• Journal of Korean Institute of Industrial Engineers
• /
• v.40 no.4
• /
• pp.404-414
• /
• 2014

Yield prediction is important to manage semiconductor quality. Many researches with machine learning algorithms such as SVM (support vector machine) are conducted to predict yield precisely. However, yield prediction using SVM is hard because extremely imbalanced and big data are generated by final test procedure in semiconductor manufacturing process. Using SVM algorithm with imbalanced data sometimes cause unnecessary support vectors from major class because of unselected support vectors from minor class. So, decision boundary at target class can be overwhelmed by effect of observations in major class. For this reason, we propose a under-sampling method with minor class based SVM (MCSVM) which overcomes the limitations of ordinary SVM algorithm. MCSVM constructs the model that fixes some of data from minor class as support vectors, and they can be good samples representing the nature of target class. Several experimental studies with using the data sets from UCI and real manufacturing process represent that our proposed method performs better than existing sampling methods.

• Journal of the Korean Society for Precision Engineering
• /
• v.16 no.11
• /
• pp.98-105
• /
• 1999

As the IC(Integrated Circuit) has been densified and complicated, it is required to thorough process control to improve yield. Experts, for this purpose, focused on the process analysis automation, which is came from the strict data management in semiconductor manufacturing. In this paper, we presents the process analysis system that can analyze causes, for a output after processes. Also, the plasma etching process that highly affects yield among semiconductor process is modeled to predict a output before the process. To approach this problem, we use adaptively trained neural networks that exhibit superior accuracy over statistical techniques. And in comparison with methods in other paper, a method that history of trend for input data is considered is shown to offer advantage in both learning and prediction capability. This research regards CD(Critical Dimension) that is considerable in high integrated circuit as output variable of the prediction model.

• Journal of the Semiconductor & Display Technology
• /
• v.22 no.4
• /
• pp.131-135
• /
• 2023

Neural network-based time series models called time series neural networks (TSNNs) are trained by the error backpropagation algorithm and used to predict process shifts of parameters such as gas flow, RF power, and chamber pressure in reactive ion etching (RIE). The training data consists of process conditions, as well as principal components (PCs) of optical emission spectroscopy (OES) data collected in-situ. Data are generated during the etching of benzocyclobutene (BCB) in a SF₆/O₂ plasma. Combinations of baseline and faulty responses for each process parameter are simulated, and a moving average of TSNN predictions successfully identifies process shifts in the recipe parameters for various degrees of faults.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2006.05a
• /
• pp.335-336
• /
• 2006

Compound semiconductor manufacturing environment also has been emerged as mass customization and open foundry service so integrated manufacturing system is needed. In this study, we design data acquisition system of compound semiconductor fabrication that has monitoring and control of process. The developed DAS is consisted of key-in system inputted by operator and automatic acquisition system by GEM protocol. And we implemented them in the actual compound semiconductor. It is expected that using developed system would offer precise process information to buyer, reduce a lead-time, and obey a due-dates and so on.

• Journal of Korean Society for Quality Management
• /
• v.47 no.4
• /
• pp.725-737
• /
• 2019

Purpose: We used a data analysis method to improve semiconductor manufacturing yield. We defined and optimized important factors and applied our findings to a real-world process. The semiconductor industry is very cost-competitive; our findings are useful. Methods: We collected data on 15 independent variables and one dependent variable (yield); we removed outliers and missing values. Using SPSS Modeler ver. 18.0, we analyzed the data both continuously and discretely and identified common factors. Results: We optimized two independent variables in terms of process conditions; yield improved. We used DS Leak software to model netting and Contact CD software to model meshes. DS Leak shows smaller the better characterisrics and Contact CD shows normal the best characteristics Conclusion: Various efforts have been made to improve semiconductor manufacturing yields, and many studies have created models or analyzed various characteristics. We not only defined important factors but also showed how to control processing to improve semiconductor yield.

