Integration of discriminant analysis with Artificial neural networks to decision analytic framework for enhancing automated visual IC inspection accuracy

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.