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Start date: 2021Subject: search.filters.subject.Accuracy

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    Integration of discriminant analysis with Artificial neural networks to decision analytic framework for enhancing automated visual IC inspection accuracy
    (2026-05-15)
    Kiatcharoenpol, Tossapol
    ;
    Klongboonjit, Sakon
    This study aims to enhance the accuracy and reliability of automated visual inspection (AVI) in semiconductor manufacturing by integrating Linear Discriminant Analysis (LDA) and an Optimization Layer by Layer Neural Network (OLLNN). Unlike prior LDA-ANN hybrid approaches that primarily emphasize classification accuracy, this study formalizes a decision-analytic inspection loop that explicitly links discriminant centroids, feasible lighting regions, surrogate nonlinear modeling, and production level validation. A two-stage decision analytic framework is developed. In the first stage, LDA is applied to classify and identify discriminant boundaries and centroids between acceptable and defective image features under three lighting setups: coaxial ring, high ring, and low ring lights. In the second stage, OLLNN is trained using these features to capture nonlinear dependencies between greyscale intensity and lighting parameters, and then a surface response plot is used to ease the optimal parameter selection. The integrating model is validated using experimental IC marking inspection data to evaluate improvements in accuracy, especially false positive rates (Type I error). It was found that for the validation state, the false positive rates are reduced from 5.8% to below 4.6%, and classification accuracy improves significantly across variable illumination conditions. After implementation in mass production, the yield is increased to 99.6% with zero false positive found. This significant development of the integrating model enhances a foundation for adaptive, data-driven control of AVI parameters in smart factory environments that support real-time learning and improvement.
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    Low-Cost Technique for Extracting the Cole-Cole Model Parameters
    (2024-01-01)
    Chimnoy, Jirat
    ;
    Karawanich, Khunanon
    ;
    Prommee, Pipat
    This paper proposed a new efficient approach for extracting the parameters of the Cole-Cole impedance model. The main offered advantage is simple and low-cost but accurate in identifying the constant phase element from the bioimpedance models. The Cole-Cole model is simplified as a low-pass filter (LPF) by using a few electronic components. The magnitude and phase response of LPF characteristics are applied to calculate the fractional order (α) and value of the capacitor. This technique offers low complexity and low-cost equipment. The circuit can be realized by a low-component count based on a single OPAMP and four resistors. PSpice simulation with a Cole model is carried out to verify the method. The simulation results are in good agreement with the theory expectations.
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    Machine Learning Based Decision Support System for High-School Study
    (2021-01-01)
    Chaiwuttisak, Pornpimol
    The objectives of this study are to investigate the correlations between personal factors, learning factors family, and economic factors affecting high-school study program selection and also to create and compare models of high-school study program selection with data mining techniques and to develop a decision support system for high-school study program selection with a data mining technique. Data were analyzed by five data mining techniques, and models of high-school study program selection were constructed. These models were then used to construct a decision support system from data mining software called RapidMiner Studio 9. The research findings were as follows personal factors, learning factors family, and economic factors affecting high-school study program selection, and from the result of high-school study program selection, the Decision Tree method, C4.5 algorithm provided the highest accuracy. Therefore, the researcher selected the forecasting model with the Decision Tree method, C4.5 algorithm together with the selection of features with the backward elimination method to create a decision support system.