (MIDO) run-to-run (R2R) control scheme for general semiconductor manufacturing processes. The controller in this research, termed adaptive dual response optimizing controller (ADROC), can serve as a process optimizer as well as a recipe regulator between consecutive runs of wafer fabrication. In evaluation, it is assumed that the equipment model could be appropriately described by a pair of second-order polynomial functions in terms of a set of controllable variables. Of practical relevance is to consider a drifting effect in the equipment model since in common semiconductor practice the process tends to drift due to machine aging and tool wearing. We select a typical application of R2R control to chemical mechanical planarization (CMP) in semiconductor manufacturing in this evaluation, and there are five different CMP process scenarios demonstrated, including mean shift, variance increase, and IMA disturbances. For the controller, ADROC, an on-line estimation technique is implemented in a self-tuning (ST) control manner for the adaptation purpose. Subsequently, an ad hoc global optimization algorithm based on the dual response approach, arising from the response surface methodology (RSM) literature, is used to seek the optimum recipe within the acceptability region for the execution of next run. The main components of ADROC are described and its control performance is assessed. It reveals from the evaluation that ADROC can provide excellent control actions for the MIDO R2R situations even though the process exhibits complicated, nonlinear interaction effects between control variables, and the drifting disturbances.

• 대한본초학회지
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• 제30권2호
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• pp.19-24
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• 2015

Objectives : The experiment was performed to investigate promotive effects of haeae-tang (HET) extract, a traditional Korean medicinal recipe, on hair growth, protein and gene expression in hair-removed C57BL/6. Methods : In experiment, animals were divided into 3 groups including normal (vehicle), HET ethanol extract and 5% minoxidil-treated groups (Minoxidil, positive control). The vehicle or testing samples were daily treated with 0.2ml per on hair-shaved dorsal skin of C57BL/6mice for 15 days. Effects of testing samples on hair growth was monitored through phototrichogram analysis by folliscope on the initial, $5^{th}$, $10^{th}$, $15^{th}$ day, respectively. Also, gene and protein expressions of vascular endothelial growth factor (VEGF) and Insulin like growth factor-1 (IGF-1), relevant to hair growth, were examined. Results : Hair density and hair thickness of Minoxidil treated-group was significantly increased compared to that of vehicle application on the $15^{th}$day, respectively. Dorsal hair density of HET treated-group was significantly increased compared to that of vehicle application on the $15^{th}$day. In addition, the Minoxidil group significantly increased the expression of cutaneous IGF-1 protein and mRNA compared to that of the vehicle-applied group on the $15^{th}$ day. And HET-treated group significantly increased the expression of dorsal VEGF protein compared to that of the vehicle-applied group on the $15^{th}$ day. Conclusions : These results suggest that this Korean medicinal recipe, HET has promoting activity on hair growth in an Alopecia animal model thus it can be used as a material of agent or products for improvement or prevention of alopecia.

• 제어로봇시스템학회논문지
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• 제6권1호
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• pp.104-111
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• 2000

A fuzzy logic Run-by-Run(RbR) controller and an in -line wafer characteristics prediction scheme for the rapid thermal processing system have been developed for the study of process repeatability. The fuzzy logic RbR controller provides a framework for controlling a process which is subject to disturbances such as shifts and drifts as a normal part of its operation. The fuzzy logic RbR controller combines the advantages of both fuzzy logic and feedback control. It has two components : fuzzy logic diagnostic system and model modification system. At first, a neural network model is constructed with the I/O data collected during the designed experiments. The wafer state after each run is assessed by the fuzzy logic diagnostic system with featuring step. The model modification system updates the existing neural network process model in case of process shift or drift, and then select a new recipe based on the updated model using genetic algorithm. After this procedure, wafer characteristics are predicted from the in-line wafer characteristics prediction model with principal component analysis. The fuzzy logic RbR controller has been applied to the control of Titanium SALICIDE process. After completing all of the above, it follows that: 1) the fuzzy logic RbR controller can compensate the process draft, and 2) the in-line wafer characteristics prediction scheme can reduce the measurement cost and time.

• Journal of international Conference on Electrical Machines and Systems
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• 제3권3호
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• pp.339-353
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• 2014

A straightforward method for optimal determining of a high frequency inductor's parasitic capacitance is presented. The proposed estimation method is based on measuring the inductor's impedance samples over a limited frequency range bordering on the resonance point considering k-dB deviation from the maximum impedance. An optimized solution to k could be obtained by minimizing the root mean squared error between the measured and the estimated impedance values. The model used to provide the estimations is a parallel RLC circuit valid at resonance frequency which will be transferred to the real model considering the mentioned interval of frequencies. A straightforward algorithm is suggested and programmed using MATLAB which does not require a wide knowledge of design parameters and could be implemented using a spectrum analyzer. The inputs are the measured impedance samples as a function of frequency along with the diameter of the conductors. The suggested algorithm practically provides the estimated parameters of a real inductance model at different frequencies, with or without design information. The suggested work is different from designing a high frequency inductor; it is rather concentration of determining the parameters of an available real inductor that could be easily done by a recipe provided to a technician.