• The Korean Journal of Applied Statistics
• /
• v.34 no.1
• /
• pp.99-114
• /
• 2021

With the Fourth Industrial Revolution based on new technology, the semiconductor manufacturing industry researches various analysis methods such as detecting process abnormalities and predicting yield based on equipment sensor data generated in the manufacturing process. The semiconductor manufacturing process consists of hundreds of processes and thousands of measurement processes associated with them, each of which has properties that cannot be defined by chemical or physical equations. In the individual measurement process, the actual measurement ratio does not exceed 0.1% to 5% of the target product, and it cannot be kept constant for each measurement point. For this reason, efforts are being made to determine whether to manage by using equipment sensor data that can indirectly determine the normal state of each step of the process. In this study, the Functional Data Analysis (FDA) was proposed to define a process abnormality detection process based on equipment sensor data and compensate for the disadvantages of the currently applied statistics-based diagnosis method. Anomaly detection accuracy was compared using machine learning on actual field case data, and its effectiveness was verified.

• IE interfaces
• /
• v.21 no.2
• /
• pp.141-150
• /
• 2008

In this paper, an efficient control method for semiconductor fabrication process is presented. Generally, control is performed with data which is under the influence of process disturbance. EWMA is one of the most popular control methods in semiconductor fabrication that effectively deals with varying process condition. A new method using EWMA, called the Dual Damping EWMA, is presented in this study to reduce over-control by separating weight factor of input and output. The goal is to reflect Drift but reduce the effects of White noise in run to run control. Simulation is performed to evaluate the performance of DPEWMA and to compare with EWMA and Double EWMA.

• Journal of the Semiconductor & Display Technology
• /
• v.10 no.1
• /
• pp.101-105
• /
• 2011

In case of high-tech process industries such as semiconductor and TFT-LCD manufactures, fault of a virtually finished product that is value-added one, since it has gone throughout the most of processes, may give rise to quality cost nearly amount to its selling price and can be a main cause that decreases the efficiency of manufacturing process. This paper proposes a real-time dispatching algorithm for semiconductor manufacturing process with rework. In order to evaluate the proposed algorithm, this paper examines the performance of the proposed method by comparing it with that of the existing dispatching algorithms, based on various experimental data.

• Journal of the Semiconductor & Display Technology
• /
• v.21 no.4
• /
• pp.65-70
• /
• 2022

In manufacturing and semiconductor industries, transfer robots increase productivity through accurate and continuous work. Due to the nature of the semiconductor process, there are environments where humans cannot intervene to maintain internal temperature and humidity in a clean room. So, transport robots take responsibility over humans. In such an environment where the manpower of the process is cutting down, the lack of maintenance and management technology of the machine may adversely affect the production, and that's why it is necessary to develop a technology for the machine failure diagnosis system. Therefore, this paper tries to identify various causes of failure of transport robots that are widely used in semiconductor automation, and the Prognostics and Health Management (PHM) method is considered for determining and predicting the process of failures. The robot mainly fails in the driving unit due to long-term repetitive motion, and the core components of the driving unit are motors and gear reducer. A simulation drive unit was manufactured and tested around this component and then applied to 6-axis vertical multi-joint robots used in actual industrial sites. Vibration data was collected for each cause of failure of the robot, and then the collected data was processed through signal processing and frequency analysis. The processed data can determine the fault of the robot by utilizing machine learning algorithms such as SVM (Support Vector Machine) and KNN (K-Nearest Neighbor). As a result, the PHM environment was built based on machine learning algorithms using SVM and KNN, confirming that failure prediction was partially possible.

• Journal of Korean Institute of Industrial Engineers
• /
• v.38 no.1
• /
• pp.46-56
• /
• 2012

The purpose of virtual metrology (VM) in semiconductor manufacturing is to predict every wafer's metrological values based on its process equipment data without an actual metrology. In this paper, we propose novelty detection-based reliability estimation models for VM in order to support flexible utilization of VM results. Because the proposed model can not only estimate the reliability of VM, but also identify suspicious process variables lowering the reliability, quality control actions can be taken selectively based on the reliance level and its causes. Based on the preliminary experimental results with actual semiconductor manufacturing process data, our models can successfully give a high reliance level to the wafers with small prediction errors and a low reliance level to the wafers with large prediction errors. In addition, our proposed model can give more detailed information by identifying the critical process variables and their relative impacts on the low reliability.